diff --git a/DEFERRED.md b/DEFERRED.md index 2ea98aee8..89a44dd22 100644 --- a/DEFERRED.md +++ b/DEFERRED.md @@ -8,10 +8,9 @@ provenance and AI-review deviation-documentation: a row here (or in - Shippable work lives in [TODO.md](TODO.md); the public feature roadmap in [ROADMAP.md](ROADMAP.md); monitoring and current-state notes in [docs/dev-status.md](docs/dev-status.md). -- Version-gated lifecycle items (deprecated-kwarg removals, v4 default flips such as - `SyntheticDiD` `lambda_reg`/`zeta` and the clustered-df convention) are canonically - tracked in the CI-enforced ledger `docs/v4-deprecations.yaml`; rows here cross-link - their `M-xxx` ids where applicable. +- Version-gated lifecycle items (deprecated-kwarg removals and default flips) are + canonically tracked in the CI-enforced ledger `docs/v4-deprecations.yaml`; rows here + cross-link their `M-xxx` ids where applicable. - The `PR` column points to the originating PR number or review tag. --- @@ -130,7 +129,8 @@ here carry the remaining implementation work and cross-link their `M-xxx` ids. | Issue | Location | PR | Priority | |-------|----------|----|----------| -| Clustered inference df **default flip at v4** (lifecycle + roster: docs/v4-deprecations.yaml M-004..M-006 + M-128..M-131; knob semantics: v4-design §7). Implementation work the ledger rows do not carry: the migration note, and flipping the test expectations that pin the current default (`TestDfConvention`, `test_moderate_t_pins_residual_df_convention`, the per-estimator knob suites). | `diff_diff/linalg.py::LinearRegression`, `diff_diff/estimators.py`, `diff_diff/twfe.py`, `diff_diff/sun_abraham.py`, `diff_diff/wooldridge.py`, `diff_diff/stacked_did.py`, `diff_diff/imputation.py` | — | Medium | + +No open rows: the clustered-df default flip shipped with the 4.0 cut. ## Decision record — won't-fix / waived diff --git a/TODO.md b/TODO.md index edd9e74da..39ffc6ab8 100644 --- a/TODO.md +++ b/TODO.md @@ -85,6 +85,7 @@ generic sparse-FE, QR+SVD rank-detection redundancy, `check_finite` bypass — m | Issue | Location | Origin | Effort | Priority | |-------|----------|--------|--------|----------| +| Re-execute the nine tutorials whose committed outputs still show the 3.x residual-df p-values/CIs after the `df_convention` default moved to `"cluster"` (t(G-1)): `01_basic_did`, `02_staggered_did`, `04_parallel_trends`, `05_honest_did`, `07_pretrends_power`, `09_real_world_examples`, `11_imputation_did`, `13_stacked_did`, `16_wooldridge_etwfe` (not `16_survey_did`: the survey df wins). Run under `DIFF_DIFF_BACKEND=rust` with the per-worktree kernel, reconcile any prose that restates a moved number, and `--nbmake` each; scheduled for the 4.0 release PR (`docs/v4-design.md` section 9 item 10) together with any item-7b/8/9 movers (24 was refreshed in #879; 23 belongs to 7b). | `docs/tutorials/*.ipynb` | #879 | Mid | Medium | | TWFE event-study one-way HC2-BM absorbed-FE R parity against the `mpd_absorbed_fe_did` golden (`clubSandwich::vcovCR(cluster=1:n, type="CR2")`): the within spec with hc2_bm auto-clusters at unit, so the one-way lane needs an explicit singleton-cluster column (`cluster=` + hc2_bm = CR2 with G=n, PT2018 §3.3) once the G=n CR2 path is verified; the hc2 lane is pinned by `TestTWFEEventStudyAbsorbedFERParity` and the clustered hc2_bm per-period lane by `test_twfe_event_study_cluster_hc2_bm_per_period_df_matches_clubsandwich`; the MultiPeriodDiD lanes died at the 4.0 cut (PR 4c-ii) | `tests/test_estimators_vcov_type.py` | #v4-4c-i | Mid | Medium | | TWFE event-study power profile: `simulate_power`/`simulate_mde`/`simulate_sample_size` have no `_EstimatorProfile` for `TwoWayFixedEffects.fit(event_study=True)` since the MultiPeriodDiD registry entry went with the class (PR 4c-ii); `EventStudyResults` carries no scalar headline, so the profile needs a design (post-period average via the `tests/helpers/event_study_average.py` arithmetic, or a named period) plus its `_SURVEY_FIT_BUILDERS` twin | `diff_diff/power.py` | #v4-4c-ii | Mid | Low | | Clustered hc2_bm Bell-McCaffrey DOF guard is batch-relative (`linalg._cr2_bm_dof_inner`: a contrast is NaN'd when its scaled max\|B\| falls below 1e-10 of the batch maximum), so the event-study core keeps the otherwise-unused post-period-average contrast column as the anchor to hold the 3.x per-period NaN masks bit-identical (PR 4c-ii, user decision 2026-10-25). Reproduction: `x_nc = treated * 1[period >= 4] + 3e-6 * default_rng(seed).normal(size=n)` as a covariate on the oracle panel flips the mask between `[eye(k), avg]` and `eye(k)` on 20/20 seeds (cond(X) ~ 9e5; 1e-7 never flips, <= 3e-8 rank-drops) - too BLAS-sensitive for a committed oracle, hence the structural pin `test_hc2_bm_contrast_batch_keeps_post_average_anchor`. Decide whether the guard should use a batch-independent anchor (the treatment contrast, or the absolute floor only) and then drop the column | `diff_diff/estimators.py`, `diff_diff/linalg.py` | #v4-4c-ii | Mid | Medium | diff --git a/changelog.d/20261028-v4-pr7a-df-convention-flip.md b/changelog.d/20261028-v4-pr7a-df-convention-flip.md new file mode 100644 index 000000000..f639797b5 --- /dev/null +++ b/changelog.d/20261028-v4-pr7a-df-convention-flip.md @@ -0,0 +1,7 @@ +### Breaking Changes + +- **4.0 cut, `df_convention` default flip (`M-004`, `M-005`, `M-006`, `M-128`, `M-129`, `M-130`, `M-131`).** The analytical tail-df convention for CLUSTERED fits defaults to `"cluster"` (the Stata/fixest `t(G - 1)` reference) instead of `"residual"` (the fitted residual df `t(n - K_full)`) on `DifferenceInDifferences`, `TwoWayFixedEffects`, `LinearRegression`, `SunAbraham`, `WooldridgeDiD` (OLS arms), `StackedDiD` and `ImputationDiD` (pretrends leads); `LPDiD` already defaulted to `"cluster"` at 3.9. What moves: p-values and confidence intervals on clustered analytical fits - `TwoWayFixedEffects` (static, and event-study with `unit=`), `SunAbraham`, `WooldridgeDiD` OLS and `StackedDiD` cluster at `unit` by default, so their default fits move; `ImputationDiD` moves on the `pretrends=True` lead rows; `DifferenceInDifferences` / `LinearRegression` move only with an explicit `cluster=` / `cluster_ids=`; the analytical lanes inside bootstrapped fits (`WooldridgeDiD` per-cell inference and post-fit `aggregate()`, `ImputationDiD` bootstrapped leads) move too, as do the fake-timing / fake-group placebo diagnostics and the power simulators that relay those p-values. What does not: point estimates, standard errors and t-statistics; survey-design fits (survey df keeps precedence); `vcov_type="hc2_bm"` (Bell-McCaffrey DOF keep precedence); bootstrap percentile p-values / CIs; unclustered and Conley fits; the BJS overall/post inference of `ImputationDiD`; `WooldridgeDiD` logit/poisson arms; permutation and leave-one-out diagnostics. `df_convention="residual"` restores the 3.x inference bit-for-bit; on the surfaces where the knob is a documented no-op (non-`pretrends` `ImputationDiD`, `WooldridgeDiD` logit/poisson) the inert-config `UserWarning` now fires for an explicit `"residual"` rather than for an explicit `"cluster"`. `SyntheticDiD` inherits the flipped attribute from `DifferenceInDifferences` without reading it (no behaviour change). + +### Internal + +- Ledger rows `M-004`..`M-006` and `M-128`..`M-131` flip to `done` with `tests/test_v4_inference_defaults.py` as their `test_ref`; the spec's section 7 / item 7 SHIPPED notes and item 10 now record the deferred bulk re-execution of the nine tutorials whose clustered-default outputs moved (24 re-executed here if its outputs moved); the REGISTRY TwoWayFixedEffects clustered-CR1 inference-df note is relabelled a plain Note (the default now matches fixest) and the SunAbraham / ImputationDiD / StackedDiD / WooldridgeDiD / LPDiD tail-df notes read the 4.0 default; `TestDfConvention`, `test_moderate_t_pins_cluster_df_convention` and the per-estimator knob suites pin the G-1 default against explicit `"residual"` baselines (every default-vs-`"cluster"` comparison became default-vs-`"residual"` so none passes vacuously); `test_ci_matches_r_twfe` tightens to machine precision; the variance-conventions audit matrix keeps its residual-df rows as explicit `"residual"` arms (the K_full accounting pins) and gains eight G-1 `_default_df` twins (three in the fast set), with `docs/methodology/variance-conventions.md` regenerated; the committed TWFE event-study oracle lanes `pooled_cluster_unit` / `within_cluster_unit` and the ImputationDiD `pretrends_all` contract lanes name the `"residual"` convention they were captured under (metadata-only, no oracle number touched); the `DEFERRED.md` Version-gated (v4) row is retired. diff --git a/diff_diff/estimators.py b/diff_diff/estimators.py index 8fc23cb01..4f7ff4e0f 100644 --- a/diff_diff/estimators.py +++ b/diff_diff/estimators.py @@ -158,22 +158,24 @@ class DifferenceInDifferences(BaseEstimator): path — absent ``cluster=``, pure Conley spatial HAC applies. ``survey_design=`` + Conley and ``inference='wild_bootstrap'`` + Conley both raise ``NotImplementedError``. - df_convention : {"residual", "cluster", "normal"}, default "residual" + df_convention : {"residual", "cluster", "normal"}, default "cluster" Degrees-of-freedom convention for analytical t-statistics, p-values, - and CIs. ``"residual"`` (default) uses the fitted residual df - (``n − K_full``); ``"cluster"`` uses the Stata/fixest cluster df - ``G − 1`` on clustered fits — it has no effect on unclustered fits + and CIs. ``"cluster"`` (default since 4.0) uses the Stata/fixest + cluster df ``G − 1`` on clustered fits — it has no effect on unclustered fits or on ``vcov_type="conley"`` (the combined Conley+cluster product kernel has no documented ``G − 1`` df reference and keeps the - residual df); ``"normal"`` deliberately uses normal-theory z - inference at the fallback level on every fit, clustered or not. + residual df); ``"residual"`` (the 3.x default) uses the fitted + residual df (``n − K_full``); ``"normal"`` deliberately uses + normal-theory z inference at the fallback level on every fit, + clustered or not. Applies only at the fallback level of the df resolution under every value: survey df and per-coefficient Bell-McCaffrey DOF (``vcov_type="hc2_bm"``) are more refined small-sample corrections and always take precedence. Point estimates, SEs, and t-statistics are unaffected — only the reference distribution changes. The - default flips to ``"cluster"`` at v4 (see the REGISTRY clustered-CR1 - inference-df deviation note). + default flipped from ``"residual"`` to ``"cluster"`` at 4.0 (rows + M-004/M-005; pass ``df_convention="residual"`` to reproduce 3.x + inference; see the REGISTRY clustered-CR1 inference-df note). Attributes ---------- @@ -238,7 +240,7 @@ def __init__( conley_metric: str = "haversine", conley_kernel: str = "bartlett", conley_lag_cutoff: Optional[int] = None, - df_convention: str = "residual", + df_convention: str = "cluster", ): # Resolve/validate vcov_type via the shared helper so __init__ and # set_params use identical validation logic. @@ -282,12 +284,11 @@ def __init__( # arrays are auto-derived from data[time].values + data[unit].values # at fit-time (panel estimators already take time/unit as column names). self.conley_lag_cutoff = conley_lag_cutoff - # Inference df convention for clustered analytical fits: "residual" - # (default; t/p/CI at the fitted residual df) or "cluster" (the - # Stata/fixest G-1 convention). Survey df and per-coefficient - # Bell-McCaffrey DOF always take precedence over either. The default - # flips to "cluster" at v4 (REGISTRY clustered-CR1 inference-df - # deviation note). + # Inference df convention for clustered analytical fits: "cluster" + # (default since 4.0; the Stata/fixest G-1 convention) or + # "residual" (the 3.x default; t/p/CI at the fitted residual df). + # Survey df and per-coefficient Bell-McCaffrey DOF always take + # precedence over either (REGISTRY clustered-CR1 inference-df note). self.df_convention = df_convention self.is_fitted_ = False @@ -2009,7 +2010,8 @@ def _refit_mp_absorb(w_r): n_eff_df = int(round(np.sum(survey_weights))) df = n_eff_df - k_effective - n_absorbed_effects _df_cluster_knob_invalid = False - # Opt-in Stata/fixest cluster-df convention (df_convention="cluster"): + # Stata/fixest cluster-df convention (df_convention="cluster", the 4.0 + # default): # the shared analytical df becomes G - 1 on a clustered fit. Placed # BEFORE the survey/replicate overrides below (which overwrite df, so # survey df keeps precedence) and upstream of the per-period BM-DOF diff --git a/diff_diff/guides/llms-full.txt b/diff_diff/guides/llms-full.txt index 9b85c24c6..f27de7bec 100644 --- a/diff_diff/guides/llms-full.txt +++ b/diff_diff/guides/llms-full.txt @@ -60,7 +60,7 @@ DifferenceInDifferences( bootstrap_weights: str = "rademacher", # "rademacher", "webb", or "mammen" seed: int | None = None, # Random seed rank_deficient_action: str = "warn", # "warn", "error", or "silent" - df_convention: str = "residual", # Analytical t/p/CI df: "residual" (n-K, default), "cluster" (Stata/fixest G-1, clustered fits), or "normal" (deliberate z); survey df + hc2_bm BM-DOF keep precedence; default flips at v4 + df_convention: str = "cluster", # Analytical t/p/CI df: "cluster" (Stata/fixest G-1 on clustered fits; default since 4.0), "residual" (n-K; the 3.x default), or "normal" (deliberate z); survey df + hc2_bm BM-DOF keep precedence ) ``` @@ -108,7 +108,7 @@ Two-Way Fixed Effects estimator for panel data. Inherits from DifferenceInDiffer TwoWayFixedEffects( cluster: str | None = None, # Auto-clusters at unit level if None alpha: float = 0.05, - df_convention: str = "residual", # Analytical t/p/CI df: "residual" (default), "cluster" (G-1), or "normal" (z); flips at v4 + df_convention: str = "cluster", # Analytical t/p/CI df: "cluster" (G-1; default since 4.0), "residual" (n-K; 3.x default), or "normal" (z) ) ``` @@ -376,7 +376,7 @@ SunAbraham( conley_metric: str = "haversine", # "haversine" | "euclidean" conley_kernel: str = "bartlett", # "bartlett" | "uniform" conley_lag_cutoff: int | None = None, # within-unit Bartlett max lag (0 = spatial-only) - df_convention: str = "residual", # Analytical t/p/CI df for cells AND aggregates (3.9: aggregates share the cells' df — previously z): "residual" (default), "cluster" (G-1), "normal" (z); survey/BM DOF keep precedence; flips at v4 + df_convention: str = "cluster", # Analytical t/p/CI df for cells AND aggregates (3.9: aggregates share the cells' df — previously z): "cluster" (G-1; default since 4.0), "residual" (3.x default), "normal" (z); survey/BM DOF keep precedence ) ``` @@ -424,7 +424,7 @@ ImputationDiD( aux_partition: str = "cohort_horizon", # "cohort_horizon", "cohort", or "horizon" pretrends: bool = False, # Include pre-treatment horizons in event study leave_one_out: bool = False, # BJS 2024 App. A.9 leave-one-out finite-sample variance (larger, less-downward-biased SE) - df_convention: str = "residual", # Pretrends lead-regression per-lead t/p/CI only (3.9: leads moved z -> t(residual)): "residual" (default), "cluster" (G-1), "normal" (z); BJS overall/post inference + Wald F knob-independent; flips at v4 + df_convention: str = "cluster", # Pretrends lead-regression per-lead t/p/CI only (3.9: leads moved z -> t(residual); 4.0: default G-1): "cluster" (default since 4.0), "residual" (3.x default), "normal" (z); BJS overall/post inference + Wald F knob-independent ) ``` @@ -1104,7 +1104,7 @@ StackedDiD( rank_deficient_action: str = "warn", vcov_type: str = "hc1", # {"hc1","hc2_bm"}; classical/hc2 rejected (intrinsically clustered), conley deferred. survey_design=... requires hc1 balance: str = "none", # {"none","entropy"}; "entropy" = CBWSDID covariate balancing (Ustyuzhanin 2026), requires fit(covariates=[...]) + weighting="aggregate", no survey_design - df_convention: str = "residual", # Analytical t/p/CI df (3.9: hc1 lane moved z -> t(pooled residual df)): "residual" (default), "cluster" (G-1, positive-weight clusters), "normal" (z); BM/survey df keep precedence; flips at v4 + df_convention: str = "cluster", # Analytical t/p/CI df (3.9: hc1 lane moved z -> t(pooled residual df); 4.0: default G-1): "cluster" (G-1, positive-weight clusters; default since 4.0), "residual" (3.x default), "normal" (z); BM/survey df keep precedence ) ``` @@ -1613,7 +1613,7 @@ WooldridgeDiD( # column is dropped per Sec 5.4, so cohort_trend_coefs # carries G-1 entries. Rejected with survey_design= and # with control_group="never_treated". - df_convention: str = "residual", # OLS analytical t/p/CI df (3.9: default-hc1 arms moved z -> t(residual)): "residual" (default), "cluster" (G-1, hc1-clustered only), "normal" (z); survey/BM DOF keep precedence; GLM arms knob-independent (explicit non-default warns); flips at v4 + df_convention: str = "cluster", # OLS analytical t/p/CI df (3.9: hc1 arms moved z -> t(residual); 4.0: default G-1): "cluster" (G-1, hc1-clustered only; default since 4.0), "residual" (3.x default), "normal" (z); survey/BM DOF keep precedence; GLM arms knob-independent (explicit non-default warns) unsupported_period_action: str = "drop", # "drop" warns and filters; "error" refuses unsupported periods ) ``` @@ -1834,7 +1834,7 @@ Returned by `SunAbraham.fit()`. | `cohort_effects` | `dict` | Cohort-level effects | | `df_convention` | `str | None` | The configured df convention echoed onto the results ("residual" | "cluster" | "normal"; 3.9) | | `inference_df` | `float | None` | The df the overall-ATT p/CI actually used (BM contrast df / survey df / knob-resolved fallback; None under bootstrap or "normal") | -| `event_study_df` | `dict[int, float] | None` | Per-event-time df provenance (finite residual df on plain analytic fits since 3.9; None under bootstrap) | +| `event_study_df` | `dict[int, float] | None` | Per-event-time df provenance (G-1 on plain clustered analytic fits under the 4.0 default; the finite residual df under `df_convention="residual"`; None under bootstrap) | **Methods:** `summary()`, `print_summary()`, `to_dataframe(level="event_study"|"cohort")` diff --git a/diff_diff/imputation.py b/diff_diff/imputation.py index 07d4a42f3..6a0cb435b 100644 --- a/diff_diff/imputation.py +++ b/diff_diff/imputation.py @@ -148,21 +148,22 @@ class ImputationDiD(ImputationDiDBootstrapMixin, _ImputationAggregationMixin, Ba but are a library extension beyond the paper's derivation. Replicate-weight survey designs raise ``NotImplementedError`` (their variance bypasses the influence-function path where the rescale lives). - df_convention : {"residual", "cluster", "normal"}, default "residual" + df_convention : {"residual", "cluster", "normal"}, default "cluster" Degrees-of-freedom convention for the PRETRENDS lead regression's per-lead t/p/CI — the one ImputationDiD surface running the shared clustered CR1 sandwich (``pretrends=True``, surfaced via - ``results.aggregate('event_study')``). ``"residual"`` (default) uses - the lead regression's residual df (``n − k_kept − absorbed - [time, unit] rank``) — the 3.9 fix: previously silent normal-theory - z on plain clustered fits; ``"cluster"`` uses ``G − 1``; - ``"normal"`` deliberately uses z. The full-design survey df keeps + ``results.aggregate('event_study')``). ``"cluster"`` (default since + 4.0) uses ``G − 1``; ``"residual"`` (the 3.x default) uses the lead + regression's residual df (``n − k_kept − absorbed [time, unit] + rank``) — the 3.9 fix: previously silent normal-theory z on plain + clustered fits; ``"normal"`` deliberately uses z. The full-design survey df keeps precedence on survey fits. Everything else — the BJS Theorem-3 overall/event-study inference and the joint pretrend Wald F (which keeps its cluster-robust ``F(q, G − 1)`` reference) — is knob-independent; an explicitly non-default value on a configuration that never surfaces the per-lead inference warns at - fit time. The default flips to ``"cluster"`` at v4. + fit time. The default flipped to ``"cluster"`` at 4.0 (row M-131); + pass ``df_convention="residual"`` for 3.x per-lead inference. Attributes ---------- @@ -219,7 +220,7 @@ def __init__( aux_partition: str = "cohort_horizon", pretrends: bool = False, leave_one_out: bool = False, - df_convention: str = "residual", + df_convention: str = "cluster", ): if rank_deficient_action not in ("warn", "error", "silent"): raise ValueError( @@ -345,7 +346,7 @@ def fit( survey_design is not None and survey_design.replicate_method is not None ) _lead_inference_reachable = self.pretrends and not _is_replicate_design - if self.df_convention != "residual" and not _lead_inference_reachable: + if self.df_convention != "cluster" and not _lead_inference_reachable: warnings.warn( f"df_convention={self.df_convention!r} affects only the " "pretrends event-study per-lead inference (pretrends=True, " diff --git a/diff_diff/linalg.py b/diff_diff/linalg.py index 107ca1d51..70c291c5e 100644 --- a/diff_diff/linalg.py +++ b/diff_diff/linalg.py @@ -4419,18 +4419,19 @@ class LinearRegression: neither is formally PSD-guaranteed in the radial pairwise form (Conley 1999's explicit PSD Bartlett formula is the 2-D separable product window, Eq 3.14, not the 1-D radial pairwise form). - df_convention : {"residual", "cluster", "normal"}, default "residual" + df_convention : {"residual", "cluster", "normal"}, default "cluster" Degrees-of-freedom convention for ``get_inference`` t/p/CI. - ``"residual"`` uses the fitted residual df; ``"cluster"`` uses the - Stata/fixest cluster df ``G − 1`` (from ``n_clusters_``) — + ``"cluster"`` (default since 4.0) uses the Stata/fixest cluster df + ``G − 1`` (from ``n_clusters_``); ``"residual"`` (the 3.x default) + uses the fitted residual df — ``"cluster"`` has no effect on unclustered fits or ``vcov_type="conley"`` (no documented ``G − 1`` reference for the Conley+cluster product kernel); ``"normal"`` deliberately uses normal-theory z inference at the fallback level on every fit, clustered or not. Fallback-level only under every value: survey df and per-coefficient Bell-McCaffrey DOF always take precedence. No - effect on coefficients, SEs, or t-statistics. Default flips to - ``"cluster"`` at v4. + effect on coefficients, SEs, or t-statistics. The default flipped + to ``"cluster"`` at 4.0 (row M-006). Attributes ---------- @@ -4500,7 +4501,7 @@ def __init__( conley_time: Optional[np.ndarray] = None, conley_unit: Optional[np.ndarray] = None, conley_lag_cutoff: Optional[int] = None, - df_convention: str = "residual", + df_convention: str = "cluster", ): # Inline validation (not utils.validate_df_convention): diff_diff.utils # imports diff_diff.linalg at module level, so importing back would be @@ -4528,13 +4529,13 @@ def __init__( self.conley_unit = conley_unit self.conley_lag_cutoff = conley_lag_cutoff # Inference df convention for clustered analytical fits: - # "residual" (default) keeps the fitted residual df `n - K_full` for - # t/p/CI; "cluster" uses the Stata/fixest cluster df `G - 1` instead. + # "cluster" (default since 4.0) uses the Stata/fixest cluster df + # `G - 1` for t/p/CI; "residual" (the 3.x default) keeps the fitted + # residual df `n - K_full` instead. # Applies ONLY at the fallback level of the `get_inference` df # resolution: survey df and per-coefficient Bell-McCaffrey DOF (which - # are more refined small-sample corrections) always take precedence. - # The default flips to "cluster" at v4 (see REGISTRY clustered-CR1 - # inference-df deviation note). + # are more refined small-sample corrections) always take precedence + # (see the REGISTRY clustered-CR1 inference-df note). self.df_convention = df_convention # Resolve/validate vcov_type via the shared helper. self.vcov_type = resolve_vcov_type(vcov_type) @@ -5149,7 +5150,9 @@ def get_inference( alpha : float, optional Significance level for CI. Defaults to instance-level alpha. df : int, optional - Degrees of freedom. Defaults to fitted df (n - k - df_adjustment). + Degrees of freedom. Defaults to the ``df_convention``-resolved + df: ``G - 1`` on clustered fits under the default ``"cluster"``, + the fitted residual df (n - k - df_adjustment) otherwise. Set to None explicitly to use normal distribution instead of t. Returns @@ -5220,15 +5223,15 @@ def get_inference( and self.n_clusters_ is not None and self.vcov_type != "conley" ): - # Opt-in Stata/fixest cluster-df convention for clustered - # analytical fits: t/p/CI at df = G - 1 instead of the residual - # df. conley is excluded: the combined Conley+cluster product + # Stata/fixest cluster-df convention (the 4.0 default) for + # clustered analytical fits: t/p/CI at df = G - 1 instead of the + # residual df. conley is excluded: the combined Conley+cluster product # kernel is a diff-diff convention with no documented G-1 df # reference (REGISTRY Conley section); it keeps the residual df. # Deliberately the LAST branch before the residual fallback: # survey df and per-coefficient Bell-McCaffrey DOF are more - # refined small-sample corrections and always win. Default flips - # at v4 (REGISTRY clustered-CR1 inference-df deviation note). + # refined small-sample corrections and always win (REGISTRY + # clustered-CR1 inference-df note). if self.n_clusters_ <= 1: # Cluster df G - 1 is undefined for an effectively # one-cluster fit (e.g. weighted fit where only one cluster @@ -5310,7 +5313,9 @@ def get_inference_batch( alpha : float, optional Significance level for CIs. Defaults to instance-level alpha. df : int, optional - Degrees of freedom. Defaults to fitted df. + Degrees of freedom. Defaults to the ``df_convention``-resolved df + (``G - 1`` on clustered fits under the default ``"cluster"``, the + fitted residual df otherwise). Returns ------- @@ -5340,7 +5345,9 @@ def get_all_inference( alpha : float, optional Significance level for CIs. Defaults to instance-level alpha. df : int, optional - Degrees of freedom. Defaults to fitted df. + Degrees of freedom. Defaults to the ``df_convention``-resolved df + (``G - 1`` on clustered fits under the default ``"cluster"``, the + fitted residual df otherwise). Returns ------- diff --git a/diff_diff/lpdid.py b/diff_diff/lpdid.py index f6b232d8f..c09ea372c 100644 --- a/diff_diff/lpdid.py +++ b/diff_diff/lpdid.py @@ -95,8 +95,8 @@ def _validate_params(self) -> None: "(the estimand becomes ambiguous); use control_group='clean' (the default)" ) # LPDiD's DEFAULT is "cluster" — its t(G-1) reference is the Stata - # lpdid convention and already the library-wide 4.0 target, so the - # default moves nothing (unlike the "residual"-default estimators). + # lpdid convention; since the 4.0 flip it is the library-wide default + # too (LPDiD adopted it at 3.9, ahead of the other knob surfaces). validate_df_convention(self.df_convention) def _rhs_column_names(self, covariates=None, ylags=0, dylags=0): diff --git a/diff_diff/stacked_did.py b/diff_diff/stacked_did.py index e6f406ca6..dd82dd120 100644 --- a/diff_diff/stacked_did.py +++ b/diff_diff/stacked_did.py @@ -138,21 +138,22 @@ class StackedDiD(BaseEstimator): raise a ``ValueError``), and does not support ``survey_design=``; matching-based balancing and the repeated-treatment extension are out of scope. Default ``"none"`` reproduces plain weighted stacked DID. - df_convention : {"residual", "cluster", "normal"}, default "residual" + df_convention : {"residual", "cluster", "normal"}, default "cluster" Degrees-of-freedom convention for the analytical t/p/CI on the pooled stacked regression's event-study and overall-ATT inference. - ``"residual"`` (default) uses the pooled residual df - (``n_eff − k_kept``, positive-weight rows) — the 3.9 fix: the + ``"cluster"`` (default since 4.0) uses ``G − 1`` where G counts + positive-weight clusters (``effective_cluster_count`` — may differ + from the raw ``results.n_clusters`` when a cluster's composed weight + is zero); ``"residual"`` (the 3.x default) uses the pooled residual + df (``n_eff − k_kept``, positive-weight rows) — the 3.9 fix: the non-BM non-survey lane previously used silent normal-theory z; - ``"cluster"`` uses ``G − 1`` where G counts positive-weight - clusters (``effective_cluster_count`` — may differ from the raw - ``results.n_clusters`` when a cluster's composed weight is zero); ``"normal"`` deliberately uses normal-theory z at the fallback level. hc2_bm Bell-McCaffrey contrast DOF and survey/replicate df always take precedence. Note the stacked design's residual df is typically large (control rows replicate across sub-experiments), so - the default's numeric movement vs the old z is small — the change - is a convention alignment. The default flips to ``"cluster"`` at v4. + the 3.9 z -> t(residual) step was small and the 4.0 flip to + ``G − 1`` is the materially moving step (row M-130); pass + ``df_convention="residual"`` for 3.x inference. Attributes ---------- @@ -204,7 +205,7 @@ def __init__( rank_deficient_action: str = "warn", vcov_type: str = "hc1", balance: str = "none", - df_convention: str = "residual", + df_convention: str = "cluster", ): if weighting not in ("aggregate", "population", "sample_share"): raise ValueError( @@ -945,7 +946,7 @@ def _refit_stacked(w_r): es_df_used[h] = float("nan") else: # BM DOF > survey/replicate df > the knob-resolved - # analytical fallback (residual t by default; the + # analytical fallback (t(G-1) under the default; the # replicate 0-sentinel is not None, so it keeps # precedence over the fallback). _df_eff = ( diff --git a/diff_diff/stacked_did_results.py b/diff_diff/stacked_did_results.py index 50fa31b83..90bb2721d 100644 --- a/diff_diff/stacked_did_results.py +++ b/diff_diff/stacked_did_results.py @@ -107,8 +107,8 @@ class StackedDiDResults(BaseResults, AggregationMixin): ``safe_inference`` for its stored p-value/CI (per-event Bell-McCaffrey Satterthwaite df under ``hc2_bm``; the scalar survey df under survey designs; the ``df_convention``-resolved analytical - fallback otherwise — finite residual df under the 3.9 default, - ``G − 1`` under "cluster"), or NaN when the row used normal theory + fallback otherwise — ``G − 1`` under the 4.0 default "cluster", + the finite residual df under "residual"), or NaN when the row used normal theory (``df_convention="normal"``), the df was undefined, or hc2_bm failed closed. Always populated on 3.9+ fits (row M-024); None only on pre-3.9 pickles. diff --git a/diff_diff/sun_abraham.py b/diff_diff/sun_abraham.py index 6f8e1fc95..3a982b051 100644 --- a/diff_diff/sun_abraham.py +++ b/diff_diff/sun_abraham.py @@ -194,8 +194,9 @@ class SunAbrahamResults(BaseResults): # p-value/CI's safe_inference actually received (the per-event # Bell-McCaffrey contrast df under hc2_bm; the survey design df - # post-drop under replicate refits - on survey fits; the - # df_convention-resolved fallback on plain analytic fits - FINITE - # residual df under the 3.9 default, G-1 under "cluster", NaN under + # df_convention-resolved fallback on plain analytic fits - G-1 under + # the 4.0 default "cluster", the FINITE residual df under "residual", + # NaN under # "normal" and on rows whose BM DOF was non-finite, where # safe_inference's non-finite-df guard yields all-NaN inference). None # under bootstrap: the stored percentile p/CIs never used a df (note @@ -632,18 +633,20 @@ class SunAbraham(BaseEstimator): ``conley_cutoff_km`` / ``conley_lag_cutoff``); ``survey_design=`` / ``weights`` / ``n_bootstrap>0`` are rejected. See the ``vcov_type`` parameter docs above. - df_convention : {"residual", "cluster", "normal"}, default "residual" + df_convention : {"residual", "cluster", "normal"}, default "cluster" Degrees-of-freedom convention for analytical t/p/CI, applied to BOTH the per-cohort-cell inference and the aggregated event-study / - overall-ATT inference (one df source per fit). ``"residual"`` - (default) uses the saturated regression's residual df — the 3.9 fix: + overall-ATT inference (one df source per fit). ``"cluster"`` + (default since 4.0) uses the Stata/fixest cluster df ``G − 1`` + (inert under ``vcov_type="conley"``); ``"residual"`` (the 3.x + default) uses the saturated regression's residual df — the 3.9 fix: aggregates previously dropped to normal theory on plain clustered - fits; ``"cluster"`` uses the Stata/fixest cluster df ``G − 1`` - (inert under ``vcov_type="conley"``); ``"normal"`` deliberately uses + fits; ``"normal"`` deliberately uses normal-theory z at the fallback level everywhere (cells included). Survey/replicate df and hc2_bm Bell-McCaffrey contrast DOF always take precedence; bootstrap p/CI (``n_bootstrap>0``) are percentile- - based and unaffected. The default flips to ``"cluster"`` at v4. + based and unaffected. The default flipped to ``"cluster"`` at 4.0 + (row M-128); pass ``df_convention="residual"`` for 3.x inference. Attributes ---------- @@ -727,7 +730,7 @@ def __init__( conley_metric: str = "haversine", conley_kernel: str = "bartlett", conley_lag_cutoff: Optional[int] = None, - df_convention: str = "residual", + df_convention: str = "cluster", ): if control_group not in ["never_treated", "not_yet_treated"]: raise ValueError( @@ -1219,8 +1222,8 @@ def _refit_sa(w_r): # Per-row df PROVENANCE (spec section 5, row M-092): the df each # stored ES row's safe_inference actually received, recorded iff # finite and > 0 else NaN. Baseline = the knob-resolved fallback - # every _compute_iw_effects row used (the residual df on plain - # analytic fits under the default; G-1 under "cluster"; NaN under + # every _compute_iw_effects row used (G-1 under the default "cluster"; + # the residual df under "residual"; NaN under # "normal"; the TSL design df on survey fits). Overwritten below at # the hc2_bm and replicate-refit override sites, and cleared under # bootstrap, whose stored percentile p/CIs never used a df. @@ -1343,16 +1346,16 @@ def _refit_sa(w_r): _overall_att_contrast_dof = float(dof_vec[-1]) except (ValueError, np.linalg.LinAlgError) as exc: # Rank-deficient or other linalg issue: fall back to - # the knob-resolved analytical df (residual t by - # default). Emit a UserWarning so the deviation is - # visible. + # the knob-resolved analytical df (t(G-1) under the + # default "cluster"; residual t under "residual"). Emit + # a UserWarning so the deviation is visible. warnings.warn( f"SunAbraham(vcov_type='hc2_bm') aggregated inference " f"could not compute Bell-McCaffrey contrast DOF " f"({type(exc).__name__}: {exc}). Falling back to the " "df_convention-resolved analytical df; aggregated " - "p-values/CIs use t(residual df) under the default " - "instead of t(BM DOF).", + "p-values/CIs use the df_convention-resolved t df " + "(t(G-1) under the default) instead of t(BM DOF).", UserWarning, stacklevel=2, ) @@ -1874,8 +1877,8 @@ def _fit_saturated_regression( # aggregated inference layer to compute per-event-time and # overall-ATT Satterthwaite DOF on user-facing outputs. Under # other vcov_type values aggregated inference falls back to the - # knob-resolved analytical df (residual t by default; G−1 under - # df_convention="cluster"; z under "normal"). + # knob-resolved analytical df (G−1 under the default "cluster"; + # residual t under "residual"; z under "normal"). if vcov_type == "hc2_bm": bread_matrix = X.T @ X bm_artifacts: Optional[Tuple[np.ndarray, Optional[np.ndarray], np.ndarray]] = ( diff --git a/diff_diff/twfe.py b/diff_diff/twfe.py index f546da935..5f6a9382b 100644 --- a/diff_diff/twfe.py +++ b/diff_diff/twfe.py @@ -60,15 +60,17 @@ class TwoWayFixedEffects(DifferenceInDifferences): auto-cluster is also preserved (routes to CR2-BM at unit). alpha : float, default=0.05 Significance level for confidence intervals. - df_convention : {"residual", "cluster", "normal"}, default "residual" + df_convention : {"residual", "cluster", "normal"}, default "cluster" Inherited from :class:`DifferenceInDifferences`: df convention for - analytical t/p/CI. ``"residual"`` (default) uses the fitted - residual df; ``"cluster"`` uses the Stata/fixest ``G − 1`` on - clustered fits (no effect on unclustered or Conley fits); + analytical t/p/CI. ``"cluster"`` (default since 4.0) uses the + Stata/fixest ``G − 1`` on clustered fits (no effect on unclustered + or Conley fits); ``"residual"`` (the 3.x default) uses the fitted + residual df; ``"normal"`` deliberately uses normal-theory z inference at the fallback level on every fit, clustered or not. Survey df and per-coefficient Bell-McCaffrey DOF keep precedence under every - value. Default flips to ``"cluster"`` at v4. + value. The default flipped to ``"cluster"`` at 4.0 (row M-005); + pass ``df_convention="residual"`` to reproduce 3.x inference. Notes ----- diff --git a/diff_diff/wooldridge.py b/diff_diff/wooldridge.py index 43ea381d7..b937c7b6e 100644 --- a/diff_diff/wooldridge.py +++ b/diff_diff/wooldridge.py @@ -958,18 +958,19 @@ class WooldridgeDiD(BaseEstimator): The Section 8 trend specification is therefore unidentified on this branch. Use ``control_group="not_yet_treated"`` (the default) for the cohort_trends surface. - df_convention : {"residual", "cluster", "normal"}, default "residual" + df_convention : {"residual", "cluster", "normal"}, default "cluster" Degrees-of-freedom convention for the OLS analytical t/p/CI (per-cell - and aggregated). ``"residual"`` (default) uses the fitted residual df - — the 3.9 fix: the default hc1 arms previously used silent - normal-theory z; classical/hc2 keep their historical ``n − rank(X)`` - values bit-for-bit; ``"cluster"`` uses the Stata/fixest cluster df - ``G − 1`` on hc1-clustered fits (inert on one-way and conley - families); ``"normal"`` deliberately uses normal-theory z at the + and aggregated). ``"cluster"`` (default since 4.0) uses the + Stata/fixest cluster df ``G − 1`` on hc1-clustered fits (inert on + one-way and conley families); ``"residual"`` (the 3.x default) uses + the fitted residual df — the 3.9 fix: the hc1 arms previously used + silent normal-theory z; classical/hc2 keep their historical + ``n − rank(X)`` values bit-for-bit; ``"normal"`` deliberately uses normal-theory z at the fallback level. Survey design df and hc2_bm Bell-McCaffrey DOF always take precedence; the logit/poisson arms are knob-independent (survey df or normal theory — an explicitly non-default value warns - at fit time). The default flips to ``"cluster"`` at v4. + at fit time). The default flipped to ``"cluster"`` at 4.0 (row + M-129); pass ``df_convention="residual"`` for 3.x OLS inference. unsupported_period_action : {"drop", "error"}, default "drop" How to handle periods lacking the required comparison support. ``"drop"`` removes those periods before estimation and warns; @@ -1006,7 +1007,7 @@ def __init__( conley_metric: str = "haversine", conley_kernel: str = "bartlett", conley_lag_cutoff: Optional[int] = None, - df_convention: str = "residual", + df_convention: str = "cluster", unsupported_period_action: str = "drop", ) -> None: self._validate_constructor_args( @@ -1064,7 +1065,7 @@ def _validate_constructor_args( bootstrap_weights: str, vcov_type: str, cohort_trends: bool = False, - df_convention: str = "residual", + df_convention: str = "cluster", ) -> None: """Shared validation for both ``__init__`` and ``set_params``. @@ -1964,7 +1965,7 @@ def _build(frame: pd.DataFrame, w: Optional[np.ndarray]): # paths use the survey design df when present and normal theory # otherwise, independent of the knob — an explicitly non-default # value would be silently ignored there. - if self.method != "ols" and self.df_convention != "residual": + if self.method != "ols" and self.df_convention != "cluster": warnings.warn( f"df_convention={self.df_convention!r} has no effect on the " "logit/poisson arms — their inference uses the survey design " @@ -2438,10 +2439,11 @@ def _fit_ols( # so classical/hc2 stay bit-identical under the default; the # within-transform arm (hc1/conley) additionally subtracts the # absorbed [unit, time] rank. Survey df keeps precedence (df_inf); - # otherwise resolve through the ``df_convention`` knob — residual t - # by default (the 3.9 fix: hc1 arms previously dropped to silent - # normal theory), ``G − 1`` under "cluster" (hc1-clustered only; - # conley has no documented G−1 reference and passes + # otherwise resolve through the ``df_convention`` knob — ``G − 1`` + # under the 4.0 default "cluster" (hc1-clustered only; conley has + # no documented G−1 reference and passes ``n_clusters=None``), + # residual t under "residual" (the 3.9 fix: hc1 arms previously + # dropped to silent normal theory; conley stays at # ``n_clusters=None``), z under "normal". ``resolve_tail_df``'s # guard returns None on a non-positive residual df, reproducing the # old ``df_one_way = NaN → df_inf`` degenerate lane. @@ -2584,8 +2586,9 @@ def _fit_ols( else: # Every non-BM arm consumes the ONE knob-resolved fallback: # survey df on surveyed fits, else the df_convention-resolved - # analytical df (residual t by default; classical/hc2 - # reproduce the historical df_one_way values bit-for-bit). + # analytical df (t(G-1) under the default on hc1-clustered + # fits; classical/hc2 reproduce the historical df_one_way + # values bit-for-bit). t_stat, p_value, conf_int = safe_inference( eff["att"], eff["se"], alpha=self.alpha, df=df_fallback ) diff --git a/diff_diff/wooldridge_results.py b/diff_diff/wooldridge_results.py index df10be0d2..9886b4fc3 100644 --- a/diff_diff/wooldridge_results.py +++ b/diff_diff/wooldridge_results.py @@ -210,8 +210,9 @@ class WooldridgeDiDResults(BaseResults): _df_analytic_fallback: Optional[float] = field(default=None, repr=False) """The RESOLVED analytical fallback df the fit's non-BM ``safe_inference`` calls actually used (survey design df on surveyed fits; otherwise the - ``df_convention``-resolved value — residual df by default, ``G − 1`` - under "cluster", None under "normal"; ``df_inf`` on GLM fits, whose + ``df_convention``-resolved value — ``G − 1`` under the 4.0 default + "cluster", the residual df under "residual", None under "normal"; + ``df_inf`` on GLM fits, whose inference is knob-independent). ``aggregate()`` reads it so post-fit re-aggregation reproduces fit-time inference on every OLS arm — it stays LIVE on bootstrap fits, whose per-cell inference remains @@ -243,7 +244,8 @@ def __setstate__(self, state: Dict[str, Any]) -> None: ``aggregate()`` on a legacy result reproduces its fit-time classical/hc2 inference (legacy hc1 pickles carried None there, which restores the pre-3.9 normal-theory aggregation for them). - ``df_convention`` (added 3.9) defaults to ``"residual"``. + A missing ``df_convention`` (added 3.9) is backfilled as ``"residual"`` + - the convention every pre-3.9 fit ran under (NOT the 4.0 default). Missing ``unsupported_period_action`` defaults to ``"drop"``. Pickles written before the 4.0 storage flip (``docs/v4-design.md`` section 5) carry the simple aggregate under ``overall_att`` / diff --git a/docs/methodology/REGISTRY.md b/docs/methodology/REGISTRY.md index 1ba85ebbf..d691b59a9 100644 --- a/docs/methodology/REGISTRY.md +++ b/docs/methodology/REGISTRY.md @@ -592,17 +592,19 @@ This matches the behavior of R's `fixest::feols()` with absorbed FE. converged separately in 3.9 (M-127: the three-value `df_convention` knob below — the SE-scale `k` here and the inference df are independent accountings). `hc2`/`hc2_bm` use leverage / Satterthwaite DOF and are unaffected. -- **Note (deviation from R — clustered CR1 inference df):** under unit clustering, Python's - CR1 t-statistics/p-values/CIs use the **residual df** from the fitted design - (`n − K_full`, e.g. 148 on the live-R benchmark panel: n=200, 50 unit FE + time + treatment), - while `fixest::feols(cluster=...)` uses the **cluster df** `G − 1` (49 on the same panel; - Stata's `min(G−1)` convention). Both sides use t-distributions. At moderate |t| the CIs and - p-values are close; at large |t| the tails diverge by orders of magnitude (measured on the - live-R benchmark: |t|≈22 → Python `p≈1e-48` at t(148) vs fixest `p≈4e-27` at t(49) — both - numerically zero, which is what `tests/test_methodology_twfe.py::test_pvalue_matches_r_twfe` - pins; the convention itself is locked at a distinguishable moderate |t|~1.8 by - `tests/test_methodology_twfe.py::test_moderate_t_pins_residual_df_convention`, where the - t(148) and t(49) tails differ by ~5%). The SE itself matches fixest under the K_reference +- **Note (clustered CR1 inference df — the `df_convention` knob):** under unit clustering, + Python's CR1 t-statistics/p-values/CIs use the **cluster df** `G − 1` since 4.0 (49 on the + live-R benchmark panel: n=200, 50 unit FE + time + treatment) — the same reference + `fixest::feols(cluster=...)` and Stata's `min(G−1)` convention use — while the 3.x default + used the **residual df** from the fitted design (`n − K_full`, 148 on that panel), still + available as `df_convention="residual"` (the remaining deviation from R when chosen). Both + conventions use t-distributions. At moderate |t| the CIs and p-values are close; at large + |t| the tails diverge by orders of magnitude (measured on the live-R benchmark: |t|≈22 → + `p≈4e-27` at t(49), the fixest value, vs `p≈1e-48` at t(148) — both numerically zero, which + is what `tests/test_methodology_twfe.py::test_pvalue_matches_r_twfe` pins; the convention + itself is locked at a distinguishable moderate |t|~1.8 by + `tests/test_methodology_twfe.py::test_moderate_t_pins_cluster_df_convention`, where the + t(49) and t(148) tails differ by ~5%). The SE itself matches fixest under the K_reference convergence above (the historical ~0.25% non-nested-FE band is closed). **Three-value knob (2026-07, widened 3.9 / M-127):** `df_convention ∈ {"residual", "cluster", "normal"}` on the library-wide knob roster — `DifferenceInDifferences` / `TwoWayFixedEffects` / @@ -614,16 +616,19 @@ This matches the behavior of R's `fixest::feols()` with absorbed FE. unclustered, and conley alike — on lanes that were already t it is a new deliberate option, not a compatibility path). Fallback-level only under every value: survey/replicate df and per-coefficient Bell-McCaffrey DOF keep precedence, and point estimates/SEs/t-statistics - never move. The default remains `"residual"` on every surface except LPDiD and flips at v4 - (lifecycle: `docs/v4-deprecations.yaml` M-004..M-006 + M-128..M-131; work row in - `DEFERRED.md` → Version-gated (v4); flipping moves every clustered p-value/CI, so it is a - major-version change). **Out of the knob's scope** (own inference stacks): the IF-based + never move. **Since 4.0 the default is `"cluster"` on every surface** (LPDiD adopted it at + 3.9; the other seven flipped at the 4.0 cut, item 7a, 2026-10-28 — lifecycle rows + `docs/v4-deprecations.yaml` M-004..M-006 + M-128..M-131, pinned by + `tests/test_v4_inference_defaults.py`; the flip moved every clustered p-value/CI, which is + why it waited for a major version), and `"residual"` restores the 3.x residual-df inference + bit-for-bit. **Out of the knob's scope** (own inference stacks): the IF-based family — `CallawaySantAnna`, `TwoStageDiD`, `ChaisemartinDHaultfoeuille`, `EfficientDiD`, `ContinuousDiD`, `ChangesInChanges`, `SyntheticDiD`, `HeterogeneousAdoptionDiD` — and the OLS-based `TripleDifference` / `TROP` (own `safe_inference` df conventions, outside the variance-conventions audit matrix's shared-CR1 scope), plus `WooldridgeDiD`'s logit/poisson arms (survey df or normal theory, knob-independent). Locked by - `tests/test_estimators_vcov_type.py::TestDfConvention` (G−1 tail match, precedence, no-op default). The full-dummy (`fixed_effects=`) idiom carries + `tests/test_estimators_vcov_type.py::TestDfConvention` (G−1 default tail match, precedence, + the explicit `"residual"` baseline). The full-dummy (`fixed_effects=`) idiom carries `df_adjustment == 0` and is unchanged — its residual t-df already matched fixest's full-K count because every FE column sits in `k_visible`. **That claim is scoped to the residual df and the non-clustered variance families; the clustered CR1 @@ -1993,16 +1998,16 @@ where weights ŵ_{g,e} = n_{g,e} / Σ_g n_{g,e} (sample share of cohort g at eve `LinearRegression.get_inference()` inside `_fit_saturated_regression`. If the linalg helper fails (rank-deficient design, singular bread), the aggregated inference falls back to the `df_convention`-resolved - analytical df (t(residual) under the default) with an explicit + analytical df (t(G − 1) under the 4.0 default) with an explicit `UserWarning`. - **Note:** Provenance exposure (4.0 program row M-092; df values updated by the 3.9 M-127 tail-df fix): `SunAbrahamResults.event_study_df` records, per relative time, the df each stored event-study row's `safe_inference` actually received - the per-event BM contrast DOF above under `hc2_bm`, the survey design df (post-drop under a replicate refit) on survey fits, - the `df_convention`-resolved fallback on plain analytic fits (FINITE - residual df under the 3.9 default; `G − 1` under `"cluster"`; NaN under - `"normal"`), and NaN on rows whose BM DOF was non-finite, where + the `df_convention`-resolved fallback on plain analytic fits (`G − 1` + under the 4.0 default `"cluster"`; the FINITE residual df under + `"residual"`; NaN under `"normal"`), and NaN on rows whose BM DOF was non-finite, where `safe_inference`'s own non-finite-df guard already yields all-NaN inference. Note the two failure modes differ and are both faithfully recorded: a helper-level failure warns and falls back to the resolved @@ -2064,24 +2069,26 @@ where weights ŵ_{g,e} = n_{g,e} / Σ_g n_{g,e} (sample share of cohort g at eve theory inside the same fit): - Cohort-level p-values: t-distribution via `LinearRegression.get_inference()`, following the `df_convention` knob - (residual df by default; `G − 1` under `"cluster"`; z under `"normal"`). + (`G − 1` under the 4.0 default `"cluster"`; the residual df under + `"residual"`; z under `"normal"`). - Aggregated event study and overall ATT p-values: - Under `vcov_type="hc2_bm"`: t-distribution with CR2 Bell-McCaffrey contrast DOF per aggregated effect (see "Phase 1b aggregated BM contrast DOF" Note above). Matches `clubSandwich::Wald_test( test="HTZ")$df_denom`. - Under `vcov_type ∈ {"classical","hc1","hc2","conley"}` (no survey): - the SAME `df_convention`-resolved df as the cells — t(residual df of - the saturated regression) by default, t(G − 1) under `"cluster"` - (inert under conley), z under `"normal"`. + the SAME `df_convention`-resolved df as the cells — t(G − 1) under the + 4.0 default `"cluster"` (inert under conley, which keeps the residual + df), t(residual df of the saturated regression) under `"residual"`, + z under `"normal"`. - Under replicate-weight survey: t-distribution with replicate-derived DOF (`survey_metadata.df_survey`). - - **Deviation from R**: R's fixest uses t-distribution at all levels with - the CLUSTER df `G − 1` on clustered fits; the 3.9 default uses the - residual df instead (the library-wide `df_convention` deviation - documented in the TwoWayFixedEffects section — `df_convention= - "cluster"` opts into the fixest convention today, and the default flips - at v4). The `hc2_bm` aggregated path matches clubSandwich exactly. + - **Note:** R's fixest uses t-distribution at all levels with the CLUSTER + df `G − 1` on clustered fits — the 4.0 default matches it (M-128, item + 7a); the 3.9 default used the residual df instead (the library-wide + `df_convention` convention documented in the TwoWayFixedEffects + section — `df_convention="residual"` restores it). The `hc2_bm` + aggregated path matches clubSandwich exactly. **Reference implementation(s):** - R: `fixest::sunab()` (Laurent Bergé's implementation) @@ -2192,16 +2199,17 @@ where `W_it(h) = 1[K_it = h]` are lead indicators, estimated on `Omega_0` only. - **Note (pretrends lead-regression tail df, 3.9 / M-127):** the per-lead `gamma_h` t/p/CI previously used silent normal-theory z on plain clustered fits (the tail-df defect family); they now follow the `df_convention` - knob — t(residual df `n − k_kept − rank(absorbed [time, unit])` on the - Omega_0 lead design) under the default `"residual"`, t(G − 1) under - `"cluster"`, z under `"normal"` — with the full-design survey df keeping + knob — t(G − 1) under the 4.0 default `"cluster"` (M-131, item 7a), + t(residual df `n − k_kept − rank(absorbed [time, unit])` on the Omega_0 + lead design) under `"residual"` (the 3.9 default), z under `"normal"` — + with the full-design survey df keeping precedence. This is the ONE ImputationDiD surface on the shared clustered CR1 sandwich; the BJS Theorem-3 overall/post-treatment inference is knob-independent (L3), and the JOINT pretrend Wald F deliberately keeps its cluster-robust `F(q, max(G − 1, 1))` denominator (the standard joint-test convention; it consumes gamma/V_gamma, not the per-lead t's, - coheres with `df_convention="cluster"`, and converges with the t's at the - v4 flip) — the per-lead-t vs joint-F pairing is a stated convention, not + and coheres with the 4.0 default `df_convention="cluster"`, whose per-lead + t(G − 1) it now shares) — the per-lead-t vs joint-F pairing is a stated convention, not a silent split. Since the M-021 post-fit migration the inert-config warning predicate is REACHABILITY-based, no longer aggregate-keyed: an explicitly non-default value warns iff the per-lead @@ -2412,17 +2420,18 @@ Estimated via WLS with Q-weights. The delta_h coefficients identify theta_kappa^ **Note (tail df, 3.9 / M-127):** the `hc1` analytical t/p/CI previously used silent normal-theory z on the non-survey lane (the tail-df defect family); -they now follow the library-wide `df_convention` knob — t(pooled residual df -`n_eff − k_kept`, positive-weight rows) under the default `"residual"`, -t(G − 1) under `"cluster"`, z under `"normal"` — with hc2_bm BM contrast DOF +they now follow the library-wide `df_convention` knob — t(G − 1) under the +4.0 default `"cluster"` (M-130, item 7a), t(pooled residual df `n_eff − +k_kept`, positive-weight rows) under `"residual"` (the 3.9 default), z under +`"normal"` — with hc2_bm BM contrast DOF and survey/replicate df keeping precedence. Two StackedDiD-specific caveats: (a) the `"cluster"` G counts POSITIVE-WEIGHT clusters (`effective_cluster_count`), while the reported `results.n_clusters` keeps the raw unique count — the two diverge only when a cluster's total composed weight is zero; (b) the stacked design replicates control units across -sub-experiments, so the pooled residual df is typically large and the -default's z → t movement is a convention alignment with near-zero numeric -effect — the materially moving step on this surface is the v4 flip to +sub-experiments, so the pooled residual df is typically large and the 3.9 +z → t(residual) movement was a convention alignment with near-zero numeric +effect — the materially moving step on this surface was the 4.0 flip to `G − 1` (M-130). No public per-row df channel changes: `event_study_df` records the resolved df per event time as before. @@ -2594,11 +2603,12 @@ where `g(·)` is the link inverse (logistic or exp), `η_i` is the individual li aggregated t/p/CI now consume ONE `df_convention`-resolved fallback df — the default-`hc1` arms (within-transform AND `hc1`+`cohort_trends` full-dummy) previously used silent normal-theory z (the tail-df defect - family), and now use t(residual df): `n − k_kept − rank(absorbed - [unit, time])` on the within arm, the historical `n − rank(X)` on the - full-dummy arms (`classical`/`hc2` values bit-identical). `"cluster"` - gives t(G − 1) on hc1-clustered fits (inert on one-way/conley families); - `"normal"` gives z. Survey design df and hc2_bm BM DOF keep precedence; + family), and now use t(G − 1) on hc1-clustered fits under the 4.0 default + `"cluster"` (M-129, item 7a; inert on one-way/conley families, which + keep the residual df); `"residual"` (the 3.9 default) gives t(residual + df): `n − k_kept − rank(absorbed [unit, time])` on the within arm, the + historical `n − rank(X)` on the full-dummy arms (`classical`/`hc2` values + bit-identical); `"normal"` gives z. Survey design df and hc2_bm BM DOF keep precedence; the logit/poisson arms are knob-INDEPENDENT (survey df when present, normal theory otherwise — an explicitly non-default `df_convention` warns at fit time). Post-fit `aggregate()` reproduces fit-time inference @@ -2769,7 +2779,7 @@ Eq. 12 reuses the absorbing clean control and only restricts the treated set (a The paper specifies no standard-error formula (Section 1 defers to "standard, well-understood techniques"); the reference Stata `lpdid` uses `vce(cluster unit)`. The entries below document diff-diff's inference and scope choices. -1. **Note:** Standard errors are **cluster-robust at the unit level by default** - `cluster=None` auto-clusters at the unit identifier and the results record `cluster_name`/`n_clusters` - with a `t(G-1)` reference distribution (G = realized clusters in each horizon's clean-control sample). Matches Stata `lpdid` `vce(cluster unit)` - measured on the Stata-anchored surfaces (absorbing via the R/`teffects` arms; non-absorbing per Deviation 4's scoping: Eq. 12 everywhere, Eq. 13 post + pooled-post on the convention-neutral subsample; elsewhere the two implementations' clean-sample conventions differ, see Deviation 4); the paper prescribes no SE. **Since 3.9 (M-127) the reference is the `df_convention` knob's `"cluster"` value — LPDiD's DEFAULT, so nothing moved**: `"residual"` opts into t(per-horizon residual df `n_eff − k_kept`; on the RA path `n_total − k0_kept − 1`, the pooled M-estimator's parameter count — nuisance coefficients plus the ATT — a library convention with no external anchor since Stata `teffects ra` reports z), and `"normal"` into z. The G here stays the RAW unique cluster count (deliberately not `effective_cluster_count`: LPDiD's reweights are strictly positive and there is no user weight column, so the two cannot diverge). **Carve-out to the flat `t(G-1)` claim:** the degenerate lanes — the unclustered-refit fallback when the clustered solve fails, the RA `G<=1` lane, and the saturated-design early return (`n_obs <= k`) — keep the literal `df=None` (normal theory, NaN provenance) under ALL knob values, exactly as before 3.9. +1. **Note:** Standard errors are **cluster-robust at the unit level by default** - `cluster=None` auto-clusters at the unit identifier and the results record `cluster_name`/`n_clusters` - with a `t(G-1)` reference distribution (G = realized clusters in each horizon's clean-control sample). Matches Stata `lpdid` `vce(cluster unit)` - measured on the Stata-anchored surfaces (absorbing via the R/`teffects` arms; non-absorbing per Deviation 4's scoping: Eq. 12 everywhere, Eq. 13 post + pooled-post on the convention-neutral subsample; elsewhere the two implementations' clean-sample conventions differ, see Deviation 4); the paper prescribes no SE. **Since 3.9 (M-127) the reference is the `df_convention` knob's `"cluster"` value — LPDiD's DEFAULT, so nothing moved; since the 4.0 flip (item 7a) it is the library-wide default too**: `"residual"` opts into t(per-horizon residual df `n_eff − k_kept`; on the RA path `n_total − k0_kept − 1`, the pooled M-estimator's parameter count — nuisance coefficients plus the ATT — a library convention with no external anchor since Stata `teffects ra` reports z), and `"normal"` into z. The G here stays the RAW unique cluster count (deliberately not `effective_cluster_count`: LPDiD's reweights are strictly positive and there is no user weight column, so the two cannot diverge). **Carve-out to the flat `t(G-1)` claim:** the degenerate lanes — the unclustered-refit fallback when the clustered solve fails, the RA `G<=1` lane, and the saturated-design early return (`n_obs <= k`) — keep the literal `df=None` (normal theory, NaN provenance) under ALL knob values, exactly as before 3.9. 2. **Note:** The regression-adjustment (RA) covariate path (`reweight=True` with covariates/absorb) reports an **influence-function cluster variance** `sum_c (sum_{i in c} psi_i)^2 / n^2`, in the same family as `ImputationDiD`'s Theorem-3 / BJS variance (see "IF-based variance estimators vs analytical-sandwich estimators" above). Its single Gram inversion is routed through `linalg._rank_guarded_inv` (finite SE on the identified subspace under near-collinearity; NaN at rank 0). Unlike the default/weighted `solve_ols` `hc1`-cluster path - which applies the `(G/(G-1))*((n-1)/(n-K_reference))` finite-sample factor (see the nested-dummy Note below) - the RA IF variance carries **no finite-sample factor**, while both paths share the `t(G-1)` reference. **PR-B2 validated this asymmetry as faithful to the authors' own tooling**, not a defect: the no-factor RA convention matches the canonical Stata `teffects ra ... atet vce(cluster)`, while the default path matches `feols`/`reghdfe`. **Originally inferred from the authors' `lpdid_regression_adjustment.do` degrees-of-freedom comments, this is now MEASURED against a runnable `teffects`:** `benchmarks/stata/generate_lpdid_ra_golden.do` runs `teffects ra (Dy x i.time) (tdiff), atet vce(cluster unit)` on an independently reconstructed clean sample (a Stata port of the `generate_lpdid_golden.R` `prep`/`clean_h` recipe), and the library RA IF SE matches it to **~1e-16 at all 7 event-study horizons** - the SE ratio is a flat 1.0 (no `G/(G-1)`, no `(n-1)/(n-k)`), directly confirming the no-finite-sample-factor convention (golden `benchmarks/data/lpdid_ra_stata_golden.json`; test `tests/test_lpdid_ra_stata_parity.py`, which also gates the Stata clean sample's realized size and cluster count `(e(N), e(N_clust))` against the library's `(n_obs, n_clusters)`). The RA *point* estimate is triple-anchored: Python == R full-interaction (`i.dtreat##(i.time c.x)`) == Stata `teffects`, to ~1e-10..1e-13 (`tests/test_methodology_lpdid.py::test_ra_covariate_point`). Because Stata is node-locked and cannot run in CI (goldens are committed, exactly like the R arm), the CI-runnable guards remain the regression pin (`test_ra_covariate_se_regression_pin` / `RA_SE_PIN`) and the ungated Monte-Carlo coverage study `benchmarks/python/coverage_lpdid_ra.py` (~0.95 empirical coverage of the true effect at cluster counts G in {30, 100, 300}). 3. **Note:** Direct covariate inclusion (`reweight=False` with covariates/absorb) emits a `UserWarning`: per online Appendix B.2.2 it preserves the non-negative LP-DiD weighting result only under linear and homogeneous covariate effects, so the regression-adjustment path (`reweight=True`) is preferred. 4. **Deviation from R:** Scope - non-absorbing treatment (Section 4.2) implements the **entry-effect** estimands (`non_absorbing="first_entry"` / `"effect_stabilization"`, PR-C1). **PR-C2 R-parity-validated both modes against an INDEPENDENT `fixest::feols` reconstruction of the paper's Eq. 12 / Eq. 13 clean-sample restrictions** (point and SE match to ~1e-13/~1e-15 for the variance-weighted variants; the `effect_stabilization` reweighted point matches and its SE is pinned as a regression guard - a small weighted-cluster convention difference vs feols; `tests/test_methodology_lpdid.py::TestLPDiDNonAbsorbingParityR`). The recipe's independence was demonstrated when an earlier draft's Eq. 12 control off-by-one diverged from the already-correct library and was corrected against the paper, plus a hand-computed Python micro-check. **`alexCardazzi/lpdid`'s `nonabsorbing_lag` is NOT a faithful Eq. 13** (it clamps `treat_diff[<0]<-0`, so its clean-control window blocks only treatment turn-*ons* - a violation of Eq. 13's stated `Delta-D = 0` both-directions control condition; it also holds two convention differences on surfaces Eq. 13 does not specify: a forward placebo window, and NA-exclusion of pre-panel-treated rows where the library clamps pre-`min_t` to untreated): it diverges ~0.01-0.05 from Eq. 13 even on a monotone no-off-switch panel, so it is **recorded in the golden `meta` as a divergent third-party reference, not a parity gate** (the alexCardazzi-pooled precedent; the golden's `meta.alex_note`/`meta.boundary_note` strings predate this two-tier framing and are generation-time text - this entry is the living statement). `first_entry` (Eq. 12) has no R-package analogue (anchored on the independent feols recipe, and - since the Stata non-absorbing arm - on the authors' Stata `lpdid` package end-to-end via `nonabsorbing(, firsttreat notyet)`). **The Stata canonical SE is now MEASURED against the authors' `lpdid` package (SSC, v1.0.3) end-to-end** - the first external anchor for the non-absorbing reweighted SE, the non-absorbing pooled windows (points and SEs), and the Eq. 12 reweighted point: Eq. 12 agrees on the full committed panel at every surface (all event-study horizons incl. placebos + both pooled windows; att ~1e-14 vw / ~1.3e-8 rw, SE ~6e-17 vw / ~2.3e-9 rw), and Eq. 13 agrees at post horizons + pooled post on a convention-neutral subsample (att ~1.1e-14 vw / ~1.9e-8 rw, SE ~1.8e-16 vw / ~1.6e-9 rw; golden `benchmarks/data/lpdid_nonabsorbing_stata_golden.json`, test `tests/test_lpdid_nonabsorbing_stata_parity.py`). **Three measured convention differences vs the package**, adjudicated against the paper's stated Eq. 13 restriction (verified against the open-access working-paper text, whose Eq. 18 is the published JAE Eq. 13): (a) *exact-L re-entry spells* - Eq. 13's treated condition is a LEVELS condition (`D = 0` on `[t-L, t-1]`), which the library implements; the package's switch-free lag window additionally excludes an exit AT `t-L`, effectively requiring `L+1` untreated periods - **the package is stricter than the paper here**; (b) *pre-panel boundary handling* - the package's missing-lag semantics admit always-treated units as controls at early `t` where the library's clamp excludes them (a paper-silent surface; both are documented conventions); (c) *placebo windows* - the package builds `h<0` clean samples by recursive lagged intersection of its `CCS_0` indicator, the library uses the backward window `[t-max(L,-h), t-1]` (also paper-silent; both are conventions). The full-panel Eq. 13 divergence from (a)+(b) and the placebo/pooled-pre divergence from (c) are recorded in the golden and locked by divergence-documentation gates (att floors + sample-size mismatch asserts), so parity claims stay scoped: Eq. 13 full-panel and placebo/pooled-pre inference remain OUTSIDE the Stata anchor (the full-panel reweighted SEs stay pinned via `RW_SE_PIN`; the pooled-pre SE has no pin or anchor). Appendix-C exit-event dynamics and the non-absorbing RA path (the package's RA engine is `listreg`, and the committed non-absorbing panel has no covariate) remain deferred follow-ups. diff --git a/docs/methodology/variance-conventions.md b/docs/methodology/variance-conventions.md index 43a5f0bdb..a80b73c99 100644 --- a/docs/methodology/variance-conventions.md +++ b/docs/methodology/variance-conventions.md @@ -22,16 +22,16 @@ produced wrong figures three separate times while this inventory was drafted). | surface | CR1 `k` (multiset) | tail df (multiset) | status | reason | |---|---|---|---|---| -| `did_absorb_hc1_cluster_unit` | 7 | 294 | **legitimate** | K_reference (D2 fixed): k = 2 visible + rank(time given unit) = 5; matches reghdfe/fixest ssc(K.fixef='nested') | -| `did_fixed_effects_hc1_cluster_unit` | 7 | 294 | **legitimate** | K_reference (D1 fixed): 66 visible minus the 59 cluster-nested unit dummies -> identical SE to did_absorb (documented deviation from a literal explicit-dummy R comparison, which counts all 66) | -| `did_plain_hc1_cluster_unit` | 4 | 356 | **legitimate** | no absorbed FE: visible k is the whole design; nothing is omitted | -| `twfe_hc1_cluster_unit_time_post` | 3 | 298 | **legitimate** | K_reference (D2 fixed): 2 visible + rank(post given unit) = 1; matches fixest cluster arm at rel 0 (committed golden) | -| `wooldridge_hc1_within` | 15 | 286, 286, 286, 286, 286, 286, 286, 286, 286, 286 | **legitimate** | CR1 k = K_reference (D2 fixed: 9 cells + T = 15, no intercept col -> +1 term; jwdid arms at ratio 1.0); tail df converged in 3.9 (M-127): the default-hc1 arms use t(residual df = n - k_kept - absorbed rank) via the df_convention knob (was silent normal theory); G-1 under 'cluster', z under 'normal' | -| `sun_abraham_hc1` | 21 | 280, 280, 280, 280, 280, 280, 280, 280, 280, 280, 280, 280, 280, 280, 280, 280, 280, 280, 280, 280, 280, 280, 280 | **legitimate** | CR1 k = K_reference (D2 fixed: 15 cells + 6, no intercept col; fixest sunab parity ~5e-15); D4 fixed in 3.9 (M-127): cells AND aggregates share the saturated fit's residual df under the df_convention knob (aggregates previously dropped to normal theory inside the same fit) | -| `stacked_did_hc1` | 6 | 309, 309, 309 | **legitimate** | L1: k_total is clubSandwich CR1S by construction (stacked_did.py pins vcovCR(type='CR1S') at atol=1e-10); tail df converged in 3.9 (M-127): t(pooled residual df n_eff - k_kept) via the df_convention knob (was silent normal theory; stacked rows make the residual df large, so z -> t is a convention alignment) | -| `lpdid_pre2_post2` | 4, 4, 5, 5, 5, 6 | 59, 59, 59, 59, 59, 59 | **legitimate** | L2: G-1 tail df (Stata/fixest convention) — since 3.9 the df_convention='cluster' DEFAULT on LPDiD (bit-identical; the one surface already at the v4 target) | -| `twfe_es_within_hc1_cluster_unit` | 11 | 290, 290, 290, 290, 290 | **legitimate** | TWFE event-study, within spec (4.0 cut, PR 4c-i): K_reference = 6 visible (intercept + 5 interactions) + rank(time given the absorbed, cluster-nested unit FE) = 5, i.e. 11; the full-dummy form would read 70 visible minus the 59 cluster-nested unit dummies = 11 | -| `twfe_es_pooled_hc1_cluster_time` | 7 | 348, 348, 348, 348, 348 | **legitimate** | TWFE event-study, pooled spec (4.0 cut, PR 4c-i): the NESTED orientation of the built-in period dummies under a time cluster - 12 visible minus their rank 5 = 7 (under-subtraction is caught here; the unit-cluster within row catches over-) | +| `did_absorb_hc1_cluster_unit` | 7 | 294 | **legitimate** | K_reference (D2 fixed): k = 2 visible + rank(time given unit) = 5; matches reghdfe/fixest ssc(K.fixef='nested') Explicit df_convention='residual' since the 4.0 default is G-1 (see the _default_df twin). | +| `did_fixed_effects_hc1_cluster_unit` | 7 | 294 | **legitimate** | K_reference (D1 fixed): 66 visible minus the 59 cluster-nested unit dummies -> identical SE to did_absorb (documented deviation from a literal explicit-dummy R comparison, which counts all 66) Explicit df_convention='residual' since the 4.0 default is G-1 (see the _default_df twin). | +| `did_plain_hc1_cluster_unit` | 4 | 356 | **legitimate** | no absorbed FE: visible k is the whole design; nothing is omitted. Explicit df_convention='residual' since the 4.0 default is G-1 (see the _default_df twin). | +| `twfe_hc1_cluster_unit_time_post` | 3 | 298 | **legitimate** | K_reference (D2 fixed): 2 visible + rank(post given unit) = 1; matches fixest cluster arm at rel 0 (committed golden) Explicit df_convention='residual' since the 4.0 default is G-1 (see the _default_df twin). | +| `wooldridge_hc1_within` | 15 | 286, 286, 286, 286, 286, 286, 286, 286, 286, 286 | **legitimate** | CR1 k = K_reference (D2 fixed: 9 cells + T = 15, no intercept col -> +1 term; jwdid arms at ratio 1.0); tail df converged in 3.9 (M-127): the default-hc1 arms use t(residual df = n - k_kept - absorbed rank) via the df_convention knob (was silent normal theory); G-1 under 'cluster', z under 'normal' Explicit df_convention='residual' since the 4.0 default is G-1 (see the _default_df twin). | +| `sun_abraham_hc1` | 21 | 280, 280, 280, 280, 280, 280, 280, 280, 280, 280, 280, 280, 280, 280, 280, 280, 280, 280, 280, 280, 280, 280, 280 | **legitimate** | CR1 k = K_reference (D2 fixed: 15 cells + 6, no intercept col; fixest sunab parity ~5e-15); D4 fixed in 3.9 (M-127): cells AND aggregates share the saturated fit's residual df under the df_convention knob (aggregates previously dropped to normal theory inside the same fit) Explicit df_convention='residual' since the 4.0 default is G-1 (see the _default_df twin). | +| `stacked_did_hc1` | 6 | 309, 309, 309 | **legitimate** | L1: k_total is clubSandwich CR1S by construction (stacked_did.py pins vcovCR(type='CR1S') at atol=1e-10); tail df converged in 3.9 (M-127): t(pooled residual df n_eff - k_kept) via the df_convention knob (was silent normal theory; stacked rows make the residual df large, so z -> t is a convention alignment) Explicit df_convention='residual' since the 4.0 default is G-1 (see the _default_df twin). | +| `lpdid_pre2_post2` | 4, 4, 5, 5, 5, 6 | 59, 59, 59, 59, 59, 59 | **legitimate** | L2: G-1 tail df (Stata/fixest convention) — since 3.9 the df_convention='cluster' DEFAULT on LPDiD (bit-identical; adopted at 3.9, the library-wide default since the 4.0 flip) | +| `twfe_es_within_hc1_cluster_unit` | 11 | 290, 290, 290, 290, 290 | **legitimate** | TWFE event-study, within spec (4.0 cut, PR 4c-i): K_reference = 6 visible (intercept + 5 interactions) + rank(time given the absorbed, cluster-nested unit FE) = 5, i.e. 11; the full-dummy form would read 70 visible minus the 59 cluster-nested unit dummies = 11 Explicit df_convention='residual' since the 4.0 default is G-1 (see the _default_df twin). | +| `twfe_es_pooled_hc1_cluster_time` | 7 | 348, 348, 348, 348, 348 | **legitimate** | TWFE event-study, pooled spec (4.0 cut, PR 4c-i): the NESTED orientation of the built-in period dummies under a time cluster - 12 visible minus their rank 5 = 7 (under-subtraction is caught here; the unit-cluster within row catches over-) Explicit df_convention='residual' since the 4.0 default is G-1 (see the _default_df twin). | | `lpdid_absorb_nested_cluster_grp` | 4, 4, 5, 5, 5, 6 | 1, 1, 1, 1, 1, 1 | **legitimate** | LPDiD absorb dummies nested in the cluster subtract their rank (adj -1 per horizon: region == grp here); _event_time stays counted (unit-level cluster does not nest time); G-1 tail df (L2) | | `imputation_default` | — | None | **legitimate** | L3: BJS imputation variance, not the shared CR1 sandwich | | `imputation_pretrends_event_study` | unpinned | 157, 157, 157, None, None, None, None, None | **legitimate** | pretrends lead regression: CR1 k = K_reference (D2 fixed); tail df converged in 3.9 (M-127): leads use t(residual df) via the df_convention knob (was silent normal theory); the None calls are the knob-independent BJS aggregates (L3) | @@ -41,6 +41,14 @@ produced wrong figures three separate times while this inventory was drafted). | `dml_did_bad_control` | — | None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None | **legitimate** | L3: Caetano et al. (2026) Eq. 11 augmented-score plug-in variance (same psi_bar - D*theta/pi centering as Chang Thm 2; per-unit influence function; normal-theory safe_inference on no-design fits; cluster= fits use the CR1 per-cell variance with df=df_survey t-inference) and the per-cell ATT_X diagnostic tuple sharing the same branch and df (one extra safe_inference call per retained cell) | | `dml_did_rcs` | — | None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None | **legitimate** | L3: Chang (2020) Thm 2 lambda-corrected augmented-score plug-in variance (Case 2; per-observation influence function; normal-theory safe_inference on no-design fits; survey_design=/cluster= fits use the design-based CR1/weighted-IF per-cell variance — replicate designs the IF-reweighting per-cell variance — with df=df_survey t-inference outside the shared CR1 sandwich) | | `duration_did` | — | None, None | **legitimate** | L3: Deaner & Ku (2026) whole-individual pooled bootstrap (centered absolute-deviation pointwise and simultaneous bands); no CR1 sandwich, normal-theory safe_inference gate only | +| `did_absorb_hc1_cluster_unit_default_df` | 7 | 59 | **legitimate** | t(G-1): the 4.0 df_convention default (M-004..M-006, M-128..M-131) - same CR1 k as the explicit-'residual' twin, tail df G-1 | +| `twfe_hc1_cluster_unit_default_df` | 3 | 59 | **legitimate** | t(G-1): the 4.0 df_convention default (M-004..M-006, M-128..M-131) - same CR1 k as the explicit-'residual' twin, tail df G-1 | +| `wooldridge_hc1_within_default_df` | 15 | 59, 59, 59, 59, 59, 59, 59, 59, 59, 59 | **legitimate** | t(G-1): the 4.0 df_convention default (M-004..M-006, M-128..M-131) - same CR1 k as the explicit-'residual' twin, tail df G-1 | +| `sun_abraham_hc1_default_df` | 21 | 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59 | **legitimate** | t(G-1): the 4.0 df_convention default (M-004..M-006, M-128..M-131) - same CR1 k as the explicit-'residual' twin, tail df G-1 | +| `stacked_did_hc1_default_df` | 6 | 59, 59, 59 | **legitimate** | t(G-1): the 4.0 df_convention default (M-004..M-006, M-128..M-131) - same CR1 k as the explicit-'residual' twin, tail df G-1 | +| `twfe_es_within_hc1_cluster_unit_default_df` | 11 | 59, 59, 59, 59, 59 | **legitimate** | t(G-1): the 4.0 df_convention default (M-004..M-006, M-128..M-131) - same CR1 k as the explicit-'residual' twin, tail df G-1 | +| `twfe_es_pooled_hc1_cluster_time_default_df` | 7 | 5, 5, 5, 5, 5 | **legitimate** | t(G-1): the 4.0 df_convention default (M-004..M-006, M-128..M-131) - same CR1 k as the explicit-'residual' twin, tail df G-1 | +| `imputation_pretrends_event_study_default_df` | unpinned | 59, 59, 59, None, None, None, None, None | **legitimate** | t(G-1): the 4.0 df_convention default (M-004..M-006, M-128..M-131) - same CR1 k as the explicit-'residual' twin, tail df G-1 | cr1_k is the sorted multiset of K_reference counts reaching the shared clustered CR1 denominator — visible columns + the signed cluster_k_adjustment @@ -127,8 +135,8 @@ output). full-dummy lm; pinned to clubSandwich::vcovCR(type="CR1S") at atol=1e-10. CR1S is a real second convention, correct by construction. - **L2 — LPDiD's G-1 tail df**: the Stata/fixest convention. Since 3.9 it is - the `df_convention="cluster"` DEFAULT on LPDiD (the one surface already at - the library-wide v4 target — bit-identical, no numbers moved), with + the `df_convention="cluster"` DEFAULT on LPDiD (adopted ahead of the + library-wide 4.0 flip — bit-identical, no numbers moved there), with "residual"/"normal" as opt-ins; the degenerate lanes (unclustered refit, RA G<=1, saturated early return) keep literal df=None under every value. - **L3 — CallawaySantAnna / DMLDiD / TwoStageDiD / ImputationDiD (default) / @@ -158,13 +166,14 @@ output). **Effective-leverage guard:** HC2, HC3, and unweighted one-way HC2-BM return warning + all-NaN covariance/DOF when any positive-weight observation has `h_ii >= 1 - 1e-8` (all rows are effective without weights); identified point estimates remain. Zero-weight rows contribute nothing and cannot trigger the guard, preserving frequency-expansion parity even with zero counts. Weighted/clustered HC2-BM retains CR2, including the all-ones-pweight boundary; its output can differ from unweighted HC2-BM at leverage one. No over-one HC1 fallback is used in the leverage-family branch. -## Tail-df landscape (converged, 3.9 / M-127) +## Tail-df landscape (converged, 3.9 / M-127; default flipped at 4.0) ONE three-value knob (`df_convention ∈ {"residual", "cluster", "normal"}`) now governs the analytical fallback df on every shared-CR1 surface: DiD/TWFE/LinearRegression + SunAbraham/WooldridgeDiD-OLS/StackedDiD/ -ImputationDiD-pretrends (default "residual") and LPDiD (default "cluster", -its Stata-anchored t(G−1)). Survey/replicate df and hc2_bm Bell-McCaffrey +ImputationDiD-pretrends and LPDiD, all at the default "cluster" (the +Stata-anchored t(G−1); LPDiD since 3.9, the rest since the 4.0 cut) with +"residual" restoring the 3.x residual df. Survey/replicate df and hc2_bm Bell-McCaffrey DOF keep precedence under every value; "cluster" fails closed warn+NaN at G<=1 and is inert on unclustered/conley fits; "normal" is deliberate z at the fallback level everywhere. Per-surface residual-df definitions: the @@ -173,9 +182,12 @@ design absorbs FE; positive-weight n_eff on weighted lanes; on the LPDiD RA path `n_total − k0_kept − 1`, the pooled M-estimator count). The pre-fix defect magnitude for the record: at |t| = 2 normal theory understated the t(G−1) p-value by 24.2% at G=20, 13.3% at G=40, 1.2% at G=500 — larger -than the D2 SE gap was before its fix. The remaining program step is the -v4 default flip "residual" → "cluster" (M-004..M-006 + M-128..M-131, the -locked #663 direction); the resolver is `diff_diff.utils.resolve_tail_df`, +than the D2 SE gap was before its fix. The 4.0 cut (item 7a, 2026-10-28) +flipped the default "residual" → "cluster" (M-004..M-006 + M-128..M-131, +the locked #663 direction; pinned by `tests/test_v4_inference_defaults.py`), +so the matrix above carries every clustered surface twice: the explicit +`"residual"` arm (the residual-df literals, which pin the K_full accounting) +and its `_default_df` twin at G−1; the resolver is `diff_diff.utils.resolve_tail_df`, and the out-of-scope roster (IF-based estimators, TripleDifference/TROP, Wooldridge GLM arms) is enumerated in the REGISTRY TwoWayFixedEffects deviation note. diff --git a/docs/migration-4.0.md b/docs/migration-4.0.md index f7aaf11e0..b66789b0f 100644 --- a/docs/migration-4.0.md +++ b/docs/migration-4.0.md @@ -301,8 +301,42 @@ and `BUSINESS_REPORT_SCHEMA_VERSION` is `"4.0"` (its `headline` block carries no Two changes alter results without changing any call: -- **`df_convention` flips to `"cluster"`** on seven estimators. Pass `df_convention="residual"` - to reproduce 3.x numbers exactly. +- **`df_convention` flips to `"cluster"`** on seven estimators (rows M-004..M-006 and + M-128..M-131): `DifferenceInDifferences`, `TwoWayFixedEffects`, `LinearRegression`, + `SunAbraham`, `WooldridgeDiD` (OLS arms), `StackedDiD` and `ImputationDiD` (pretrends + leads); `LPDiD` was already there. Clustered analytical p-values and confidence intervals + now use the Stata/fixest `t(G − 1)` reference instead of the fitted residual df + `t(n − K)`; point estimates, standard errors and t-statistics never move. + + ```python + TwoWayFixedEffects().fit(df, outcome="y", treatment="d", post="post", unit="unit") + # 4.0: p-values / CIs at t(G - 1) on the auto-clustered fit + TwoWayFixedEffects(df_convention="residual").fit( + df, outcome="y", treatment="d", post="post", unit="unit" + ) # 3.x inference, bit-identical + ``` + + **Which fits move.** Every fit whose analytical sandwich is clustered: `TwoWayFixedEffects` + (static and event-study with `unit=`), `SunAbraham`, `WooldridgeDiD` OLS arms and + `StackedDiD` cluster at `unit` by default, so their default fits move; `ImputationDiD` + moves only on the `pretrends=True` lead rows; `DifferenceInDifferences` and + `LinearRegression` move only when you pass `cluster=` / `cluster_ids=`. The analytical + lanes that survive inside a bootstrapped fit move too: `WooldridgeDiD`'s per-cell inference + and its post-fit `aggregate()`, and `ImputationDiD`'s bootstrapped pretrends leads. Code that + relays these p-values moves with them: the fake-timing / fake-group placebo diagnostics + (when `estimator_kwargs` carries `cluster=`) and the power simulators + (`simulate_power` / `simulate_mde` / `simulate_sample_size`) on `TwoWayFixedEffects`, + `SunAbraham` and `StackedDiD` profiles. + + **Which fits do not move.** Unclustered and Conley fits (the knob is inert without a + cluster count), survey-design fits (the survey df keeps precedence), `vcov_type="hc2_bm"` + (Bell-McCaffrey DOF keep precedence), bootstrap percentile p-values and CIs, the BJS + overall/post inference of `ImputationDiD`, `WooldridgeDiD`'s logit/poisson arms, the + permutation and leave-one-out diagnostics (they use only the point estimates), and every + estimator without the knob. On the surfaces where the knob is a documented no-op — + non-`pretrends` `ImputationDiD` fits and `WooldridgeDiD` logit/poisson fits — passing + `df_convention="residual"` emits the inert-config `UserWarning` (there is nothing to + restore there). - **`SpilloverDiD` auto-clusters at `unit`** when `cluster=` is omitted — it is the one estimator whose default standard errors move. Pass **`cluster=False`** for the 3.x observation-level HC numbers; `None` and omission both mean "auto-cluster", so diff --git a/docs/tutorials/24_staggered_vs_collapsed_power.ipynb b/docs/tutorials/24_staggered_vs_collapsed_power.ipynb index e1479b45c..7dcef4424 100644 --- a/docs/tutorials/24_staggered_vs_collapsed_power.ipynb +++ b/docs/tutorials/24_staggered_vs_collapsed_power.ipynb @@ -37,10 +37,10 @@ "id": "0c4f03f8", "metadata": { "execution": { - "iopub.execute_input": "2026-08-10T22:40:44.355443Z", - "iopub.status.busy": "2026-08-10T22:40:44.355143Z", - "iopub.status.idle": "2026-08-10T22:40:45.083622Z", - "shell.execute_reply": "2026-08-10T22:40:45.083227Z" + "iopub.execute_input": "2026-10-28T15:24:55.749044Z", + "iopub.status.busy": "2026-10-28T15:24:55.748981Z", + "iopub.status.idle": "2026-10-28T15:24:56.479188Z", + "shell.execute_reply": "2026-10-28T15:24:56.478728Z" } }, "outputs": [], @@ -134,10 +134,10 @@ "id": "08de7530", "metadata": { "execution": { - "iopub.execute_input": "2026-08-10T22:40:45.084992Z", - "iopub.status.busy": "2026-08-10T22:40:45.084888Z", - "iopub.status.idle": "2026-08-10T22:40:45.170232Z", - "shell.execute_reply": "2026-08-10T22:40:45.169803Z" + "iopub.execute_input": "2026-10-28T15:24:56.480611Z", + "iopub.status.busy": "2026-10-28T15:24:56.480509Z", + "iopub.status.idle": "2026-10-28T15:24:56.565409Z", + "shell.execute_reply": "2026-10-28T15:24:56.564957Z" } }, "outputs": [ @@ -228,7 +228,7 @@ }, { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -296,10 +296,10 @@ "id": "8496ad42", "metadata": { "execution": { - "iopub.execute_input": "2026-08-10T22:40:45.171194Z", - "iopub.status.busy": "2026-08-10T22:40:45.171128Z", - "iopub.status.idle": "2026-08-10T22:40:45.182869Z", - "shell.execute_reply": "2026-08-10T22:40:45.182539Z" + "iopub.execute_input": "2026-10-28T15:24:56.566499Z", + "iopub.status.busy": "2026-10-28T15:24:56.566436Z", + "iopub.status.idle": "2026-10-28T15:24:56.577928Z", + "shell.execute_reply": "2026-10-28T15:24:56.577538Z" } }, "outputs": [ @@ -310,7 +310,7 @@ "True effect-on-treated (E1): 2.61\n", "Collapsed-2x2 target (E2): 2.10 <- diluted by not-yet-live states\n", "\n", - "Collapsed 2x2 estimate : 2.17 95% CI [1.88, 2.46]\n", + "Collapsed 2x2 estimate : 2.17 95% CI [1.87, 2.46]\n", "Callaway-Sant'Anna : 2.67 95% CI [2.26, 3.09]\n", "\n", "The 2x2 reports ~81% of CS's number. That gap is not noise — both\n", @@ -337,16 +337,16 @@ "id": "77f02fec", "metadata": { "execution": { - "iopub.execute_input": "2026-08-10T22:40:45.183908Z", - "iopub.status.busy": "2026-08-10T22:40:45.183844Z", - "iopub.status.idle": "2026-08-10T22:40:45.242964Z", - "shell.execute_reply": "2026-08-10T22:40:45.242565Z" + "iopub.execute_input": "2026-10-28T15:24:56.579044Z", + "iopub.status.busy": "2026-10-28T15:24:56.578970Z", + "iopub.status.idle": "2026-10-28T15:24:56.639103Z", + "shell.execute_reply": "2026-10-28T15:24:56.638684Z" } }, "outputs": [ { "data": { - "image/png": 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", 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t0Gpr8ZnoE2EOPgv7gCZ8U7WD1pw7S7dl7X4VBaNz4Jwbdg5Ezefp06dVTa5xk74t164t57Ek1725NFpzzMxtA8uvXbtW6DpHbDiuG8PltlxPxR0XW44/vsN4oFYZ9yPD0aWKOzdFKe5vHXWfM+xvgWOM44v+LTju6GQbFRWl/z5Ze89ACBC2gbQg1Af3SewX9sl4f4s6zpbulyX7UBRL7yHF3XuL+36U5D5ryfeTvAcLAETkdMYZNiJeT0REpYd9AIiIiIiIvAgLAEREREREXoQhQETkdIxNJV5PRESOwwIAEREREZEXYQgQEREREZEXsXy8Ow+BYcIw1BemCjc1BBcRERERkbvBEMEY7rd69epqCNiieF0BAJl/S8boJiIiIiJyN3FxcWqOkKJ4XQEANf/awQkLC3N2cohcEibAmT17towYMUJNbENERESuDRO9oZJby+sWxesKAFrYDzL/LAAQmZaamqpm38RNhN8TIiIi92FJiDs7ARMREREReREWAIiIiIiIvAgLAEREREREXoQFACIiIiIiL8ICABERERGRF2EBgIiIiIjIi7AAQERERETkRVgAICIiIiLyIiwAEBERERF5ERYAiIiIiIi8CAsARERERERehAUAIiIiIiIvwgIAEREREZEXYQGAiIiIiMiL+Ds7AURERETknRKSMyQhJdPs+xGhQRIRFuzQNHkDFgCIiIiIyCnmb42V6auOmX1/fM9omdC7oUPT5A1YACAiIiIipxjSobb0jqkqGdm5MnDmZrVs8dhOEhzgp28BIPtjAYCIiIiInALhPXikZeXol8VUD5OygcyiliZ2AiYiIiIi8iIsABAREREReREWAIiIiIiIvAgLAEREREREXoQFACIiIiIiL8ICABERERGRF2EBgIiIiIjIi7AAQERERETkRVgAICIiIiLyIiwAEBERERF5ERYAiIiIiIi8CAsARERERERehAUAIiIiIiIvwgIAEREREZEXYQGAiIiIiMiLsABARERERORFWAAgIiIiIvIiLAAQEREREXkRFgCIiIiIiLwICwBERERERF6EBQAiIiIiIi/CAgARERERkRfxd3YCiIiIyL0kJGdIQkqm2fcjQoMkIizYoWkiIsuxAEBERERWmb81VqavOmb2/fE9o2VC74Y8quTxEty0MMwCABEREVllSIfa0jumqmRk58rAmZvVssVjO0lwgJ8+00PkDea7aWGYBQAiIiKyCmo08UjLytEvi6keJmUDma0g7zLETQvD/KYSEREREXlRYZijABEREREReREWAIiIiIiIvIhrt08QEZFdueuIFUREZD8sABAReRF3HbHCEAsxxOuIqGRYACAi8iLuOmKFpxViyPl4HZE3YwGAiMiLuOuIFZ5WiCHn84TriK1hZCv3ueMTERF5SCGGnM8TriO2YpCt3OcqJyIiIiKPasUg53C7AsCSJUtk+fLlcv36dWnSpImMGjVKKlWq5OxkERERETmUJ7RikHO41TwAw4cPlwULFkjLli2le/fu8ttvv0mrVq0kISHB2UkjIiIiIhvl5un0z7edulLgNdmfWxURp02bJpUrV9a/fuCBByQ8PFyWLVsmQ4cOdWraiIiIiMh6y/bHy+tLDuhfD5+9XSLDg+X1/jHSr2kkD6m3twAYZv7hxIkTkpOTI/Xr13damoiIiIjI9sz/uHm75GJywQkKL1zLUMvxPnl5CwDs3r1bJk+eLMnJyXLo0CGZO3eudO3a1ez6mZmZ6qHB3xERERGRcyHMZ/LSg2Iq2AfLfETU+71jqomfL16RV7YAQI0aNVRfgMGDB0tUVJRMnTq1yD4A77zzjgoT0h61atVyaHqJiIiIqDDE+sdfyzB7aFAIwPtYj7y8BSAiIkLuuece9fyRRx6R6Oho+fDDD+Xtt982uf5LL70kzz77bIEWABYCiIjImTiBE5FIQkqGXdcjDy4AGAoKCpI6depIXFxckevgQURE5Co4gRORiJ+PZWE9EaHBPFzeWgDIyMiQH3/8UYYMGaJftmXLFtm+fbsMGzbMqWkjIiKyBidwIm939GKK/Pf3g8WuF+TvKw2rlnNImryJ2xQAAgICZM2aNfLyyy9Lw4YNJSkpSQ4cOCBPP/20jBw50tnJIyIishgncCJvtvPMFRk5Z4dcS8+WamHBciE5Q3X4NdUZODMnT+79bJN8/khbaVQt1Amp9UxuUwDw8/OTL7/8Ui5duiR79+6VkJAQady4sZQvX97ZSSMiIiIiC/x18KI8sWCXyti3ql1evh7WTraeSlTzABgOBYp5AEZ2qSffbD4tZxLTZMCnG+WD+1tKv6bVeJy9qQCgqVKlivTs2dPZySAiIiIiKyzcHicv/bxPDf/Zo3GEfDK4tZQJ9FOTfXWJqizN3lih1pszop3cHF1FDf15X5ua8uSCXbLpRKKMnbdTxveMVg9fDgvqXcOAEhEREZH70Ol08sma4/L8j3tV5n9gm5oy65E2KvOvMRznv329ivrXFUMC5duR7WVEl7rq9fRVx2TMvJ1yPTPHCXviOVgAICIiIqJSkXdjsq/3lh9Rrx/v3kDeG9hcAvwsz4L6+/nK6/2bqL8L9PeVlQcvyoBPNsrpy6k8azZiAYCIiIiI7C4zJ1ee+n63zNl0Wr1+7c4Yeb5fY/GxcPhPY4Pa1pKFYzpJ1bAgOZZwXe6asUHWHr1k51R7BxYAiIiIiMiuUjKyZcTs7fL73ngJ8PORjx5qJSO71ivxdlvWKi9Ln+wqrWuXl+SMHBkxe5vMWntChRmR5VgAICIiIiK7wcy9D36+RXXcDQn0k9nD28tdLarbdRjd7x7rKA+0rSV5OpF3/jws47//R9Kzcu32GZ6OBQAiIiIisgvE5Q/8bLMcOJ8slcsFyvePdZKu0ZXtfnSD/P1kyn3N5K27m4i/r48s2XNeBs7cJOeS0u3+WZ6IBQAiIiIiKrH9566pTHjslTSpXbGsLB7bWZrVDC+1I4u+BI90qivzHu2gRgtCoeOujzfI1pOJpfaZnoIFACIiIiIqkQ3HLssDszbL5etZEhMZJovHdZK6lUMcclQ71q8kS57sIk2qh0liapYM+XKrzN18mv0CisACABERERHZDOE3I+Zsk9SsXOncoJL8MKajRIQGO/SI1qyQ3+LQv0V1ycnTyau/HpCXftqnRiKiwlgAICIiIiKbzN54Sp7+brdk5+rkjuaRMntEOwkNDnDK0cTEYh892FJevA1DjYp8vz1OBn+xVRKSM5ySHlfGAgARERERWQXDbr677LCa5AuGdaojHz/YSnXOdSb0Cxh7SwOZPRwFEX/Zeeaq9J+xQf6JS3JqulwNCwBEREREZLGc3Dx5fvFe+ezvE+r1pL6N5I27moivr20TfJWG7o0iZMmTXSUqopxcTM6U+2dtlsU7zzo7WS6DBQAiIiIisgjG2h8zd6cs2nlWkN9/975m8sStUTbP7lua6lUOkZ8f7yy9bqoqWTl5MnHRHnlz6UFVgPF2LAAQERERUbGuqhF2tsiqwwkS5O8rsx5pKw+0q+3SRw79ET5/pI083TNavf564ykZ+vU2tS/ezN/ZCSAi74BOWAkpmWbfjwgNUrM7EhGR6zmflK4yzscTrkt4mQD5alhbaVu3orgDhCY927uhxESGyrML96gZiu/6ZIN8/khbuSkyTLwRCwBE5BDzt8bK9FXHzL4/vme0TOjdkGeDiMjFHL2YIkO/2iYXkjMkMjxYvhnZXhpWDRV3069ppNSrXE5Gf7tDTVZ276eb5P37W8jtzSLF27AAQEQOMaRDbekdU1UysnNl4MzNatnisZ0kOMBP3wJARORouXk6/fNtp67IzdFVxM+FOrM6247TV2TknO2SnJGjOtR+O7K9VC9fRtxVo2qhatKwp77bLeuPXZbH5++SJ2+NUi0ErtSJubSxDwAROQTCe5rWCJeY6v82t+I5luHB8B8icrRl++Ol1wdr9a+Hz94uXd9drZaTyMqDF9Wsusj8t65dXlXauHPmX1O+bKAaJnT0zfXU6xlrjqtWgeSMbPEWLAAQERGR10Emf9y8XWqISEMXrmWo5d5eCPhhe6yMmbtDMnPypGfjCJn/aEeVcfYU/n6+8vIdMfK/B1pIoL+v6th8zycb5cSl6+INWAAgIiIirwv7wQRW/wb//EtbhvcNw4O8aYKvGauPyQs/7hPs/qA2NWXWI23ULLueaECrmqplA30bTl5KlXtmbJQ1hxPE07EAQERERF4DY8Av3hkn8dcyzK6DbD/eR58Ab4ICzxtLDsi0FUfV68e7N5CpA5ur2nJP1rxmeTVpWNs6FSQlM0dGfrNdPllzXBWGPBU7ARMRkVti500qTnZunhy7eF32n7sm+248DsUnq7AWS2w8fkna16voFZ2CM3Ny5dkf9sjv++IFc3q9dmeMjOiSHyPvDaqEBsmC0R3ljaUHZMHWWHlv+RE5GJ8s7w1sLmUDPS+77Hl7REREHg/x2a8vOVCg8yaa8F/vH6OG+iPvg5leMVylltnH/4cupKjlxoIDfCUju/hCwIw1J+SHHWfljmaR0r9FddUR1hVnvC2plIxsNbsvxscP8PORD+5vqfbX2wT6+8rbA5pJk+ph8vqvB+T3vfEqLAgTidWqWFY8CQsAREReyJ1rz7XOm8aN81rnzc8ebs1CgBfUVh+9cF1fq3/g/DU5HJ8iWbmFM/WhQf7SpEaYNLsx4hgetSuUlW7vrVHXjLkgj7KBfuLv6yOXUjJlzqbT6lGjfBmVMe7fIlJiIsM8ojCQkJIhw7/ermq7QwL91Oy+XaMrizcb0qGOmudg3LydqsXorhkb5JMhraVzA885LiwAEBF5GXeuPUf8NmKUzXXe9LnRebN3TDW3KdBQ0TB3yJELKfqMPv7H6+zcwldBWLC/yuAbZvbrVCxrcnx3XO8oMOIdwy1pa35wfwvp0biqrD92SZbuOS8rDl6Uc0npMnPtCfVoUCXkRmGgujSoUs4tT+Ppy6nyyNdbJe5KulQuFyhzRrRXx4xE2tWtqPoFoGUE19wjX22TV+64SYZ3rusRBT8WAIiIvEhp1p6jVQGZtXQ8snL1z9Oy8v/PuPG/qffVc/37eYXWNfy/KFrnzS/Xn1QZMxRsPOHH2lvgOkCN6/7zybL/bH5mH2E9OSZG4wkvE6DP6Of/Hya1K5a1+HzjOsf1jsKw4VCg1YwKwz1vqqoeuP7WHEmQJf+cl9VHEuTEpVT58K9j6oHWgLtaVpc7m0dKzQruESqy7+w1GT57mySmZqnjNndUe6lTKcTZyXIp1cuXkUVjO8lLP+2Tn3efU5ULB84ny3/vaaqfxNJdsQBAROQlLBn68IUf98rpxFTJzNblZ9pvZLzTjDLtBZ7fyMSbirV2lnf+PKweCOOoXyVEoqqUU7W0DSLy/69buawE+bv3D7i7h5Lh+kHYiYrZv5HZP5Zw3eTQmxXKBugz+lqmv2aFMiUu3CGT3yWqsjR7Y4V6PWdEO7P7gGEwb28WqR6ImcckWWgZwGyy2A88pvx5WPUTQOET/QZcdYJDtGqMnbtTUrNyVbw7av7RCZYKQ0YfrUE4Tm//cUgW7zwrxxOuq6FRq7ro+bUECwBERF4CGbSihj6Ea+k5MuXPIyX+LHSyLBPgl/8IvPEI8FM/pvplN14jk64tM/v+jdcIARk7b1exn189PFgSUjJVwWT/OWQykwu8j/wdaj21QoEqIESEqNeeNNmRq4SSpWXlqJr9/Ix+fqb/+CXTmf1KIYFGYTxhKva+tFpyDDP7lo74ExocIPe2rqkeV1KzZNn+C6owsOVUouyKTVKPt347KB3rV1KFgX5NqkmFENe4rpbsOS/PLfxHhVB1iaokMx9uo/aHzMO19+jN9aVRtVB5csFu+ScuSe78eIM6dm3qVBB3xAIAEZGXQGc/S7SrU0Giq4UWzMAbZcr1y4wy+HgE+fuajLm2V5M8MpnmOm/63AjhWP9CD8nT6ST2SpqcSLiuMpsnElLVLJ94jbG+TyemqQdmADXOgGotBYjzjrrxHJnQ0tovTwsle65PQzV0ojYiD467qTm1KpcLkmZGHXTdLWyrYkigDO5QWz0uJmeokWOW7j0vu2OT1Kg6eLz6y37p1rCK6jyM/inlgpyT/fp6wyl587eD6vkdzSNVzTZbwiyH1qElT3aR0d/ukKMXr8tDn2+Rt+5pIg+0q+12AyuwAEBE5CUiQi1rrn62TyPp1KCSuCL8oBbXeRPvYz0/8bmRiS8nfQzWw+Q+GNlFFQoupaoCgVYwOH8tQ8VEJ566UmgSKLRq1Ktc7kaBIL+1AM/rVQ6xOR7YnTINOG4YOjMpLUte+WV/kaFk2kRShiJCg4xi9sOlaliQW2X2i4OQkJFd66lH3JU0+W1vvKpxR+vH6sMJ6hHkv096NI6Qu1pUl1sbRzgklhznburyI/LZ3yfUa3RkxTj/LNBaD/0kfnq8i2pFWX7gopox+c99F+TQhWS3GliBBQAiIi9x4Vp6ke9rtecIg3BllnbeNAcZTsRm42E8rF9qZo4a91sVCG48EO97+nKayvwiI4dHwe2JikdXBQKDfgYoHKB22FVGY0LMfUpGjopfT77xf4rB/1iWnF5wWUrmjfduLDfVGdec1rUryC0Nq6gQHmT4XTUevrRg3Phx3Ruox/GEFFm6J16FCZ28nCp/7r+gHhh2s0+TaqploGtUFTUOfWlMhoZOrIhdh0l9G6kZfj2p4OVo5YL85bMhbWTGmuPywcqj8vfRS4XWcfVhiVkAICLyAgu3x8kLP+3Vvy6u9tzVWdN50xohQf7SrGa4ehgPP3r2arq+QJBfOEhVz6+lZ6thFPH4+8ilQp1XtVYI1XJwo5/BgXPJ8sQCy0djQgdrUxn3/NfFZ9zxMDVGvi2Mrx1zhnWuI3e3rGGXz3R3URGhMqF3qDzTK1qNIoMQod/2xKthRTG6DB7lywbIbU2rSf/m1aVD/Up2+R6isz6uM7Q8YHvvDGgm97erZZd98na+vj7yxK1RMnvjKbmalu12wxKzAEBEZKGE5AzVsdQchDi4Yi3nN5tO62uah3SoLV0aVJbJv9lWe+5KbOm8aSt/P1+pWzlEPTAkpGFoBUKG8sOI8gsEWssBMnfIGOw4c1U9LKFlrJ/6brfULH9YUjJRa58tmXYaYQmVvqi9DAsOkNBg/xuP/Of/Lvv3PSwLK2O4LED2nU2Sh77YareQM2+CWnetr8MLfRvL7rgk1SqAUKHL1zPlu21x6oEReUo6+/DV1CwZ+c121RcB/XI+GdxaesX8e+1SySFsz1Tm33hYYqznamGVLAAQEVlo/tZYmb7qmNn3x/eMlgm9G7rU8Zy19oQaDhNGda2nJrJBZuLmhvavPfdGOJboyIoHam2Na19PXi7YzwAFBDyKC6XBCC2nEtMKLUfmvbiMe9iN58YZd/xfLtC/xHHf7etVsqgjtquHkjkbzgNGkMHj1TtjZOvJRNUy8Me+C4VmH76zRaTqM2Bu9mHjviT1q5STEbO3qWsP8yV8PbyttKnD8+GsgRUSLFzPkVgAICKyEGrPe8dUVbHUA2duVssWj+2k78SHFgBXgZppFFYwSRE8eWuUGplFyzw4svbcW2FkpCbVw9XDEMI9JvzwT7F//3SPKNUio2XyywX7u8R5sqYjNll+TDtHVVaPyXc1lQ3HMftwvKw4cEG1JM1ae1I9MKcFQoTQMoCQMnN9SXDoUSZAQe3bke0lumooT0UpiLCwlcsVW8NYACAispDWcRRjmmtiqoep4Q5dCTL/U5YdVhkGrdMfYlXJNVSzMEysU4PK6vryxI7YZB46AvdoXFU9UNmA+H2ECWG4WnRQR8EeD7QGNKwaKr/8c67QNrQGgad6RDHzX4ra16votq1hrvWrRUREJZKnZvs9IN9sPqNeI7QAoT/kOtw50+CIjtj0L7QuGs4+/NchzD4cL+uOXtLPPmwOzsLHq4+rMepd+ZxofatQ2NEcPJ9coGXVFftWuXtrGAsAREQeAnHA//lpn/ywI069/r8BTWVIhzrOThZ5UKbBGEPJHAf9OAa0qqke6OD76d/H5Yv1p9yyA2pxfau0EEtX7VvlCa1hLAAQEXkAjPU9cdEe+fWf8yr+972BLeS+NjWdnSzysEwDuYYKIYFqJCF37YBqqm+VOa7Ut8qTWsNYACAicnMYI/6p73apWSn9fX1k+oOt5I7mzEC6OnfMNJDrcOcOqKb6Vrk7PzcbWMH+U84REZHDIG52zNwdKvMf6OcrMx9uw8y/G3G3TAO5Xl8Sc1cMlke6QV8Scg4WAIiI3FRqZo6MnLNd1hy5JMEBvvLV8Lac6IfIy/qSgHEhwN36kpDjsQBAROSGkjOyZdjX22TTiUQJCfSTb0a0V+EjROR9fUkiwgrGyaMvCZazLwmZwz4ARERuJiktS4Z+vU32nr2mZn39ZmR7aVW7grOTRUROwL4kZAsWAIiI3Mjl65ny8Jdb5fCFFKkYEqhm+bR0NBAi8kzsS0LWYgGAiMhNYOKowV9uUbOBVgkNkvmPdlAzgRIREVmDBQAiIjcQdyVNhny5VWKvpEn18GCZP7qj1Ksc4uxkERGRN3UCzs7Olo0bN8rcuXP1yxITE+2VLiIiuuHU5VR5YNZmlfmvXbGs/DCmEzP/RETk2AJAbGystG7dWnr27ClDhw7VLx89erQsXbrU9tQQEVEBRy+myP2zNsv5axnSoEqILBzTSWpVLMujREREji0ATJgwQTp16iTJyckFlk+cOFGmTJlie2qIiEhv/7lrqub/UkqmNK4Wqmr+MbwfERGRw/sArF27Vg4fPiyBgYEFljdr1kx27txZogQREZHIrtirapz/lIwcaV4zXI32U75swXsuERGRwwoAGRkZ4uub33jg4/PvDHPx8fFStiybpomISmLLyUQZNWe7pGblSts6FeTrEe0kLDiAB5WIiJwXAtS9e3eZOXNmgQJAamqqTJo0SXr16mWflBEReaF1Ry/J8NnbVOa/c4NK8u2o9sz8ExGR81sApk2bJt26dZM//vhDdDqdDBo0SNavX6/ew8hARETm5Obp9M+3nboiN0dXKTCJjTdbefCiPDF/l2Tl5smtjarIZw+3keAAP2cni4iIPIxNLQCNGzeW/fv3S9++faV///4qJGjMmDGyZ88eadCggf1TSUQeYdn+eOn1wVr96+Gzt0vXd1er5d7ut73nZdy8nSrz369JNZn1SFtm/omIyLUmAouIiJBXX33VvqkhIo+FTP64ebvk3/r/f2e3xfLPHm4t/ZpGijdavPOsPL94j6Bx5O6W1eX9QS3E38/maVqIiIiKxF8YInJI2M/kpQcLZf5BW4b3DcODvMW8LWdk4qL8zP+D7WrJB/e3ZOafiIhcrwXg7NmzqsPvhg0b5OrVq4Xev379uj3SRkQeArH+8dcyzL6PbD/ex3qdGlQSb/HVhlPy1m8H1fPhnevKa3fGiC/7QxA5REJyhiSkZEpGdq5+2cHzyfrQu4jQIIkI47wb5JlsKgBg9t+cnBx56623pHz58uKWslJFskx0rvPxEwkILrieOT6+IgFlbFw3zaDus9DKIoFlbVs3O11El2c+HYEhNq6bIaLLtc+6AWUxfFT+85xMkbwc+6zrX0bkxvC0kpMlkpdtp3WDRXz9rF83N1skN8v8un5BIn7+NqybI5KbWcS6gSJ+Adavm5crkpPx77Wh/a9d174BIv6Bhdc1pcC6eZJw9ZpYYtryw/L4zTXl5gYVJdDfRAOlr7+If1D+c51OJDutiDRYsa5V33u/grdOta6/1feIGWvPyLRVp9TzMV1ryYt96omPYea/tO8RWTlSRjIK74M73SOycm7stxvfI7LSTJ8HV79HaAyvI5U+f5vuEZKTbqd1Lf/ef7/5lHyw5syNVzopI5nyyMw1+vcf7x4lT/WIsu0e4eh8RKHvcwDzEY7OR2Slm/8uOyofUdT1Y8RHh2F8rISx/s+cOSNVqlQRd4PZi8PDw+Xai6ESFmRi5JHoPiJDFv37+v8izd9A6nQVGfH7v6+n1hdJSzS9bvVWIo/9/e/r/zUTuRZret0qjUWe2Prv6086iFw6bHrd8NoiE/b9+/rz7iLnd5tet2wlkedP/vt69h0iZzaY/8K8bNAxc/4gkWMrxKw3DDJ4C4eKHPzV/Lr/Of9vZuDncSJ7Fphfd9IJkZDK+c9/f05k+5fm1x2/V6RCnfznK14R2fSx+XUf3yIScVP+8zXviKwtYgbr0atFarTJf75xusjK18yvO+w3kXo35z/f9oXIHxPNrzt4oUjDvvnPd88X+fVx8+sOmiPSZED+8wM/iywabn7duz8VaTUk//nR5SIL7je/7u3TRNqPzn9+ar3IN3eqp/ESIZ/7PCyP6eZJpCTkv9/7TZEu4/Ofn9sp8kUP89u95UWRW1/Kf55wSDZ/PEIeyra8z1B5SZHb/LZLf99N0sH3kPj53LhNtXtU5I7385+nXhZ5r4hBB1oMFhnwWf5z3BTfrm5+3Zi7Re7/9t/Xb4SbXze6j6QN+k5iXluuXp4KfVR8rLhH6FITZVrO/fJJ7j1q0QT/RfK038/iU4P3CFvuEa0zZsoVCZODb/aVsitf4D3CQfcIU7J6vCGB3SbYdI+QTzuaX7fzUyJ9/pv//OoZkenNza9rxT0iPeYBOdHlPfUc3+Emcxrb7R7BfATzEc7IRyQvHCfhU1Lk2rVrEhYWZv8WgMjISElPL6IETkRkoL3vYYmURImXiv/W1hrAkorlAuWOhuXkz10n5JJUkO9ye6hHVbkid/ptkbv8Nklznam/dh+obnkr52H5Ovd29fol/wUyxv83ZyeLyCuVCfCTpjVuZOSzbB4Thcgt2dQC8Pnnn8tPP/0kM2bMUMN+Gs4G7DYtAJfOmy4dMQTIdZr3rV3XFZv33TQEKP7CRfl89lx5bMQjElmtaolDgNBkv+zgJRn7/YFCq2p3DzUKUExVyc1Kky2nkmTJvovy58HLkpzx7zmvW6mM9G9RQ+5qUV2iI8o5LQQoTeevbwE4+EpXKRtYfAhQXp5OXvlptyzYkd+y9uYd0TK0Qw2T6zoiBCgtK0fa/PcvtWjnK73+3Qc3CgHCPsT8F3PQ+OS3APjmut09Ii09zfR5cPF7hKbAdfT6bVK2TFm3CQEq1TBBB4cAFf4+MwTI0fmItIx0899lB+Ujkq9elvAq1UuvBaBNmzby4osvSnR0tMn3bShTOB5+jAx/kIpaz5ptWrxu2dJZ1/DmYNd1g0tnXXXzDSqFdfFjEejcdfGjqf1w2nVd/3+/8PZcFzcc7RrWrg38b+q6Nly32O36qnUrlzedyagWHiyv94/RDwHqF1xOutyER015MydX1h29LEv2nJe/Dl6U04np8vHq4+rRuFqo3NWyuvRvXl1qVSzmO4Kbvz2/yyr23GBdcwWAG3Jy8+T5H/fKT7viVVLevbe53N+ulpPvETmSLsFF74PL3yNwHnzc+x4RGFL8eXDFe4SewXWEwkKR6xZ9j7D7utZ87+19j3DIuobf+2K+z8xHlP49ItDXsu9yaeYjrLh+bCoAjBw5Utq1ayejR492307ARORQ7684qv6/r3V1+XHXefV8zoh2Rc4EHOTvJ71jqqpHamaO/HXooizdc17WHr0khy+kyOFlR2TqsiPSpk4F1Spwe7NIqRJq4c3dQbJz8+SZ7/+R3/fFq/384P4WcndLg5p/IiIiB7OpAHD06FFZs2aNVKyIeF4ioqJtOn5ZNp9MlEA/XxnXPUpfAGhfr6LZzL+xkCB/lXHGIyktS/7cf0GW/HNetpxKlJ1nrqrH5KUHpEtUZenforr0bVJNwstYWHNSSjC84JMLdslfhxIkwM9HPn4Ik51Vc2qaiIiIbCoA1KtXT8UXsQBARMVBSOC0FUfU84fa15Lq5a0IKzGjfNlAeah9bfW4mJwhv+2NV2FCe+KSZP2xy+rxys/7pXujKipMqGfjqlIm0MSwv6UoPStXHpu7Q6UlyN9XZj7SRm5tFOHQNBAREdmtADBu3DgVBvTRRx9JVFRUoU7AwcGcOIOI8v195JLsik1SmeAnbr0xprYdVQ0LllFd66nHmcRUFSKEwsDRi9dlxcGL6hESmB9KhMIAQo4C/Ep3EvTrmTkycs52NbFZ2UA/+XJoW+kcdWM4WyIiIncsADz99NPq/+bNm7tvJ2AiKnW4F7y/Mr/2f1jnumpWTYxWUVrqVAqRJ3tEq8fhC8kqRAiFgbNX0+WXf86rR/myAaqvAPoMtK9b0e4z715Ly5Zhs7fJP3FJEhrkL3NGtpM2dRguaU+cwZWIyAkFgPXrMewaEVHRlh+4KPvPJasa+DHd6jv0cDWuFiaN+4XJpL6NZHdckioMIFTo8vVMWbA1Vj2qhQXLnc0jVctAsxrhJR7SOPF6pjzy1TY5GJ+sChpzR3aQZjWLmDCIbDJ/a6xMX3WswLKBMzfrn4/vGS0Tejfk0SUismcBoGvXruIMa9eulWnTpsmmTZvE399fpWPKlClmhyMlIufJzdPJBzdq/0d0qSeVyjlndB5k6lvXrqAer94ZI1tOJsqv/5xTnYgvJGfIlxtOqUfdSmVVqwAKA1ERoTbVSg/5cqscS7gulcsFyrxHO6hCCNnfkA61VUiXOREuNhIUEZGrKdHUd4mJiWpEIDTzN2rUSCpVqiSlJTc3V15//XWZNGmSfPPNN5KWliZPPvmk9OrVSw4cOCDlypUrtc8mIuv9tjc/Dj802F9G3+zY2n9zMOIQRgnC4617msraI5fy5xg4hDkG0uSj1cfV46bIMFUY6N8iUmpWKGuycKNBnH/9KuVk6Fdb1TbQqjB/dAdpUIX3pNKCUDI8iIjIgQWA9PR0eeaZZ+Srr75SGXPw8/OTUaNGyYcffihlypR8lA9j2P7ff/+tf40RiNAJuU6dOrJlyxZVECAi14CJrz78Kz9E47Gb60t4WecOx2lujoE+TaqphzbHAMKEMMfAofhk9Xh32WFpizkGWubPMVC5XJAs2x8vry/5d0bj4bO3C7oRoExQs0IZWfBoR6ldyYqJucgrsR8DEbldAQC18KtXr5bFixdLx44dVRP75s2bZeLEieq9GTNmiCNcunRJ/R8ezhhbIlfy0+5zcupyqlQoGyAjutYTV1fUHAM7zlxVj8lLD0rDquXkUHxKob/XGgQe797A5TP/zHi6BvZjICK3KwD88MMPsmLFCmnVqpV+2T333KNq4/v27euQAkBOTo48++yzKg1t2rQxu15mZqZ6aJKTk0s9bUTeLCsnT6bfqP0f172BlAsqUaShwxnOMXDhGuYYOK+GFt1z9prJzL+hj1cflwfa1bZ4cjNnYMbTNbAfAxE5k02/zCkpKSqzbwzLHJHBRp+DRx99VI4cOSIbNmwQX1/zY3q/8847Mnny5FJPExHl+2FHnJxLSpcqoUHySMe6bn1YqoUHy6M311ePn3edlQkL9xS5fvy1DNUnoFOD0usPVVLMeLoG9mMgImeyaTac1q1by9SpUwuM94/nGJGnqNp4e8DnPPbYY/Lnn3+qMCRMRFaUl156Sc1arD3i4uJKNX1E3iwjO1dmrM6v/X/y1iiHz75bmiydLyAhJUNcPePZtEa42Qc71xIReT6bWgAwFGe/fv3kxx9/lHbt2qll27dvl4sXL8qyZcukNDP/Y8eOlSVLlqjMf0xMTLF/ExQUpB5EVPrmbTkjF5MzpXp4sDzYvpZHHfKI0GC7rkdERORWLQCdO3eWY8eOyZAhQyQ7O1vF4+M5luG90vLEE0/IL7/8ojL/TZo0KbXPISLrYSSdmWtPqOdP94xWo+x4kvb1KkpkeLCYawfAcryP9YiIiFyZzb3zqlatKm+88YY4yuXLl+Wzzz5Tz5s2bVrgvS+++EL1CSAi5/lm82m5fD1L6lQqK/e1qelxpwIde1/vHyPj5u1SmX2dUeYf8L4rdwAmIiKyuQUA8wD88ccfhZZjGd4rDZUrV1YhQKYezPwTOVdyRrbMWntSPR/fM1oC/Gy6tbi8fk0j5bOHW0tEWFChzsJYjveJiIhcnU2/0uhYixmAjWHZK6+8Yo90EZEb+Wr9KbmWni0NqoSosfQ9GTL5fz17i/71nBHtZMMLPZj5JyIizy4ALFiwQB5++OFCy9EP4LvvvrNHuojITVxNzZKvNpxSz5/t3cgrQmAM9xEx/96wz0RE5OUFAEyslZaWVmh5amqqXL9+3R7pIiI3MWvdSbmemSM3RYbJbU2rOTs5REREVBoFgFtuuUWF+mAEIA2ev/zyy9KtWzdbNklEbghj3s/ZlF/7/1zvhhaPlU9ERERuNgoQJgHr2rWrREdHS8eOHVVH3C1btqhWgXXr1tk/lUTkkj77+4RkZOdJi1rlpedNEc5ODhE5SEJyhiSkZKrJ/zQHzydLcED+8L8RoUGcVI7I0woAjRs3ln379snMmTNl165d4uPjI8OHD1eTdEVGchQMIm8Qfy1d5m+JVc8n9mmo7gNE5B3mb42V6avyZ/3WDJy5Wf8co4FN6N3QCSkjolIrADz44IPy/fffy+TJk82+R0Sebcbq45KVm6c6wXaNquzs5BCRAw3pUFt6x1Q1+z5aAIjIwwoAP/zwg8lMPkKBFi5cyAIAkYeLu5ImP2yP08f+s/afyLtEhAUzxIfIWwoASUlJJp9DXl6ebNy4kSFARF4ATf85eTq5ObqydKhfydnJISIicooEN+0PY1UBoEKFCiafa3x9feXdd9+1T8qIyCWduHRdftp1Vj1/ljG+RERO466ZT08y3037w1hVANi+fbv6v127dvrnmoCAAKlVq5ZUrFjRvikkIpfy4V/HJE8n0uumCGlVu3BFABEROYa7Zj49yRA37Q9jVQGgbdu26v9Tp05J3bp1SytNROSiDsUny9I959Vz/qgQETmXu2Y+PUmEm/aHsakTsL+/v5w9mx8CYErNmjVLkiYiclH/W3lU/X9Hs0hpUj3c2ckhIvJq7pr5JDctACDUpygYDYiIPMves0my4uBFwWS/E3pHOzs5RERE5MgCwKFDhwqNAHTs2DF58cUXZfz48bamhYhc2Psr8mv/72lZQ6IiQp2dHCIiInL0TMDGYmJiVMvAuHHj1IzAROQ5dpy+ImuPXhI/Xx8Z34u1/0RERO7M154ba9iwoRw8eNCemyQiF6r9v79tTalTKcTZySEiIiJXKABkZmbK+++/zw7ARB5m0/HLsvlkogT6+cqTPVj7T0RE5JUhQOXKlSu0LC0tTUJDQ2XBggX2SBcRuQB06J+24oh6/lD7WlKjfBlnJ4mIiIicUQCYN29eoWWYGbhFixZSvnz5kqaJiFzE5tPJsis2SYL8feWJW6OcnRwiIiJyVgHgnnvuscdnE5ELw2i+szbnT/o1rHPdEo81zSnriYiI3LgAoElMTJSjR4+qMIFGjRpJpUqV7JcyInKqM3nl5eildAkJ9JMx3eqXeHucsp6IiMiNCwDp6enyzDPPyFdffSW5ublqmZ+fn4waNUo+/PBDKVOGccJE7iw3Tye7s2uo5yO71pNK5Uo+nTynrCciInLjAsCkSZNk9erVsnjxYunYsaP4+PjI5s2bZeLEieq9GTNm2D+lROQwfx29Kkm6MhIa5CeP3lzy2n/glPVERERuXAD44YcfZMWKFdKqVasC/QLq1Kkjffv2ZQGAyI3l5ObJV1vj1fPBrSMkvEyAs5NEREREzp4HICUlRWX2jWFZcnKyPdJFRE7y0+5zEpeUKUGSLYNaRvA8EBEReRibCgCtW7eWqVOnqs6/GjyfMmWKtGnTxp7pIyIHysrJk+l/HVPPm/tfUB2AiYiIyLPYFAI0bdo06devn/z444/Srl07tWz79u1y8eJFWbZsmb3TSEQO8sOOODmXlC6VyvpL47xLPO5EREQeyKYWgM6dO8uxY8dkyJAhkp2dLTk5Oeo5luE9InI/Gdm5MmN1fu3/sPbVxN8nz9lJIiIiIleaB6Bq1aryxhtv2Dc1ROQ087ackYvJmVI9PFjublJZ5mzlySAiIvJENrUAEJFnSc3Mkc/+PqGeP90zWgL9eWsgIiLyVPyVJyKZs+m0JKZmSZ1KZeW+NjV5RIiIiDwYCwBEXu5aerbMWptf+/9Mr2gJ8ONtgYiIyJPxl57Iy3214ZQkZ+RIVEQ5uatFDWcnh4iIiFy1AIDRfzZu3Chz587VL0tMTLRXuojIAa6mZsnXG06p58/2bih+vj487kRERB7OpgJAbGysmgysZ8+eMnToUP3y0aNHy9KlS+2ZPiIqRbPWnZTrmTkSExkm/ZpU47EuRkJyhuw/d00Onv93xnM8xzI88D4REZFHDgM6YcIE6dSpk+zcuVOCgoL0yydOnCiTJk2S/v372zONRFQKElIyZM6m/Nr/5/o0FF/W/hdr/tZYmb4qf64EzcCZm/XPx/eMlgm9G9r/ZBERETm7ALB27Vo5fPiwBAYGFljerFkzVSggIteHYT8zsvOkZa3y0qNxhLOT4xaGdKgtvWOqmn0/IvTfChEiIiKPKgBkZGSIr29+9JCPz78xw/Hx8VK2bFn7pY6ISsX5pHSZvyVWX/tv+D0m8yLCgtWDiIjI6/oAdO/eXWbOnKmeaxmH1NRUFf7Tq1cv+6aQiOxuxprjkpWbJ+3rVZSuUZV5hImIiLyITS0A06ZNk27duskff/whOp1OBg0aJOvXr1fvYWQgInJdsYlpsnB7nHr+XG/W/hMREXkbm1oAGjduLPv375e+ffuqDr8ICRozZozs2bNHGjRoYP9UEpHdoBNrTp5Obo6uLB3qV+KRJSIi8jI2tQBARESEvPrqq/ZNDRGVquMJ1+Xn3WfV8+f6NOLRJiIi8kI2FwDy8vLk7NmzcuXKlULvtWzZsqTpIqJSqv3P04n0uqmqGv2HiIiIvI9NBYBNmzbJ4MGD5cyZMybfR78AInIth+KTZeme8/pZf4mIiMg72VQAeOKJJ1T8Pyb+qlChgv1TRUR297+VR9X/dzSPlJjqYTzCREREXsqmAsDRo0fVZGBhYcxEELmDvWeTZMXBi4LJfif0inZ2coiIiMjdRgGKioqShIQE+6eGiErF+yvya//vaVlDoiJCeZSJiIi8mMUFAAz1qT0Q+vPoo4/Kvn37JD09vcB7eBCR69h++oqsPXpJ/Hx9ZDxr/4mIiLyexSFAZcqUKbSsefPmJtdlJ2Ai14Dv4rTlR9Tz+9vWlDqVQpydJCIiInKXAoA20y8RuY9NJxJl66krEujnK0/2YOw/ERERWVEA6Nq1q/75gw8+KN9//73J9fCe4bpE5MTa/xX5tf+DO9SWGuULt+IRERGR97GpE/APP/xgNsOxcOHCkqaJiOzg7yOXZHdskgQH+Mrj3RvwmBIREZH1w4AmJSWZfK7NDLxx40aJjIy0ZpNEVMq1/8M61ZWIsGAeZyIiIrK+AGA46ZepCcB8fX3l3XfftWaTRFQKlh+4IAfOJ0tIoJ+MuYW1/0RERGRjAWD79u3q/3bt2umfawICAqRWrVpSsWJFazZJRHaWm6eTD27M+juqaz2pGBLIY0xERES2FQDatm2r/j916pTUrVvXmj8lIgf5be95OXrxuoQF+8uom+vzuBMREVHJOwEz80/kmnJy8+R/N2r/H+tWX8LLBDg7SUREROQJBQAick0/7TonpxPTVNjP8C71nJ0cIiIickEsABB5iMycXJm+6ph6Pu6WBlIuyKoIPyIiIvISLAAQeYiF2+PkXFK6RIQGycMd6zg7OUREROSibK4ixLj/Z8+elStXrhR6r2XLliVNFxFZISM7V2asOa6eP9kjSsoE+vH4ERERkf0KAJs2bZLBgwfLmTNnzE5CRESOM2/LGbmYnCk1ypeRB9rV4qEnIiIi+xYAnnjiCenbt69MnDjR5IRgROQ4qZk58tnfJ9Tzp3tGSZA/a/+JiIjIzgWAo0ePytq1ayUsLMyWPyciO5qz6bQkpmZJ3Upl5d7WNXlsiYiIyP6dgKOioiQhIcGWPyUiO7qWni2z1ubX/j/Tq6EE+LFfPxERERXNptwCQn8effRR2bdvn6Snp0tGRkaBBxE5xlcbTklyRo5ER5ST/i2q87ATERFR6YQADR06VP3fvHlzk++zEzBR6buSmiVfbzilnk/o3VD8fH142ImIiKh0CgDr16+35c+IyI5mrTsh1zNzJCYyTPo1qcZjS0RERKVXAOjatas406FDh+TIkSMqHZUrV3ZqWoicISElQ77ZdFo9f65PQ/Fl7T8RERFZqEQ9BhMTE2Xz5s1qXgA8L20Yeah79+5y2223yYABA2T//v2l/plErgjDfmZk50nLWuWlR+MIZyeHiIiIPL0AgI6/Y8aMkapVq0rnzp2lS5cu6jmW4b3ScunSJXn99ddlw4YNpfYZRK7ufFK6zN8Sq55P7NNIfHwY+09ERESlXACYNGmSrF69WhYvXizx8fFy4cIF9XzVqlXqvdIycOBAufXWW0tt+0TuYMaa45KVmycd6lWULlGVnJ0cIiIi8oY+AD/88IOsWLFCWrVqpV92zz33SJ06ddQMwTNmzBBXkZmZqR6a5ORkp6aHqCRiE9Nk4fY49fw51v4TERGRo1oAUlJSVGbfGJa5Wgb7nXfekfDwcP2jVq1azk4Skc2mrzomOXk66dawirSvV5FHkoiIiBxTAGjdurVMnTq1wHj/eD5lyhRp06aNuJKXXnpJrl27pn/ExeXXnhK5m+MJ1+Xn3WfV82d7N3R2coiIiMibQoCmTZsm/fr1kx9//FHatWunlm3fvl0uXrwoy5YtE1cSFBSkHkTu7sO/jkqeTqTXTVXV6D9EREREDmsBwMg/x44dkyFDhkh2drbk5OSo51iG94jIvg7FJ8tve+PVc9b+ExERkcNbAADDfr7xxhviSLGxsbJr1y79nAMYDjQpKUkaN26sHkSe6oOVR9X/dzSPlJjqYc5ODhEREXljAcAZjh8/LnPmzFHP7777btmxY4d6PPjggywAkMfaE5ckKw9eFEz2O6FXtLOTQ0RERG7OrQoAPXr0UA8ib6z9v6dVDYmKCHV2coiIiMgb+wAQkWNsP31F1h69JP6+PjK+J2v/iYiIyEkFAITc2PIeEVkOQ+tOW35EPR/UtpbUqRTCw0dERETOKQBgJmBzGZaFCxeWNE1EJCKbTiTK1lNXJNDPV57qEcVjQkRERI7vA4ARd0w9h7y8PNm4caNERkbaJ2VE3l77vyK/9n9wh9pSvXwZZyeJiIiIvLEAUKFCBZPPNb6+vvLuu+/aJ2VEXmzNkQTZHZskwQG+8vitDZydHCIiIvLWAgBm+wXM/qs91wQEBEitWrWkYsWK9k0hkZfJy9PJ+yvyR/4Z1qmuRIQGOztJRERE5K0FgLZt26r/T506JXXr1i2tNBF5teUHLsiB88kSEugnY25h7T8RERG5QCdgzAL8xx9/FFqOZenp6fZIF5FXys3T6cf9H9W1nlQMCXR2koiIiMjD2FQAeOmll+To0fxMiiEse+WVV+yRLiKv9Nve83Is4bqEBfvLqJvrOzs5RERE5IFsKgAsWLBAHn744ULLhwwZIt9995090kXkdXJy8+R/N2r/EfoTXibA2UkiIiIiD2RTASAzM1PS0tIKLU9NTZXr16/bI11EXuenXefkdGKaCvsZ3pl9bIiIiMiFCgC33HKLCvXJzs7WL8Pzl19+Wbp162bP9BF5hcycXJm+6ph6/nj3BhISZFX/fCIiIiKL2ZTLmDp1qnTt2lWio6OlY8eOatKiLVu2qFaBdevW2bJJIq+2cHucnEtKl4jQIHm4Yx1nJ4eIiIg8mE0tAI0bN5Z9+/bJsGHDVNgPRv4ZPny47N27V2666Sb7p5LIg2Vk58rHq4+r50/2iJLgAD9nJ4mIiIg8mM1xBpGRkTJ58mT7pobIC83bckYSUjKlRvky8kC7Ws5ODhEREXk4m1oAtJj/jRs3yty5c/XLEhMT7ZUuIq+Qmpkjn/59Qj1/umeUBPmz9p+IiIhcsAAQGxsrrVu3lp49e8rQoUP1y0ePHi1Lly61Z/qIPNqcTaflSmqW1K1UVu5tXdPZySEiIiIvYFMBYMKECdKpUydJTk4usHzixIkyZcoUe6WNyKNdS8+WWWvza/+f6dVQAvxsbpAjIiIiKt0+AGvXrpXDhw9LYGBggeXNmjWTnTt32rJJIq/z1YZTkpyRI9ER5aR/i+rOTg4RERF5CZuqHDMyMsTXN/9PfXx89Mvj4+OlbNmy9ksdkYdC2M/XG06p58/2bih+vv9+j4iIiIhcrgDQvXt3mTlzZoECAIYDnTRpkvTq1cu+KSTyQLPWnZDrmTnSpHqY9G1SzdnJISIiIi9iUwjQtGnT1Iy/f/zxh5oEbNCgQbJ+/Xr1HkYGIiLzElIy5JtNp9Xz5/o0FF/W/hMREZE7TAS2f/9+6du3r/Tv31+FBI0ZM0b27NkjDRo0sH8qiTzIp2tOSEZ2nrSqXV5ubRTh7OQQERGRl7G4BQAhP2PHjlXPL1++LBEREfLqq6+WZtqIPM75pHRZsDVWPX+ud6MCfWiIiIiIXKoFYNy4cfrnVapUKa30EHm0j1cfl6zcPOlQr6J0iark7OQQERGRF7K4AFCtWjXZsGGDivknIuvFJqbJoh1x6vlzfVj7T0RERC4eAoTJv9DxVysAFBW6wEICUWHTVx2TnDyddGtYRdrXq8hDRERERK5dAHj++eflwQcflJMnT8qtt94qK1euLN2UeYiE5AxJSMk0+35EaJBEhAU7NE3keMcTrsvPu8+q58/1bshTQERERK5fALjzzjvlt99+k9q1a6vXHO/fMvO3xqqaX3PG94yWCcwQerwP/zoqeTqR3jFVpUWt8s5ODhEREXkxiwsAy5cvl9zcXPHz8yvdFHmYIR1qq0xfRnauDJy5WS1bPLaTBAf46VsAyLMdik+W3/bG62f9JSIiInKLAkB0dLS89tpr0rFjR/UarQFFtRZQPoT34JGWlaM/JDHVw6RsoE1zsJEb+mDlUfX/nc0j5abIMGcnh4iIiLycxbnQTz/9VJ555hmZPn26ej1w4ECz62JiMCIS2ROXJCsPXhRM9vtML9b+ExERkRsVALp37y7//POPfgQgZvKJivf+jdr/Aa1qSlREOR4yIiIicp95AAzt27fP/ikh8jDbTl2RdUcvib+vj+rsTUREROS2BYCmTZtKdna2bNy4UebOnatfnpiYaM+0EbktzIXx/ooj6vmgtrWkdqWyzk4SERERke0FgNjYWGndurX07NlThg4dql8+evRoWbp0qS2bJPIom04kytZTVyTQz1ee6hHl7OQQERERlawAgFmBO3XqJMnJyQWWT5w4UaZMmWLLJok8qvZ/2o3a/8Edakv18mWcnSQiIiIiPZvGoly7dq0cPnxYAgMDCyxv1qyZ7Ny505ZNEnmMNUcSZHdskgQH+MrjtzZwdnKIiIiISt4CgBGAfH199SMCaeLj46VsWcY6k/fKy0Psf/7IP8M615WI0GBnJ4mIiIio5C0AGBJ05syZ8p///EdfAEhNTZVJkyZJr169bNkkUalKSM6QhJRMs+9jRmZM2FZSyw9ckAPnk6VckL+M7cbafyIiIvKQAsC0adOkW7du8scff6h450GDBsn69evVexgZiMjVzN8aK9NXHTP7PobpnNC7ZBN15ebp9LP+juxaTyqEFAyRIyIiInLbAkDjxo1l//79MmvWLKlUqZIKCRozZow8/vjjUrVqVfunkqiEhnSoLb1jqkpGdq4MnLlZLVs8tpMEB/jpWwBKaume83Is4bqElwmQUV3r8ZwRERGR5xQAICIiQl599VX7poaolCC8B4+0rBz9spjqYVI20OavQAHZuXny4V/5tf+PdauvCgFEREREHtMJmIgK+mnXWTmdmCYVQwJleOe6PDxERETkslgAICqhzJxc+WjVcfX88e4NJCTIPq0KRERERKWBBQCiElq4PU7OJaWrfgQPd6zD40lEREQujQUAohJAp+KPV+fX/j/VI0rfqZiIiIjIVbEAQFQC87acUfML1ChfRu5vV4vHkoiIiFweCwBENkrNzJFP/z6hn0cgyJ+1/0REROT6WAAgstGcTaflSmqW1K1UVu5tXYPHkYiIiNwCCwBENriWni2z1ubX/mMGYX8/fpWIiIjIPTDXQmSDr9aflOSMHGlYtZzc2bw6jyERERG5DQ5YTsVKSM5QHV3NwfCXmGXXWyDs56sNp9TzCb0aip+vj7OTRERERGQxFgCoWPO3xsr0VcfMvo8OsAiD8RYI/UnNypUm1cOkb5Nqzk4OERERkVVYAKBiDelQW3rHVFVj3g+cuVktWzy2k37Me7QAeIuElAz5ZvNp9fy5Pg3Fl7X/RERE5GZYAKBiIbwHj7SsHP2ymOphUjbQ+y6fT9eckIzsPGlVu7zc2ijC2ckhIiIisho7ARNZ6HxSuizYGqueT+zTSHx8GPtPRERE7ocFACILfbz6uGTl5knH+hWlc4NKPG5ERETkllgAILLAmcRUWbQjTj1/jrX/RERE5MZYACCyAEZBysnTyS0Nq0i7uhV5zIiIiMhtsQBAVIzjCSnyy+5z+pF/iIiIiNwZCwBExfjfX8ckTydqKNTmNcvzeBEREZFbYwGAqAgHzyfL73vj1fNnvWiyMyIiIvJcLAAQFeF/fx1V/9/ZPFJuigzjsSIiIiK3xwIAkRl74pJk5cGLgsl+n+nF2n8iIiLyDCwAOEgugshv2HbqSoHX5JreX5lf+z+gVU2Jiijn7OQQERER2QULAA6wbH+89Ppgrf718Nnbpeu7q9Vyck0opK07ekn8fX1kfM9oZyeHiIiIyG5YAChlyOSPm7dLLiZnFlh+4VqGWs5CgOvR6XQybcUR9fz+drWkdqWyzk4SERERkd2wAFCKEOYzeelBMRXsoy3D+wwHci0bjyeqFoBAf195qkeUs5NDREREZFf+4mZOnz4ts2fPlosXL0qzZs1k1KhREhwcLK4Imcj4axlm30chAO9jvU4NKjk0bVR87f+QDrUlMrwMDxURERF5FLdqAdi/f7+0aNFCDh48KNHR0TJz5kzp3r27ZGdniytKSMmw63pU+lYfTpB/4pKkTICfjOvegIeciIiIPI5btQC88MIL0r59e1m0aJF6/fDDD0udOnXk22+/VS0BriYi1LKWiaycvFJPCxUvL08n76/IH/lnaOc6Fp8/IiIiInfiNi0AWVlZsnLlSnnggQf0y6pWrSo9evSQpUuXiitqX6+iRIYHi08x601avFeeW7hH4q6kOShlZMryAxfkYHyylAvyl7HdWPtPREREnsltCgCxsbEq1Kdu3boFluP1iRMnzP5dZmamJCcnF3g4ip+vj7zeP0Y9Ny4EaK9b1Sqv/v9x11np+f5ambz0gFy+XnDEICp96Ij9wY1x/0d2rScVQgJ52ImIiMgjuU0IUHp6uvo/JCSkwPLQ0FD9e6a88847Mnny5ELLP//8cylTxjEdPO+qVFZWJVWU67n/Hu5yfjnSo/wVaSinpUlEoKy7VkFiM8vI7I2nZd6mk9IuNFnahl6TIF/XmTBMpxMZEHRdPf/689PiU1zThgsytw8HU0Pk2JUqEuybK7rDq+SToyvFm+Xk5Kj/Fy5cKP7+bnObICIi8lrpReSHjfnoMOyJGzhz5oyq7f/jjz/ktttu0y8fPXq07Nq1S3bu3Gm2BQAPDVoAatWqJdeuXZOwsDBxlJSMbGn2xgr1fM6IdnJzdBXVQmBow7HLMnX5Ydl79pp6XTEkUJ64NUqNRhMc4CfOlpaVIzGvLVfPD77ZV8oGul/G0NQ+ZOfmSe8P1srpxDSZ1LeROubeLj4+XhWSH3vsMYmMjHR2coiIiKgYyOOGh4dblMd1mxAgZNqxUwcOHCg0MlDTpk3N/l1QUJA6CIYPZzDM7KNvgHHmH7pGV5Zfn+ginw5pLfUrh8iV1Cx567eDKjRo0Y44zhdQSn7adVZl/iuFBMrwzgVDzIiIiIg8jdsUAHx9fVUH4K+//lquX88P4di6dat6DB48WDyFj4+P3N4sUlZM6CZT7m0m1cKC5VxSuuoo3O/Ddaqjqps02riFzJxc+WjVcfUcw36GBLlfqwYRERGRRxYAtHh+TPqFCcDuuusu6d27tzz11FPSt29f8TT+fr7yYPva8vek7vKf2xtLeJkAOZZwXcbM3Sn3frZJtpxMdHYSPcIP2+NUAatqWJA83LGOs5NDREREVOrcqrqzYsWKsm3bNlm3bp2aCfjtt98uMvzHEyD2/7FuDeSBdrXli3Un5asNp2R3bJI8+PkWuaVhFRWz3rRGuLOT6ZbSs3Ll49X5tf9P9oh2iX4WRERERKXNrQoAgBFJMPa/t0ELwMS+jdQEVTNWH5cFW2Nl7dFL6tG/RXV5rndDqVu54AhJVLTvt8fJpZRMqVG+jDzQthYPFxEREXkFtwoBovzZhd+8u6mseu4WubtldXVIlu45L70+WCsv/7xPLiZn8DAVM96/5rO/82v/x/eMlkB/fhWIiIjIOzDX46bqVAqR6Q+2kt+f7iq3NqoiOXk6mb81Vm55b428u+ywXEvLdnYSXc6y/fGqoKS5npmrRmMKCWLoDxEREXkPFgDcXJPq4TJ7RHv54bGO0qZOBcnIzpPP/j4hN09drf5HnDvlZ/7HzdslF5MzC7UIPLlgt3qfiIiIyBuwAOAhOtSvJIvHdpIvh7aVRlVDJTkjR7UEoEVg/tYzarIrb4VM/uSlB6WowVPxvmF4EBEREZGnYgHAg2AOgV4xVeWP8TfLB/e3kJoVykhCSqa8/PN+NdMt+grkeVkmF3Mm/LkvXuKvme8bgSOC97eduuLQtBERERE5g9uNAkTFQ1z7va1ryh3NI+W7rbFqqEvMdPvUd7tl1roT8nzfxnJzdGVVYPA0V1OzZM/ZJNkTd0324v+z1+Ty9YJhP+YkpLADNREREXk+FgA8WJC/nwzvUk8Gtq0lX284JZ+vOyn7zyXL0K+3Saf6leT5fo2kVe0K4q5SM3Nk/zlk9K/lZ/rPJknclfRC6/n6iFjS8IERloiIiIg8HQsAXqBckL883TNazXT76Zrj8u3mM7L5ZKIM+HST9G1SVSb2aSTRVUPFlWXl5MmRCynyz9kk2RuXpDL9xxJSTGbs61cOkeY1w6V5zfLSolZ5aVwtVI3+c+Fahsl+AGgHqRYeLO3rVXTErhARERE5FQsAXqRiSKC8cmeMjOhaTz5ceVR+3HVWlh+4KCsPXpT7WteUZ3o3VJNiORv6KZy8fF2F8eTX7F+TQ+eTJctER+ZqYcHSotaNzH7N8tKsZriaNM3Y6/1j1ChAyOwbFgJ8DN5H6BQRERGRp2MBwAshk//eoBbyWLf6Mm3FEVUIWLTzrPz6z3l5pFMdeeLWKFVYcFQn3XNJ6f+G8cQlqTCl65k5hdZFxh41+y1rlb+R4Q+XiDDLwnb6NY2Uzx5uLa8vOVBgKFDU/CPzj/eJiIiIvAELAF4MYT+zHmkru2OvqiFDt5y8Il9tOCU/bI+T0TfXl1E311PhQxrDYTIxYs7N0VWsrjVPvJ6pz+yr/+OSJDE1q9B6ZQL8pGmNMJXR1zL9tSuWLVHHZWTyu0RVlmZvrFCv54xoZ9M+EBEREbkzFgBIdQT+bnRHWX/ssioIHDifLP/766h8u/m0PNkjSgZ3qC1rDieo2nPN8NnbJbKY2vPrNzrpIpOvZfrPXi3cSdff10caR4bqa/Xxf3REOfH3s/8otYaZfcT8M/NPRERE3oYFAFJQs96tYRXpGlVZ/tgfL++vOCqnLqeqCbJmrD5uspYenWoRV4/QmlsbR8jh+BT90JvI9B+/dF10pjrpVglR8foqs1+rvMREhklwgB/PBBEREZEDsABABfj6+sidzatL3ybVZNGOs/LhX0ckIaVw5h+0vD3mF8CLbBND8lQPD84P46kVLi1rlpemNcMlLLhwJ10iIiIicgwWAMikAD9fFfpTo3ywDJu9vcijlJ2bn/EvXxaddMtLyxthPMj0c2x9IiIiItfCAgAVKSk926Ij9BqGF+1S1yNnFyYiIiLyJPbvZUkexdIa/Jsiw5j5JyIiInIDLABQkTBSDkb7MVevj+V4n7PoEhEREbkHFgCoSBgmE0N9gnEhgLPoEhEREbkfFgDI4ll0I8KCCizHLLpYzll0iYiIiNwHOwGTRTiLLhEREZFnYAGALMZZdIncX25urmRnWza6FxERuY6AgADx87PPxKksABAReQGdTicXLlyQpKQkZyeFiIhsVL58ealWrVqJR15kAYCIyAtomf+IiAgpW7Ysh+0lInKzSpy0tDRJSEhQryMjI0u0PRYAiIi8IOxHy/xXqlTJ2ckhIiIblClTRv2PQgDu5yUJB+IoQEREHk6L+UfNPxERuS/tPl7SvlwsABAReYmSxowSEZFn3MdZACAiIo91/fp1GT58uMTGxpp87cpQw4e0njhxwtlJoSJcvnxZnSctNttRduzYIW+99ZY429WrV2Xs2LGSkZHh7KSQFVgAICIij4VMyTfffCNXrlwx+drV+24grZcuXTK7Dvp2fPbZZ/Lkk0/K5MmTZd++fVZ/jj224c1QqMR5Sk5OdmiH0HHjxkmDBg1KfB737t0rzz77rIwePbrQe8eOHVOFG1MPFECgQoUK6hqdNm2aHfeQShsLAERERG4IGbCWLVuqjN5NN92kMmFt27aVOXPmOHQb5Hh//PGHnD9/Xh544IESncd77rlHHn74YTl+/LjMnTu30Pvh4eHSvXv3Ag+MRIN1q1Spol/v6aeflg8++EDS09NLYW+pNHAUICIicmkrV66UZcuWqVrPu+++W2655ZYCteTff/+9bNy4UYKCguTOO++Unj17WrX9UaNGqe1gRI06derIoEGDVCbKMMRj4sSJ8tprr8nvv/8uhw8flrp166oa2HLlyunX27NnjyxcuFDVBLdu3VoeeeQR8ff/92d2+fLlaj+gQ4cOKvNmGM+blZUlX3zxhezfv1/V7D744INFprtWrVoqwxcaGqpfhucvvPCCqqGFn3/+WVatWqUyZ4GBgWrZmjVrZP78+fL+++9btA1LFXcutOP4xhtvqAzsoUOHpGbNmuo4hoWFFdhWccfKkCXbPX36tHr/008/1XeizMzMlDFjxsirr76qjre2nRdffFH++usvtZ3atWur7WDfcG5OnTolzZs3l8cee6zQCCxoCfj444/NXh/F7Zf2+a+//rr8+OOPcvToURkxYoR06tSp0D5//vnncv/99+vTYOt5/O9//ytNmzaVL7/8UlasWFHofYw0Y/z3M2fOlD59+qjviqZbt24SHBwsixcvVtc9uT62AJSyhOQM2X/umhw8/2/TIJ5jGR54n4iITHv88cdl4MCBKvOKTA4ycchka5BJRoYNmREMkYdMJzK21kCBAjWbyGhdvHhR2rVrJ+vXry8U4tGlSxeVOY+KilI1oPgbZAxh06ZN0rFjR5WpjImJkc2bN6sMmuapp55S+4IMFTKHb7/9ttovQ/fee69KOzKj8fHxJjN+hqpWrVogwwdIG2Kyc3Jy1OsePXqoQstLL72kXqNm+KGHHlLHErW7lmwDpkyZIh999FGR6SnuXGjH8dZbb1WhJY0aNZJFixZJr169VOHOmmNlyJLtInONdVDIMuxjYRhipW0HhRYUGLAdZPr79u2rMriJiYnSuHFjeeedd+SZZ54plI7bbrvN7PVhyX5pn4/POnPmjLqecH6MYZsoxOF61Fh6Ho0h82+NAwcOyNatWwuFC6EQ07VrV5OFCHJROi9z7do13A3U/47wwYojujov/Gb2gffdRWpmtj7deO6OPGEfHOH8+fO6N954Q/1P7i89PV138OBB9X8hmdfNP7LSrVg3zbJ1rbB69Wqdj4+PbsuWLfpleXl5+uty1apVOl9fX92hQ4f073/99de6kJAQXUJCgnp96dIldc/fvXu3ydemvPjii7p+/frpX586dUr9zfjx4/XLEhMTdaGhobo5c+bo/6Z///4FtnPu3Dn1/7p163TlypXTXbhwocDfY9maNWvU67/++kvn7++vO3nypH6dd955R33u5s2bLTpeOTk5uo4dO+p69epVYPmGDRvUtlesWKG74447dJ07d1brWrONW265RXf33Xeb/WxLzoV2HD/88EP9OthfLNu7d6/Fx8qYJdvdvn27en316lX9OikpKQWOr7adjz76SL/OkiVL1LLPP/9cv2zBggXq3FtzfViyX9p2/vvf/+qKEhsbq9bbsWOH2XXMnUdzvvjiC11QUFCx602YMEFXtWpVXVZWVqH3nn/+eV2bNm0s+jwqnfu5NXlchgCVsiEdakvvmMIleE1EaFBpJ4GIyLy3q5t/L7qPyJBF/75+L0okO830unW6ioz4/d/XHzYTSUssvN4b1yw+G3/++aeqoUSohGFNozYD5tq1a6VFixaqVtawFhohPTt37pR+/fpZ9DkYvWXevHly8uRJVQuLMA9TowTdd999+ucVK1ZUNczr1q2TYcOGqXQiNGL27Nmq5hvx0dWr5x9b1MCjRvzll19WNdJarXRAQID8888/qqYY+4KwoXr16uk/Ay0IWs29JSZMmKDCVrZt21ZgOWqKsZ277rpLtaTgM81NIGRuG6jZ10KITLHmXBiGBWF/cRzOnj0rzZo1s+hYmVPUdq2BlgSN1snWeFlKSop6GNa6F3V9WLNfaHEoCj4XQkJCzK5j7jyWBFpP0LKBc4p0G0O4k5Y2cn0sAJSyiLBg9SAiIusgfAHhEuYgdMN4ZmNkshDjbemQjAj1aNOmjQpfQCYPYTGIZUaogzFk6oxfa58zZMgQFZrx7bffqlFYGjZsqMKV0GcBoSNIJz7DEEI9kOnX9sXU9i2FjCU6fCLGHJ9tKswJQ0YirMiwkGHpNoorTFlzLnB8Dfn6+upDZSw5VuYUtV1rGG4H2zC3zHjbRV0f1uxX+fLli0yfdpzx/bDlWrDVkiVL1H48+uijJt/HKEScadx9sABAROTN/nPe/Hs+RrXEk44Xsa5Rl7JnSj6UJDpyojOmOeigiQ7ChhDrnZqaWqCDYlEQK4547F9//VW/DP0ATEGrQJMmTfSvEadtmMEaOnSoeqAfAOLl0Zk4Li5O7ce1a9fUe1rm0dS+oLbYELZvCXRiRedTdC411W8AGbORI0eqzpnoFDxr1izV+dWabRTHHucCLDlWtkCnZDDsA2AuA22roq4Pe+4X4v3RCoZCqvG5Kul5LMpXX32lWirQt8AU9H9AYZrcAzsBExF5s8AQ84+AYCvWLWPZulbACCkI4UBHTA1qILXxxzGEIWrwUTOpmTp1qurgahg2VBSEFCHsR+soiUwrMsimIFOv1fru3r1bdcTUOnEiw6XNLYDMZu/evVUnU2SAEQqD7SJtxmEz586d0+8LRo8x7ESJkXuKgxFjpk+frj6/c+fOJtfBaDSVK1dWGTisizHfjxw5YtU2iusEbI9zAZYcK1ugkIewFcPO3Rj5xp6Kuj7svV+333672r4hS84jzv0PP/xg9efhe4hrE6MfmYJC75YtW+SOO+6wetvkHGwBICIil4R4cmTSMHIKYusRDoS4ZjzX3kfGFJkr1EyihhW1kBiK0DgcxBzUyKLGFGOoY3sbNmxQmUVTscyoPcYQkNHR0WpoTQzRqMWG47NR+4naUYRwIHOGWvb69eur9xE7jcwTRjBCDDky4MiUayMaYdjRV155RYUMIZb9woULKhypKBjy8s0335RWrVqpYSHx0Pzvf/9TEzThc5EpRxw+MsBoCUDfCoQsYaQi1NoXtw1AphL7hfHezZ2rkp4LwMg7xR0rWyBWH/0gcL4XLFigRlkyHjWnpIq6Puy9XwgzQ98ODDmLoU4tuRYAn4UWCG3+AEwchlF9MHqSNvM0aEOjavCdwzYGDBhgMj1oQUO/FwwPSu7BBz2BxYvgy4KbKm5OxuMOU9HSsnIk5rXl6vnBN/tK2UD3Kz96wj44An4c8QOCHyutwyW5L8x+i46tiP22JjPmKlDrj5pbjKmPTI+WmTEMtUAmBjXviKk2fB81k999953qAIuYbOPXgFp6ZNjxPzLx6CCL7Wnj8KNmG8cOmSRMgqSN896+ffsC6UBLAmpB8fuCjqfG8df4/UEBA5+DoUINw0U0GMv94MGDKvOFQgk6J6NW1XDSJQ06LRuHDRl2IEb8PUJ+8LeGsecIfUGGDQUNZPqK24ZWAEAnYAwrWpSizgWODwoE6CxrmPlGxhgZZYTJWHOsbNku5mrA+PooqOEcGR5fU9vBucQxNDwWuB6XLl0qgwcPVsfE8O/wPTN3fRS3X+b2wxxcn/h7ZNYtuRa0kDdcy5gkTGuBQJqNGV9zOP84p4adoTXIRmLo3EmTJukLFuSc+7k1eVwWAMirMs+esA+OwAKAZ3H3AoCzGRYAzMU/EzkaWonQimOuVt5R0McErUqYX4LcpwDAPgBEREREbqZatWpOz/wDQsOY+Xc/LAAQEREVAXHaiIE2NSsrEZE7YvwDERFRETDBkdY5kojIE7AFgIiIiIjIi7AAQERERETkRVgAICIiIiLyIiwAEBERERF5EXYCJiKiYiUkZ0hCSqbZ9yNCgyQijHMMEBG5A7YAEBFRseZvjZU7P95g9oH3yf3l5eXJ999/r2a7LYlLly7J77//bvXfYbI1zLy7fPly/SyzmOxq4cKFapZkR8FMvTgOmJ3XnTlqP7KystTnYFIwZ3KVdNh6LrSZpx2BBQAiIirWkA615benusrisZ30y/Acy/DA+6Xp6tWr8tdff8mvv/6qMoI5OTmF1rl48aJaZ8WKFeq5O83siYwBfvyd8ffGGShM6oSMeEk8++yzsn37dqv+5uuvv5Z27drJvHnzZM2aNWrZoEGD5JFHHpEff/xRDh06JPZgyfFCAQbHAbPtujNH7QdmoMXnYNZsZ3KVdNh6LjB77xtvvCE//fSTlDaGAJFXhS9kZOfqlx08nyzBAX7qOcMXiIqG8B48UjKy9cuuZ+ZIq9oVxM/Xp9QOHzJrEydOVJnDVq1aSUREhJw6dUrS09Nl6tSpcvfdd6v1Xn31VXn//felffv2EhISogoJ3bp1k88//1zKli3r0qcXtZXIGCDN4eHhDv97e0M6kIE5f/68VX/35ZdfygsvvCAvvfSSen3lyhWV8d+7d680a9bMbulztePlCYKCguSBBx6QChUqODspbs3Hx0d9B/7zn/+oWZ7xurSwAEBeAeEJ01cVrNEaOHOz/vn4ntEyoXdDJ6SMyH0s2x8vry85oH89fPZ2iQwPltf7x0i/ppGl8pkPP/ywbNu2TXbs2CExMTH65XFxcbJz5071/O+//5b//ve/smHDBunSpYs+lOWHH36QzMzMIgsAqDFEiAm0bNmywGy/aG1o0qSJREVFFfgbNNE3bdpUoqOji90Gmv2XLl0qd955pyQkJKha7Jo1a0qLFi30IS74HFi2bJns379fqlSpIj179iyU1uzsbNmyZYv6PPw9tmPu71u3bq3CaO677z4JCAhQ76PVZPHixdK7d2+pVKmSfruo7T948KA0aNCg0L5i2zju2r4aHgNkyo3Xh08++UTuueeeQplrc8cJ+4WM/smTJ+XEiROqdh60AsT69evlwIED0q9fPylfvnyxx1yTkpKijheOUadOnSQ0NNSq4605evSoHDlyROrWrVugIGLtsVm9erVKJ64pwDZ3794td911l/4aRZpwHiy5too7rubguK5bt05uvvlmqVGjhsnryhxz66IAYHjOi7vuiztPJdk/Ywgfw/3Az89P6tSpoyoSAm58J7TWxeK+K/bcH8C1bm4byPiPGTNGVq1aJb169ZJSo/My165d02G38T9ZJzUzW1fnhd/UA8/dycVr6bp9Z5PMPvA+/ev8+fO6N954Q/1P7i89PV138OBB9b+t/tx3Xlf3xvff8FH3xgPv29vmzZvV/XrhwoVFrvfxxx/rgoODdXl5eVZtf/HixboKFSrounXrpuvXr58uPDxcN2PGDP37AwcO1D3wwAMF/gbHEWnas2ePRds4deqUWr9///66mJgY3R133KErV66c7qmnnlLv5+bm6u6++261Dv4en/f6668XSmtcXJyufv36uqZNm+ruuusu9Xzy5Mlm/3779u1q2dWrV/XbSElJUctwXDVTpkzRBQUF6Xr27Km2fdtttxVYZ8iQIboBAwYUSAv2HevgWJhSs2ZN3ZdffmnxsU5NTVXpxnFp27ateo7H7bffrj4H+4vXp0+ftuiYw6JFi9Tyli1b6vr06aOLjo7WbdmyxeLjrZ03HI+oqCi1jbJly+rGjx+vX8faYzN27FjdoEGD9K+HDx+u1v3999/V64yMDHUdr1692uL9tPT6O3bsmHp95MgRXZ06dXRPPPGE+r6Yu65MKWrdS5cuqc/ZvXu3Rdd9cefJ0v0zZpwOGDx4sDrP9913n9o+0hMbG6t/35Lvij32x9JtQK9evQpca5bez63J47IAQF5RACDrsADgWUz9YODHH99jSx7J6Vm69v+3slDm37AQ0OH//lLrWbI9SzPqKIT6+vrqMjMzi1xv69atOh8fH5XB2rdvn0XbP3nypC4kJET3999/F8gIIAN26NAh9frnn3/WlSlTRpecnKxf5+WXX9Y1a9bM4m1oP/ojR47UpwvrI71nzpxRr+Pj49U6SHtRx6Jz587618jI/vLLL2b/3pJMzdGjR3V+fn66pUuXqtdI30MPPVRgHaQ1ICBAl5CQoN/O008/revUqZPJdF68eFH9/aZNm6w61tCgQQPdZ599pn+N97AtZDyt2RYyu4GBgboPP/xQv86FCxf0+2TJ8dbOGzJiWVlZ+usM1+O6detsOjbff/+9LiIiQv+6bt26qsAzadIk/fZQGMP31JL9tOb6wzHZuXOn+vzXXnvNouvKWFHrmisAFHXdF3eeLL1uiisAGEKaURAYPny4TQWAkuyPJdvQTJgwocCxLo0CAEOAiIi8UHp2rsS8lj/SSknhF+dCcoY0e2OFResffLOvlA0s/ucHneWqVasmgYGBRa6HuP/vvvtOJk+eLDNnzlRN7rfccos8/fTTqgnfFKyPpn10zFu0aJFqroeKFSuqkJPGjRvL7bffLmXKlFEhHUOHDlXvL1iwQMaOHWvxNjRo0tfiebt27Sq+vr4qtKR2bcs6TyMdGJnn3LlzKmwDf6/1f7AVwm4QaoKwBkD6Jk2apPZLg+OI0Je5c+eqjr3oJDx//nyZMmWKyW1evnxZ/W8YC27NcSqOJdvCOrhucP41CBuxNnQEnnnmGX1YCK6z7t27q5AShM9Ye2zwtwgfQTgTrlF0VEc/Fjy0ULYOHTpIcHCwxftp6XHFttGX5q233pKnnnrKpuvKlmuwqOu+uPNkz+sGIW4Iu8EIPAj5WrduncV/a6/9sWQbGnx/tO9SaWEBgIiIXBIySegIih/+4jrDoQMiHsicIP529uzZ0qdPH33crjGMEoIMG+J8DSFjV7lyZfUcBY+BAweqTB0KAJs2bZIzZ87I4MGDLd6GBpkWDWKR/f39VQdnS40bN07++ecflWFH3DkKNk8++aTKiNkqNjZWZWAN1atXr9B6o0aNUscTmVzEvSPdONamlCtXTv2PmGmNNcepOJZsC/uF+Ht7dKA0dXxwDdhybJARRKYVIxzh2u7cubM6j0OGDFEjEiGTjkKCpftpzXFFphT9HAwz/9ZeV7Zcg0Vd98WdJ3tcNxgsoH///qqvBUaYQh8F9B9KSEiw6O/tuT+WbEOD74+pvgP2xAIAEZEXKhPgp2riLbHt1BXV4bc4c0a0k/b1Klr02ZZAjSt+GPfs2aM6/1kCmRF05rv33ntVR13U2JsqAGC4PdSyaR1Oi+qEfOutt6rWCBQEUOurdXy0dBv2gMwA9gUZg40bN8rHH38sbdu2lePHj5tcH7WKgM6PGuNMBmpXd+3aVWAZOkQaGz58uBplCZ2xMRoTCkXmMifVq1dXhQCM1NSmTRu7HydLtoWOwiWdx8Dc8cBrw8ynNccGkMFHRh/r4LpCWtEpGMPXouD62muvWbyf1hzXb7/9VtU8Y3SZt99+26LrCqNpleQaLE5x58ke1w1aZ9DJHYU2rXA6Y8YMNdSmNd8VS9jzusP3p1GjRlKaOA8AWTSE5v5z19SwmRo8xzI88D4RuRfUUiEMx5LHzdFV1Gg/5uq1sBzvYz1LtmdpzSxGR8GIKAhdMDXuP2rytFFNUFNoCD/mWKaNGmMMI8pgFI61a9cWGsEDDw2a6JHhx9j0CP1AgcDabRRHy5gUlelAywYgU4aWjY8++kgVSgwzNoZ/r9XKGmbOtLH1DfcNBQDtOIKp8cdRc41CFEZaWrlypar1Nge1mRh+FRlEex8nS7eF44OhQ1FbbQihJJYeb80vv/yif45aeozMoo00Ze2x0QoASLthbT/+R9iQNmqMpftpzXFFARppnzVrlrz88ssWXVfGrFnXEsWdJ3tcN0gfvgvaOddC3wxZ8l2xx/5YA62NpToCEFsAyBIcQpPIu2Gcfwz1OW7eLpXZz4/Ezadl5fG+vecDQAjOkiVL5I477lC1yZgQCvG7CA3AsH0IQUDc/9atW+W5555TwxDedNNNaqhCZNbxw4sQBVPw4/rYY4+pzBvCIlDQwLCMiPfHRGJaLS4KKwj5weegEIIaXmu3URxkTlDb9+6776ohAJGpNB6W8rPPPlNDIaJfAgo1aI1o3ry5NGzYUGW6Tf09MiTIkCL8Iz4+XtWGGurbt6/KrGM/kH5klhDOYsro0aPVZyP8AyEYRcG6OO7Tpk1TIQ72Ok5gybZwXeBawTEYP368mjsCGXmEceFcWnK8NThmOO8I3fniiy+kVq1a+v4gthwbZPYR252WlqZauLRlH374oToXiP+3dD+tPa4oBKClAX+H6xqFlqKuK2NFrWvLzLvFnSd7XDf4W/R7QF8OtAjib5FB9/P7txUS57+474o99sdSKDxj6FNMglea2AJAFs8Aau5R2jOAEpHzYZz/zx5uLRFhQQWWVwsPVstLax4AxBqjFhCtAGgWR80pMi/Tp09XmXJAJg41ZhjjG7PPYmx3FAZQo2c4d4Ax1IYivhgdA/H3yAggg2McB48wDxRCEOqBsARrtoHaUsSEG2dW7r///gKx07/99pvUr19f/vjjD9XB0Rgya/h8ZE4QKoJ9xnrI/Jv7e9Tmo8UC66MjK44d0mIYwoI+EsjAYk4FZD6RduN1ABkkjFU/YsQIi1puUFAzjN225Fgjs2Y4dj6ONdJiPI+DJduaM2eOmlgMrUO4HlBANMyEFXe8tfOGzqIIa0KIDzq8Yl3DMeStPTZIKwpHuJ617SCsDJ+F82Dtflp7/WEMfLRUYL4FrFfcdWWoqHWNJwKz9Lov7jxZ+h3VGKcD82EgjSjEIWON7zG+F/fee2+Bvyvuu2KP/bF0GwitQoHFsNWiNPhgKCDxIihVoRMImvKMb+RElA83eMygitqXyMjSydiR4yDUAZln/GhqNYy2wkzA2mg/iPlH2E9pzgRMrgGTWCEkA+EeltwTkJFC5vrNN98UT2ftsSEyB31MJkyYoFpbMOqStfdza/K47ARMRETFQl+fhJRMycjO1S8rF+Qvh+Lz+wZFhAZJRFjJChfkehAWhBpjhMuMHDnS4gxux44d1cOT2XpsiMxBywVaERyBBQAiIioW+wJ5J4xV/+eff6p4ZMwRQDw25BlYACAiomKhr0/vGPMTKaEFgDxPixYt1NCPxGNDnoUFACIiKhbCexjiQ0TkGTgKEBERERGRF2EBgIjIS3jZoG9ERB5HZ6f7OAsAREQeThtvHJMPERGR+9Lu48bzUViLfQCIiDwcZr3EzJ0JCQnqNSYtwmRaRETkPjX/yPzjPo77ueFsxh5fAMjMzJRFixbJzJkz5fDhw/Lrr79Kly5dnJ0sIiKXV61aNfW/VgggIiL3g8y/dj/3mgIApqA+d+6cPPXUU/Lggw9Kdna2s5NEROQWUOOPiYoiIiJ47yQickMI+ylpzb9bFgAw2x5+xM6ePevspBARuSX8eNjrB4SIiNyTW3UCZswqEREREZEXtQDY2m8AD01ycrJT00NERERE5LUFAMTyf/fdd0Wus337dqlXr57Nn/HOO+/I5MmTCy1nQYDIvJSUFMnIyFD/h4SE8FARERG5OC1va8lcAT46J84Mc/36dZXJKErFihXF17dgpBL6ANSqVUvWrFkj3bt3t6oFAJ2IY2JiSphyIiIiIiLXExcXJzVr1nTdFoBy5cqpR2kKCgpSD8PPxIEJDQ11eJ8ClMxQcMHnh4WFOfSziefBlfC74Bp4HlwDz4Pz8Ry4Bp6HkkGdPlruq1evXuy6Ht8HwBhaE4orFZU2ZP5ZAHA+ngfn4zlwDTwProHnwfl4DlwDz4PtwsPDPW8UoO+//14qV64szZs3V6/vvvtu9Xrq1KnOThoRERERkVtwqxaAAQMGSK9evQotx7T2RERERETkYQUA43h+d4O0v/766269D56A58H5eA5cA8+Da+B5cD6eA9fA8+A4Th0FiIiIiIiIHMut+gAQEREREVHJsABARERERORFWAAgIiIiIvIiLAC4iMuXL8uGDRvUJGHkeFeuXJHdu3er/6n0YTbvHTt2SFJSEg+3k1y7dk1d8wkJCTwHTobZ6nH/P3jwoLOT4tUTUO3cuVOuXr3q7KR4rQsXLqjfhdOnTzs7KV6BBQAXkJOTo+Y0uOWWW+Sbb75xdnK8CjJAvXv3loYNG8rIkSPVTM1DhgyRjIwMZyfNI2VlZcmDDz6ojvfQoUMlMjJSpk2b5uxkeZWjR4/KPffcI/Xq1VPXfIMGDaR///7M+DjRpEmT1P3/+eefd2YyvFJubq5MnDhRqlWrJo8++qi0bNlSXnnlFWcny+sKX7fddptER0fL2LFjpW3bttKiRQs5fvy4s5Pm0VgAcAGvvfaa+jFGZogc69ixY/Liiy+qFhgUBg4dOiRr1qxR54Ts75133pG1a9fKkSNHVG3nTz/9pDI969at4+F2kBMnTsiIESMkMTFRXfOnTp1S34Px48fzHDjBkiVL1D0HGSByPNz/582bp2r/te9DjRo1eCoc6N1335V//vlHTp48qVoA0EKM+Z2eeeYZnodSxAKAk61atUrNcPzJJ584Oyle6f7775eePXvqX9euXVvuuOMO1RxP9vf111/L8OHDVUsLINPTpk0btZwcA8ccLY4+Pj7qNWZTHzRoEK95J0BGZ9y4cTJ//nwJDg52RhK8Gip+PvroIzU/z0033aSW+fr6qnNCjnPp0iWJioqSKlWqqNf4LuB3Acup9LjVRGCeBhf3sGHD5LvvvpPw8HBnJ4dEBNNioCaoadOmPB52hv4VsbGx6sZuqH379sx8Otn27dvVDzA5NvQE4YbPPfecNG/enIfeCdavX6/CEhECh8IYCgQIiQsNDeX5cCC0Pvbt21fefPNN6dKli2ohXrx4scyePZvnoRSxAGBHCGkorhNpp06dxM/PT2U0EQON2tCbb77ZnsnwaojdRxNiUapWrapiDc01RSIMiH0x7E/7blSqVKnAcrxm52vnmTNnjqxcuVJWr17txFR4H2R2AgMDZcKECc5Oitc6f/68+Pv7q/s+whFxL0I4HPoEvPXWW85Ontdo1KiRygt98MEH8ssvv8iZM2fk9ttvl86dOzs7aR6NBQA7QqZx48aNRa6zbNkyKVeunCrZ7tmzR8U/a+EmqIlADemWLVukY8eO9kya10BcM2I6i4Iby3/+859Cy3FOEPu/YMECadasWSmm0jsFBASo/407WKenp6uMEDne0qVLZcyYMTJjxgzVCZUcY9++fSrT+e233+p/M3Dvwm8Afg/QSlamTBmeDgfckzAIBzqh4rcXlXN///23CgtFR1SEypFjOsH/+uuvaoCCiIgI9Ztw1113yX333Sd//fUXT0EpYQHAjnBDt0b9+vXl1VdfLTAs3/Lly9VQoH/++ac9k+Y10HnLlvh9/BAjI4T/Bw4cWCpp83bVq1dXP7jnzp0rsByv0feCHOv3339Xsf8YhYkxz46VlpamMpiIP9ccPnxY8vLyVAUGKiH4nSh9devWVf9j9B9k/qF79+7SuHFjFR7EAoBj/Pbbb+pehMw/oPCLEcoGDx4sqampEhIS4qCUeBcWAJwEFzcehmrWrCmjR4/mEGQONnfuXHXc0YKDISqpdCDzj1pmjHqCH1ytNQCFXsRBk+OgggG1a1OnTpWnnnqKh97BOnToUKiiAhUP+D4gM0SOgXhzZC4NKyXQCoP+eVqHVCp9ONbog2EIFaE4NxgNiEoHCwDk1X7++Wc1JCJqQDEyjfajjFEIUENH9oW4WhQCEGPbrVs3mTlzpgqJe/zxx3moHQRDrg4YMEDuvfdead26tf6ax+gnjLklb4IMJvpioAIChS/0D/v888/VdwEx6eQYqIR4+OGHVVQEfhfQD+///u//5Mknn9SPVkb2xwKAC8FoKGz2dfyY6OhvgfGf8dBgToZFixY5ODWeD8caGdCPP/5Ypk+fLk2aNJEvvvhCypcv7+ykeQ1MroPCLWKeDfvLBAUFqWGJyTkwDCVqn8mxnn32WVX5g7ArxJ6j/9enn36qCgPkGA899JD6zUUrPFol0SKAyiG2yJcuHx2GoyEiIiIiIq/AicCIiIiIiLwICwBERERERF6EBQAiIiIiIi/CAgARERERkRdhAYCIiIiIyIuwAEBERERE5EVYACAiIiIi8iIsABC5kLVr18o///xT6p+zceNG2bFjR6l/DrkG4/PtzPP/559/ysWLF8Vd8Lti2sKFC+XChQviavLy8uT777+Xy5cvu+z+/PTTT3Lu3Lli18vMzFTpys3NdUi6yLuwAEDkQt555x2ZN29eqX/O//73P/nyyy/F02zYsEF27drlsL9zBZak3fh8O+v8I63jxo1z2ZmfTR1LT/2ulNTgwYMdUllhLcymjJllDx8+7LL7M3LkSNm+fXux62F2bsyUPmvWLIeki7yLv7MTQESO17VrVwkNDfW4Qz9t2jSpWbOmtG7d2iF/5wpsSbuzzv+LL74ozz33nMrYuMux9NTvCrmHl156SR588EEZNWqUy35vyD2xAEBkQmxsrOzevVvuvvtu9RpNsIsWLZKYmBhp3ry5Wnb8+HE5duyY3Hbbbeq1TqdTtYdxcXFSr149adGiRaHtWrKOsaVLl0pYWJjccsst6jU+E7Vb1atXl5YtW4qfn5/ZvzW3brt27Qr8mCD0KDw8XOrXr6/2OycnRzp06CDlypUrtE28j+PTpEkTiYqKKtH+Yd2jR4+q5xUrVlTrV61atdhjYmq/UKOGZvX09HQVAgA4NydOnCjyM8z9HY6HNftTGtdMceelqLQXxfD879+/X86ePSv9+vUrsM6+ffvUtrXltly7hvbs2SNbt26VJUuWWLx/Gks++/r167Ju3Tr1t61atZKDBw+q5dieJdeauWPpjGNlLe041q1bVx3H7Oxs6datmwQHBxdap06dOrJlyxYJDAyUnj17qvfS0tJk8+bNKuQE1yoKQdbCecV2fH191d/jHJQpU0b/Po7rr7/+KnfccUeBAtWPP/4oHTt2lBo1aqjwHYS89OrVS62P41qlShV1DozhfexHRkaGOsc4p8Zw/IvaRnGKOy7F7bPxvQHHvlmzZoXex35v27ZNLl26pO6r+D5obr31VnX94V7y8MMPW70PROawAEBkwtWrV2XAgAHqhlypUiUVL41m5f79++szMO+//75cuXJFZRISEhLknnvuUbHNTZs2lb1796offvzgaT92lqxjCBnIsWPHypo1a2TlypVq2dNPPy1z586Vm2++WW3Px8dHfvnlF5OZ5qLWRVhD5cqVpW3btvrQo+TkZLW/DRs2VJlm/LDixy8yMlKtg3Tfe++9KhPbpk0btQ72591337Vp/7RM4fLly/V/jx/B6dOnq9ouc8ztF35g4+Pj5dq1a+q1Vntb3GeY+zv86FuzP6VxzRR3XsylvbgCgOH5x+cjjefPn1cZJc2TTz6pMiPI1Npybk0VZFFYM8yoWXLdWfLZyJgj01ihQgWpVauWKmThOTJtWgHA1uvAEccK6cejKMgImisc4zgi3SiEoMCJgg4Kxbh3aJlW7Vgjzv2mm25SGWIUAFavXq2u0wYNGqhjtmnTJnnmmWfk9ddfF2vg2CYmJqr71qFDhyQ1NVV+++03dVwA7+Fz8F7jxo31fzds2DAV9ogCgBa+g0IC9qFRo0YqPUgnCgaaVatWqZAdfM+QqT5y5Ih89tln0rdvX/06U6ZMKXIbxbHkuBS3z/D111/L448/rgo5KKTiGkABTZOUlCTdu3dXBQmcO2ynd+/eMmPGDPU+7m94H9tlAYDsSkdEheTl5ekqVaqk+/HHH9XrKVOm6Nq2basrX768Ljc3Vy1r3Lix7tNPP1XP77rrLt3QoUN1OTk56nVmZqauW7duuueee06/TUvW6du3r3qdkZGhu/fee3UtWrTQxcfHq/cuXryow1d27969+vV37dqlO3XqVKH0F7fufffdpxszZkyBz8X+nj17Vr3Ozs7WNWvWTPef//xHv85tt92m69Chgy4pKUm/7Ndff7Vq/4qzfPlyXUhIiC4xMdHk+8Xt191336174oknrP4MU39n7f6UxjVjyXmxZJ+Nz7fha6StevXquhkzZujfj42N1fn4+Og2btxo07EwZcCAAWobhizZP0s+u0ePHmo9bZ1169ap62TYsGElvg4ccawWLVqke+CBB4p87Nu3z+zf4zgGBATo9uzZo17j/tGlSxfd4MGDC6xTrlw53cmTJ/XLsO+4PvH5muPHj6v11q9fryuKn5+f7s8//zT7/rhx43T9+vXTv46Li1Pn5NChQwXWwzn4+eef1fP09HS1Tv/+/dW1AAcPHlTHd9u2ber1pUuXdGFhYbqXXnpJvw3ck9asWWPxNorbH1uPi/E+JyQkqL/54osv9MvGjx+v0qft88yZM3UNGzbUpxV++umnAtt99913dfXr1zf7uUS2YAsAkQmodUENM2rQUOv9999/y5gxY+S1115TNYWoVUMICmpmUAOE2s233npLfv75Z9X8jwea4/H3YMk6mpSUFLn99ttVbZjWbA/+/v7qgRpFrRkZTc6mWLOu5q677lK1cNrfd+nSRdWsAWpoMXoLaqEMa5fxN9bunzH8LdKJz0BTOPb7wIED6vjbY7+s/Qxb98fe14wl58UeEL6Ams758+fLE088oZYtWLBA1Vp37ty5ROfWEEZlqV27dqHlRe2fJZ+NlhfU1uJ4ayFuOA+ocS3pdeCoYzVw4ED1KAnUfmuhZggZGT9+vKoxRmsZ0g1ovUBaNWjpwHEAhJgA0ovzhPSiBcQaaB1ErTtaGnCfQCuLLR599FF1LQBaK9DygWsCrRY4rkizYU08PgvfK0u3URxrjktR+/z777+rc4FOv5oXXnhBtTxpEDKEvz19+rQ+pBItiYbQAmHtqEZExWEBgMgM/KBgBAY072IowI8//ljF4eMHAGEG+EHBDwtiffHjgPhmxJsa0jIhZ86cKXYdDX6w8ZnILBpmthE6gfTgB+T5559X6XvkkUcKxSNbu67h3xjCDxfCMbQYVkCYhinW7J+hzz//XHUKRZM54vkRlwwIozCXRmv3y9rPKMn+2POaMdxnc+fFXoYMGaLCk06ePKnij5HBxbKSHAtjiM1HiISxovbPks9GnDcgk23I+LUt14GjjlVJQ4BM7S8y+ijgIOQH+wtaWJUGmU4UDhYvXlxgOQrXWugQ+kYgNEuDwq127DS43lEwQkhM+/bt9RlWhLvhvaL6KdlyL0JGvLgOsSX53lhyXCzZZ6QV33utAKadA8O0Yxu4VyA8DtcTQtkQMmTYvwrfG3ZEJ3tjAYCoiMzchAkTVM03MuK4IWMZavhwU9c65aKDLkycOFF1vDPFknU0o0ePVj8ciINFxtHwR3/48OEqZhYdHJEO1BR99dVXKh7WmDXrFkcbthE1nNHR0SXaPw3iYLV4/kGDBqllyLCgxg2ZKHOs2S9bP8OW/bH3NeNIaEVB/DFqs9GJGZlXLfNjr7Si8FhcJteYJZ+tZfQQS414cA1aBqpVq1ai68BRxwqFfa3fgTnoT1BUAQD7a/warVKGGWG8NoT0YpnW6dkUtF6hhUWD+5JxAQA13StWrFC14VrfCBwT3L+046tlgrWade05zoO19yLch0qTJcfFkn1GHwXj84LOy+hfpAkICFDDfKJVAAVHVCBgFCq0KmjX76lTp1RfBiJ74jwARGagOR0/npMnT9Y3L+P/9evXqx9EbRkyeXjMnDmz0Da0yV4sWcfwBwGd1ZBhQq2fNmkSOvmhJgg/TKjFxJCKyGRgJAxj1qxrCXSEw+Pbb78tsByhFNbunwY/jPghNPxhwwQ5hhkEa/cLtcyGtXyWfobx39myP/a+ZixlnHZboRYbtdl4IExCa+2xV1rRCRPhEYYdIItjyWcjfAgZf3Sy1aAWFmP6l/Q6cNSxQvgPMptFPVAAKAoyo+hIarh/6KxvOBKQqbAhHBvjzrE4Blom+7HHHiuQDlM10WhlwHVv2DHauPY8IiJCheQgw6xBzbc11wOggyzuidhfU/cie7DkuFiyzwhn00YAMjwvhtChHHCeUEGAliqEgWqjWGnHCS0DRPbEFgAiM7SYbtTMYfIiQAYiJCRE1c4YxpyiBho1Y4ixvfPOO1VG9Y8//lA37VdeecXidQwLAaidRMagR48eqlYJmRr87X333adG0UAaMOwhMsHGMJqJpetaCrVU2CZ+GJEmbBPx09gHa/dPyxB06tRJjcKCWHk0u+PHD/tuTnH7hZFa3nvvPVWLhowKRtux5DNM/Z21+1Ma14wlTKW9uFGAzGVq8bmffPKJ/N///V+B9+yRVi1dqDnFKDmWKu6zccwxwg1ahVA4RCgMzjEyVFqNt6XXmqlj6YxjZQsUZlDIwnFAqwQmL0NLVFEQ0vLqq6/K0KFD1chLKGQgtAlDc2JkHtRgW5opR8sXYt2R6cWoZdqISxpk/tFKh5F0UBhC3PucOXOsDg9CIRvhfwhFQj8HFP5QGMDnIg32YMlxsWSfEdaDfca1gFYhXJ8YrcjwukOBAAVJtCahhQf3DlzD2ghtCL9CvxXD4XOJ7IEtAERFQLjJAw88oG72mmeffVb9yCKWW4NaaNTYYMhBDBeHzDo6Ahr+4FuyDjKIWqdWrRDQp08flflGhhe1mog1RaYXP/j4cUIrgbHi1kUnNsPOcIafq0EztGEYAzIX+CFCSwDSj7hiwxovS/bPGDIoyAyihhxjwON/xPQjXMaU4vYLsbP/+c9/VIsAfkhRk2bJZ5j6O1v2x97XjCXnxVTajRmfb+PXgIwUMinIvGLiIUO2HgvjDCAmNTLsAGnJ/lny2YijRgd1vIeCFoamRYbfsLba1uvAGcfKFrjuUBDGZ+N7ge+HYa2xqWMNb775psq8orCE7xaO2V9//aXPgJqDa1zrU4D+BjhmKODhuOJ4LVu2TK1jGP+OghUKABgiFy0y+Fx0VNbi6lEYwN8Y1qoDMtC472hwfpFxRuEK/WlQINMy/5Zuo6j9seS4WLrPs2fPVtc9ZhlGuBPOC647bZ8xhCy+Ewhhw+fgekO/Cy2cDP2IRowYoe/HQWQvPhgKyG5bIyIiMgOdIzF6DjLZpkYEshUyT8iIaTX+yBgis4fOuih4eTp0gkctNWYyJs+BQhLmgkGrFOaiILInFgCIiMitofMkwkIQGoY6LYS/oCYYrUOmZmb1NCwAEJG1GAJERERuDeE2mPkVQ4JiJlWE8qBQ4A2Z/6LCe4iIzGELABERERGRF2ELABERERGRF2EBgIiIiIjIi7AAQERERETkRVgAICIiIiLyIiwAEBERERF5ERYAiIiIiIi8CAsARERERERehAUAIiIiIiIvwgIAEREREZF4j/8Hwm/tBRNpoewAAAAASUVORK5CYII=", "text/plain": [ "
" ] @@ -399,10 +399,10 @@ "id": "cd8bb89e", "metadata": { "execution": { - "iopub.execute_input": "2026-08-10T22:40:45.243918Z", - "iopub.status.busy": "2026-08-10T22:40:45.243857Z", - "iopub.status.idle": "2026-08-10T22:40:51.159747Z", - "shell.execute_reply": "2026-08-10T22:40:51.159322Z" + "iopub.execute_input": "2026-10-28T15:24:56.640131Z", + "iopub.status.busy": "2026-10-28T15:24:56.640070Z", + "iopub.status.idle": "2026-10-28T15:25:02.590152Z", + "shell.execute_reply": "2026-10-28T15:25:02.589785Z" } }, "outputs": [ @@ -442,7 +442,7 @@ " 2.700\n", " 2.525\n", " 93.500\n", - " 0.796\n", + " 0.800\n", " 0.924\n", " \n", " \n", @@ -451,7 +451,7 @@ " 2.618\n", " 2.117\n", " 80.876\n", - " 0.184\n", + " 0.192\n", " 0.960\n", " \n", " \n", @@ -474,8 +474,8 @@ "2 slow 2.554 1.577 61.754 \n", "\n", " 2x2 CI covers truth CS CI covers truth \n", - "0 0.796 0.924 \n", - "1 0.184 0.960 \n", + "0 0.800 0.924 \n", + "1 0.192 0.960 \n", "2 0.004 0.928 " ] }, @@ -541,10 +541,10 @@ "id": "823fbfb7", "metadata": { "execution": { - "iopub.execute_input": "2026-08-10T22:40:51.160706Z", - "iopub.status.busy": "2026-08-10T22:40:51.160635Z", - "iopub.status.idle": "2026-08-10T22:40:53.156527Z", - "shell.execute_reply": "2026-08-10T22:40:53.156172Z" + "iopub.execute_input": "2026-10-28T15:25:02.591424Z", + "iopub.status.busy": "2026-10-28T15:25:02.591359Z", + "iopub.status.idle": "2026-10-28T15:25:04.490809Z", + "shell.execute_reply": "2026-10-28T15:25:04.490432Z" } }, "outputs": [ @@ -552,7 +552,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "True lift 0.5 (~5%), 50 states, moderate rollout: 2x2 power = 0.90 CS power = 0.75\n" + "True lift 0.5 (~5%), 50 states, moderate rollout: 2x2 power = 0.89 CS power = 0.75\n" ] } ], @@ -577,16 +577,16 @@ "id": "5f8e0c4b", "metadata": { "execution": { - "iopub.execute_input": "2026-08-10T22:40:53.157604Z", - "iopub.status.busy": "2026-08-10T22:40:53.157541Z", - "iopub.status.idle": "2026-08-10T22:41:27.624583Z", - "shell.execute_reply": "2026-08-10T22:41:27.624220Z" + "iopub.execute_input": "2026-10-28T15:25:04.492028Z", + "iopub.status.busy": "2026-10-28T15:25:04.491963Z", + "iopub.status.idle": "2026-10-28T15:25:39.729844Z", + "shell.execute_reply": "2026-10-28T15:25:39.729485Z" } }, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -629,27 +629,27 @@ " fast\n", " 10\n", " 93.500\n", - " 0.370\n", + " 0.373\n", " 0.562\n", - " 1.521\n", + " 1.508\n", " \n", " \n", " 1\n", " moderate\n", " 14\n", " 80.876\n", - " 0.437\n", + " 0.441\n", " 0.543\n", - " 1.243\n", + " 1.232\n", " \n", " \n", " 2\n", " slow\n", " 20\n", " 61.754\n", - " 0.601\n", + " 0.606\n", " 0.578\n", - " 0.961\n", + " 0.953\n", " \n", " \n", "\n", @@ -657,14 +657,14 @@ ], "text/plain": [ " rollout all live by wk 2x2 reports (% of truth) MDE 2x2 MDE CS \\\n", - "0 fast 10 93.500 0.370 0.562 \n", - "1 moderate 14 80.876 0.437 0.543 \n", - "2 slow 20 61.754 0.601 0.578 \n", + "0 fast 10 93.500 0.373 0.562 \n", + "1 moderate 14 80.876 0.441 0.543 \n", + "2 slow 20 61.754 0.606 0.578 \n", "\n", " CS / 2x2 MDE \n", - "0 1.521 \n", - "1 1.243 \n", - "2 0.961 " + "0 1.508 \n", + "1 1.232 \n", + "2 0.953 " ] }, "execution_count": 7, @@ -760,10 +760,10 @@ "id": "9a902d30", "metadata": { "execution": { - "iopub.execute_input": "2026-08-10T22:41:27.625613Z", - "iopub.status.busy": "2026-08-10T22:41:27.625554Z", - "iopub.status.idle": "2026-08-10T22:41:28.227131Z", - "shell.execute_reply": "2026-08-10T22:41:28.226735Z" + "iopub.execute_input": "2026-10-28T15:25:39.731346Z", + "iopub.status.busy": "2026-10-28T15:25:39.731284Z", + "iopub.status.idle": "2026-10-28T15:25:40.315036Z", + "shell.execute_reply": "2026-10-28T15:25:40.314661Z" } }, "outputs": [ @@ -807,10 +807,10 @@ "id": "34cfb285", "metadata": { "execution": { - "iopub.execute_input": "2026-08-10T22:41:28.228106Z", - "iopub.status.busy": "2026-08-10T22:41:28.228036Z", - "iopub.status.idle": "2026-08-10T22:41:40.367042Z", - "shell.execute_reply": "2026-08-10T22:41:40.366647Z" + "iopub.execute_input": "2026-10-28T15:25:40.316293Z", + "iopub.status.busy": "2026-10-28T15:25:40.316231Z", + "iopub.status.idle": "2026-10-28T15:25:52.443766Z", + "shell.execute_reply": "2026-10-28T15:25:52.443183Z" } }, "outputs": [ @@ -819,9 +819,9 @@ "output_type": "stream", "text": [ "Flat effects, clean all-treated tail, moderate rollout, 50 states:\n", - " lift 0.20 (~2%): clean-tail 2x2 power 0.25 CS power 0.15\n", - " lift 0.30 (~3%): clean-tail 2x2 power 0.41 CS power 0.21\n", - " lift 0.40 (~4%): clean-tail 2x2 power 0.61 CS power 0.37\n", + " lift 0.20 (~2%): clean-tail 2x2 power 0.24 CS power 0.15\n", + " lift 0.30 (~3%): clean-tail 2x2 power 0.40 CS power 0.21\n", + " lift 0.40 (~4%): clean-tail 2x2 power 0.60 CS power 0.37\n", " lift 0.50 (~5%): clean-tail 2x2 power 0.78 CS power 0.54\n", " lift 0.65 (~6%): clean-tail 2x2 power 0.95 CS power 0.71\n", " lift 0.85 (~8%): clean-tail 2x2 power 1.00 CS power 0.94\n", diff --git a/docs/v4-deprecations.yaml b/docs/v4-deprecations.yaml index 490a49647..69c77c8ba 100644 --- a/docs/v4-deprecations.yaml +++ b/docs/v4-deprecations.yaml @@ -79,12 +79,13 @@ rows: new: null deprecated_in: "4.0" removed_in: null - status: planned + status: done phase: 5 old_default: "'residual'" new_default: "'cluster'" code_refs: [diff_diff/estimators.py, diff_diff/linalg.py] - notes: "Locked #663 direction; moves every clustered p-value/CI. Migration note + flip TestDfConvention expectations." + test_ref: tests/test_v4_inference_defaults.py + notes: "DONE at the 4.0 cut (item 7a, 2026-10-28): default flipped from 'residual' to 'cluster' (t(G-1) on clustered analytical fits; df_convention='residual' restores the 3.x inference; point estimates, SEs and t-statistics never move); pinned by tests/test_v4_inference_defaults.py. Original: Locked #663 direction; moves every clustered p-value/CI. Migration note + flip TestDfConvention expectations." - id: M-005 kind: default-flip group: df-convention-flip @@ -92,12 +93,13 @@ rows: new: null deprecated_in: "4.0" removed_in: null - status: planned + status: done phase: 5 old_default: "'residual'" new_default: "'cluster'" code_refs: [diff_diff/twfe.py, diff_diff/linalg.py] - notes: "MultiPeriodDiD's knob is subsumed by its class removal [M-010]; merged event-study mode inherits this row." + test_ref: tests/test_v4_inference_defaults.py + notes: "DONE at the 4.0 cut (item 7a, 2026-10-28): default flipped from 'residual' to 'cluster' (t(G-1) on clustered analytical fits; df_convention='residual' restores the 3.x inference; point estimates, SEs and t-statistics never move); pinned by tests/test_v4_inference_defaults.py. Original: MultiPeriodDiD's knob is subsumed by its class removal [M-010]; merged event-study mode inherits this row." - id: M-006 kind: default-flip group: df-convention-flip @@ -105,12 +107,13 @@ rows: new: null deprecated_in: "4.0" removed_in: null - status: planned + status: done phase: 5 old_default: "'residual'" new_default: "'cluster'" code_refs: [diff_diff/linalg.py] - notes: "The 'evaluate extending the knob to standalone CR1-t estimators' item was resolved EARLY by PR C ([M-127], 3.9): the knob now lives on SunAbraham/WooldridgeDiD/StackedDiD/ImputationDiD (their flips are [M-128]..[M-131]) and LPDiD (already at 'cluster', no flip row)." + test_ref: tests/test_v4_inference_defaults.py + notes: "DONE at the 4.0 cut (item 7a, 2026-10-28): default flipped from 'residual' to 'cluster' (t(G-1) on clustered analytical fits; df_convention='residual' restores the 3.x inference; point estimates, SEs and t-statistics never move); pinned by tests/test_v4_inference_defaults.py. Original: The 'evaluate extending the knob to standalone CR1-t estimators' item was resolved EARLY by PR C ([M-127], 3.9): the knob now lives on SunAbraham/WooldridgeDiD/StackedDiD/ImputationDiD (their flips are [M-128]..[M-131]) and LPDiD (already at 'cluster', no flip row)." - id: M-007 kind: warning-retirement group: obligation-warning-retirements @@ -1555,12 +1558,13 @@ rows: new: null deprecated_in: "4.0" removed_in: null - status: planned + status: done phase: 5 old_default: "'residual'" new_default: "'cluster'" code_refs: [diff_diff/sun_abraham.py] - notes: "PR C ([M-127]) extended the knob here with default 'residual'; flips with [M-004]..[M-006] at 4.0 (G-1 = the fixest/Stata clustered convention). Moves every clustered p-value/CI on this surface; covered by the same migration note." + test_ref: tests/test_v4_inference_defaults.py + notes: "DONE at the 4.0 cut (item 7a, 2026-10-28): default flipped from 'residual' to 'cluster' (t(G-1) on clustered analytical fits; df_convention='residual' restores the 3.x inference; point estimates, SEs and t-statistics never move); pinned by tests/test_v4_inference_defaults.py. Original: PR C ([M-127]) extended the knob here with default 'residual'; flips with [M-004]..[M-006] at 4.0 (G-1 = the fixest/Stata clustered convention). Moves every clustered p-value/CI on this surface; covered by the same migration note." - id: M-129 kind: default-flip group: df-convention-flip @@ -1568,12 +1572,13 @@ rows: new: null deprecated_in: "4.0" removed_in: null - status: planned + status: done phase: 5 old_default: "'residual'" new_default: "'cluster'" code_refs: [diff_diff/wooldridge.py] - notes: "PR C ([M-127]) extended the knob here with default 'residual' (OLS arms only; GLM arms are knob-independent); flips with [M-004]..[M-006] at 4.0. Moves every clustered OLS p-value/CI on this surface; covered by the same migration note." + test_ref: tests/test_v4_inference_defaults.py + notes: "DONE at the 4.0 cut (item 7a, 2026-10-28): default flipped from 'residual' to 'cluster' (t(G-1) on clustered analytical fits; df_convention='residual' restores the 3.x inference; point estimates, SEs and t-statistics never move); pinned by tests/test_v4_inference_defaults.py. Original: PR C ([M-127]) extended the knob here with default 'residual' (OLS arms only; GLM arms are knob-independent); flips with [M-004]..[M-006] at 4.0. Moves every clustered OLS p-value/CI on this surface; covered by the same migration note." - id: M-130 kind: default-flip group: df-convention-flip @@ -1581,12 +1586,13 @@ rows: new: null deprecated_in: "4.0" removed_in: null - status: planned + status: done phase: 5 old_default: "'residual'" new_default: "'cluster'" code_refs: [diff_diff/stacked_did.py] - notes: "PR C ([M-127]) extended the knob here with default 'residual'; flips with [M-004]..[M-006] at 4.0. The stacked residual df is typically large (t ~= z), so the flip to G-1 is the materially moving step on this surface; covered by the same migration note." + test_ref: tests/test_v4_inference_defaults.py + notes: "DONE at the 4.0 cut (item 7a, 2026-10-28): default flipped from 'residual' to 'cluster' (t(G-1) on clustered analytical fits; df_convention='residual' restores the 3.x inference; point estimates, SEs and t-statistics never move); pinned by tests/test_v4_inference_defaults.py. Original: PR C ([M-127]) extended the knob here with default 'residual'; flips with [M-004]..[M-006] at 4.0. The stacked residual df is typically large (t ~= z), so the flip to G-1 is the materially moving step on this surface; covered by the same migration note." - id: M-131 kind: default-flip group: df-convention-flip @@ -1594,12 +1600,13 @@ rows: new: null deprecated_in: "4.0" removed_in: null - status: planned + status: done phase: 5 old_default: "'residual'" new_default: "'cluster'" code_refs: [diff_diff/imputation.py] - notes: "PR C ([M-127]) extended the knob here with default 'residual' (pretrends lead regression only; BJS overall inference and the joint pretrend Wald F are knob-independent); flips with [M-004]..[M-006] at 4.0; covered by the same migration note." + test_ref: tests/test_v4_inference_defaults.py + notes: "DONE at the 4.0 cut (item 7a, 2026-10-28): default flipped from 'residual' to 'cluster' (t(G-1) on clustered analytical fits; df_convention='residual' restores the 3.x inference; point estimates, SEs and t-statistics never move); pinned by tests/test_v4_inference_defaults.py. Original: PR C ([M-127]) extended the knob here with default 'residual' (pretrends lead regression only; BJS overall inference and the joint pretrend Wald F are knob-independent); flips with [M-004]..[M-006] at 4.0; covered by the same migration note." # ---- Alias diet (2026-07-31 consolidation-scope decision) ---------------- - id: M-132 diff --git a/docs/v4-design.md b/docs/v4-design.md index 36ac4aaa0..9c9965f6d 100644 --- a/docs/v4-design.md +++ b/docs/v4-design.md @@ -862,8 +862,15 @@ domain vocabulary, not drift. ImputationDiD-pretrends (their 4.0 flips are [M-128]..[M-131]) and LPDiD (default already `"cluster"`, no flip row). The flip moves every clustered p-value/CI; the flip PR updates `TestDfConvention` / - `test_moderate_t_pins_residual_df_convention` / the per-estimator knob - suites' expectations and adds the migration-guide entry. + `test_moderate_t_pins_cluster_df_convention` / the per-estimator knob + suites' expectations and adds the migration-guide entry. (SHIPPED, item + 7a, 2026-10-28: the six signature defaults - TWFE inherits DiD's - read + `"cluster"`; `TestDfConvention`, `test_moderate_t_pins_cluster_df_convention` + and the per-estimator knob suites pin the G-1 default with explicit + `"residual"` baselines; the variance-conventions matrix keeps its + residual-df rows as explicit `"residual"` arms and gains G-1 `_default_df` + twins; the committed oracle lanes that embed residual-df inference name + that convention explicitly. Pinned by `tests/test_v4_inference_defaults.py`.) - **Fast-path default** [M-008]: decided 2026-10-18 (4.0-cut framing, user decision) - evaluated, KEPT OFF. No opt-in telemetry was ever collected in the 3.9 -> 3.13 gap and the golden recapture never ran, so there is no @@ -1199,12 +1206,21 @@ tests-first PR that flips no rows). report schemas gain an additive `design` key under the unshipped DR 3.0 / BR 4.0 numbers. Pinned by `tests/test_v4_unify_ddd.py`.) 7. Inference defaults: the df_convention flips + [M-080] as re-scoped + - the uniform `cluster=False` off-switch. + the uniform `cluster=False` off-switch. (7a SHIPPED 2026-10-28: the + df_convention flips [M-004]..[M-006] + [M-128]..[M-131], pinned by + `tests/test_v4_inference_defaults.py`; 7b = [M-080] pending.) 8. [M-093] sentinel retirement + event-study schema enforcement. 9. [M-090] docs reorganization: Bacon to the diagnostics family, catalogue counts, JOSS prose, REGISTRY archive framing for the removed classes. 10. Release: bump to 4.0.0 (closes the cut window), changelog compile, - migration-guide retitle, due gate green. + migration-guide retitle, due gate green; re-execute (Rust backend) the + tutorials whose clustered-default outputs moved at item 7a and were + deliberately left stale - 01_basic_did, 02_staggered_did, + 04_parallel_trends, 05_honest_did, 07_pretrends_power, + 09_real_world_examples, 11_imputation_did, 13_stacked_did, + 16_wooldridge_etwfe (NOT 16_survey_did: the survey df wins) - plus any + 7b/8/9 movers; 24 is handled in 7a and 23 in 7b (each re-executed there + if its outputs moved). Ordering constraints: 4a before 4b before 4c-i before 4c-ii; 4 before 5 and 6; 5 before 6 (the unified DDD container is born canonical); 7 and 8 independent; 9 after diff --git a/tests/fixtures/README.md b/tests/fixtures/README.md index 54bdf1f2a..ca3d1d55e 100644 --- a/tests/fixtures/README.md +++ b/tests/fixtures/README.md @@ -64,7 +64,11 @@ route was closed at capture): the contract test pins reference MEMBERSHIP against it, so a surface that mis-marks an estimated horizon as reference fails instead of having that row skipped. Re-capturing on a post-removal tree is NOT a substitute: the fit-time route is gone, so -nothing independent remains to disagree with. +nothing independent remains to disagree with. The ImputationDiD +`pretrends_all` lanes (analytic and boot) were captured under the 3.x +`df_convention="residual"` default (lead rows at t(residual df)); since the +4.0 flip to `"cluster"` (item 7a) the contract tests fit those lanes with +`df_convention="residual"` explicitly, so the oracle numbers are untouched. ## `v4_twfe_event_study_oracles.json` - TWFE event-study decoupling oracles (4.0 PR 4c-i) @@ -76,7 +80,12 @@ built its surface through an internal `MultiPeriodDiDResults` and `results_base._from_mpd`, and while `MultiPeriodDiD` still existed as the independent twin of every lane. Per-lane capture SHAs: 11 lanes at `68e383bd` (top-level `provenance`), the two cluster + hc2_bm lanes at `d45d5316` -(`provenance.lane_additions`). Layout: +(`provenance.lane_additions`). The two unit-clustered analytical lanes +(`pooled_cluster_unit`, `within_cluster_unit`) were captured under the 3.x +`df_convention="residual"` default; since the 4.0 flip to `"cluster"` (item +7a) their lane definitions and `echo.twfe_ctor` carry +`"df_convention": "residual"` explicitly - a metadata-only change, no +numeric field was touched. Layout: - `provenance` - capture SHA, `diff_diff.__version__`, weight backend, numpy/scipy/pandas/python versions, plus `lane_additions.` with the diff --git a/tests/fixtures/v4_twfe_event_study_oracles.json b/tests/fixtures/v4_twfe_event_study_oracles.json index fa7223688..74977ffa7 100644 --- a/tests/fixtures/v4_twfe_event_study_oracles.json +++ b/tests/fixtures/v4_twfe_event_study_oracles.json @@ -93,7 +93,7 @@ "echo": { "panel": "main", "survey_label": null, - "twfe_ctor": {"cluster": "unit"}, + "twfe_ctor": {"cluster": "unit", "df_convention": "residual"}, "twfe_fit": { "outcome": "outcome", "post_periods": [4, 5, 6, 7], @@ -715,7 +715,7 @@ "echo": { "panel": "main", "survey_label": null, - "twfe_ctor": {"cluster": "unit"}, + "twfe_ctor": {"cluster": "unit", "df_convention": "residual"}, "twfe_fit": { "outcome": "outcome", "post_periods": [4, 5, 6, 7], diff --git a/tests/helpers/twfe_es_oracle_lanes.py b/tests/helpers/twfe_es_oracle_lanes.py index 8871dd698..0b7b20127 100644 --- a/tests/helpers/twfe_es_oracle_lanes.py +++ b/tests/helpers/twfe_es_oracle_lanes.py @@ -163,9 +163,13 @@ def _within(ctor: Dict[str, Any], survey_label: Optional[str] = None) -> Lane: LANES: Dict[str, Lane] = { "pooled_default": _pooled({}), - "pooled_cluster_unit": _pooled({"cluster": "unit"}), + # The two unit-clustered analytical lanes were captured under the 3.x + # df_convention="residual" default; since the 4.0 flip (G-1 default) + # they pin the captured convention explicitly (metadata-only change - + # the oracle numbers are untouched). + "pooled_cluster_unit": _pooled({"cluster": "unit", "df_convention": "residual"}), "pooled_hc2_bm": _pooled({"vcov_type": "hc2_bm"}), - "within_cluster_unit": _within({"cluster": "unit"}), + "within_cluster_unit": _within({"cluster": "unit", "df_convention": "residual"}), "within_hc2": _within({"vcov_type": "hc2"}), # Cluster + hc2_bm: the CR2 Bell-McCaffrey bypass that no other lane # reaches; captured at d45d5316 (PR 4c-ii, before the core's post-average diff --git a/tests/test_aggregate_contract.py b/tests/test_aggregate_contract.py index 929637959..d2e113ae7 100644 --- a/tests/test_aggregate_contract.py +++ b/tests/test_aggregate_contract.py @@ -3724,7 +3724,12 @@ def test_replicate_overall_row_migration_delta(self): ) def test_pretrends_arm_matches_oracle(self, imputation_panel): - plain = _fit_imputation(imputation_panel, est_kw={"pretrends": True}) + # The oracle was captured under the 3.x df_convention="residual" + # default (lead rows at t(residual df)); the 4.0 default is G-1, so + # the lane names the captured convention explicitly. + plain = _fit_imputation( + imputation_panel, est_kw={"pretrends": True, "df_convention": "residual"} + ) es = plain.aggregate("event_study") _assert_es_matches_oracle(es, _im_oracle("pretrends_all"), **ANALYTIC_TOL, name="imp/pre") assert (np.asarray(es.event_time) < -1).any() # leads present @@ -3741,7 +3746,7 @@ def test_isolation_under_public_field_mutation(self, imputation_panel): object.__setattr__(res, "anticipation", 3) # Imputation-only public config fields the kit snapshots: object.__setattr__(res, "leave_one_out", True) - object.__setattr__(res, "df_convention", "cluster") + object.__setattr__(res, "df_convention", "residual") pd.testing.assert_frame_equal(res.aggregate("event_study").to_dataframe(), base_es) pd.testing.assert_frame_equal(res.aggregate("group").to_dataframe(), base_gr) pd.testing.assert_frame_equal(res.aggregate("simple").to_dataframe(), base_sm) @@ -3786,7 +3791,12 @@ def test_bootstrap_relays_fit_time_surfaces(self, imputation_panel): _assert_bootstrap_simple_relay(res) def test_bootstrap_pretrends_relay_matches_oracle(self, imputation_panel): - res = _fit_imputation(imputation_panel, est_kw={**_IM_BOOT_KW, "pretrends": True}) + # Lead rows stay analytical under bootstrap and were captured under + # the 3.x df_convention="residual" default (see the analytic arm). + res = _fit_imputation( + imputation_panel, + est_kw={**_IM_BOOT_KW, "pretrends": True, "df_convention": "residual"}, + ) es = res.aggregate("event_study") _assert_es_matches_oracle( es, _im_boot_oracle("pretrends_all"), **BOOT_TOL, p_atol=_IM_P_ATOL, name="imp/boot/pre" diff --git a/tests/test_estimators_vcov_type.py b/tests/test_estimators_vcov_type.py index ff752369d..f37a49c34 100644 --- a/tests/test_estimators_vcov_type.py +++ b/tests/test_estimators_vcov_type.py @@ -2265,12 +2265,14 @@ def test_within_event_study_saturated_full_k_df_le_zero_nan_inference(self): class TestDfConvention: - """`df_convention="cluster"` (opt-in Stata/fixest G-1 inference df on - clustered analytical fits; REGISTRY clustered-CR1 inference-df deviation - note). The knob changes ONLY the reference t-distribution for t/p/CI — - never the point estimate, SE, or t-statistic — and sits at the fallback - level of the df resolution (survey df and per-coefficient Bell-McCaffrey - DOF always win). Default stays "residual" until the v4 flip.""" + """`df_convention` (Stata/fixest G-1 inference df on clustered analytical + fits under the 4.0 default "cluster"; "residual" restores the 3.x + residual-df tail; REGISTRY clustered-CR1 inference-df note). The knob + changes ONLY the reference t-distribution for t/p/CI — never the point + estimate, SE, or t-statistic — and sits at the fallback level of the df + resolution (survey df and per-coefficient Bell-McCaffrey DOF always + win). The default flipped from "residual" to "cluster" at 4.0 (rows + M-004/M-005), so every "off" baseline below is an EXPLICIT "residual".""" @staticmethod def _clustered_panel(n_units=50, seed=42): @@ -2295,15 +2297,17 @@ def _clustered_panel(n_units=50, seed=42): ) return pd.DataFrame(rows) - def test_default_residual_is_noop(self): - """Explicit df_convention="residual" is byte-identical to the default.""" + def test_default_cluster_is_g_minus_1(self): + """The 4.0 default: explicit df_convention="cluster" is byte-identical + to the default, the default p-value IS the t(G-1) tail, and the 3.x + residual-df tail is only reachable through explicit "residual".""" from scipy import stats data = self._clustered_panel() r0 = DifferenceInDifferences(cluster="unit").fit( data, outcome="y", treatment="group", post="post", unit="unit" ) - r1 = DifferenceInDifferences(cluster="unit", df_convention="residual").fit( + r1 = DifferenceInDifferences(cluster="unit", df_convention="cluster").fit( data, outcome="y", treatment="group", post="post", unit="unit" ) assert (r0.att, r0.se, r0.t_stat, r0.p_value, r0.conf_int) == ( @@ -2313,17 +2317,25 @@ def test_default_residual_is_noop(self): r1.p_value, r1.conf_int, ) - # and the default really is the residual-df tail, not G-1 - assert abs(r0.p_value - 2 * stats.t.sf(abs(r0.t_stat), 49)) > 1e-4 + G = data["unit"].nunique() + assert r0.inference_df == G - 1 + np.testing.assert_allclose(r0.p_value, 2 * stats.t.sf(abs(r0.t_stat), G - 1), rtol=1e-12) + # and the residual-df tail (the 3.x default) differs measurably + rr = DifferenceInDifferences(cluster="unit", df_convention="residual").fit( + data, outcome="y", treatment="group", post="post", unit="unit" + ) + assert rr.inference_df > G - 1 + assert abs(r0.p_value - rr.p_value) > 1e-4 @pytest.mark.parametrize("estimator_cls", [DifferenceInDifferences]) def test_cluster_convention_matches_g_minus_1(self, estimator_cls): - """Knob on: p/CI follow t(G-1) exactly; att/SE/t unchanged.""" + """Knob on: p/CI follow t(G-1) exactly; att/SE/t unchanged vs the + explicit "residual" baseline (the 3.x default).""" from scipy import stats data = self._clustered_panel() kw = dict(outcome="y", treatment="group", post="post", unit="unit") - r0 = estimator_cls(cluster="unit").fit(data, **kw) + r0 = estimator_cls(cluster="unit", df_convention="residual").fit(data, **kw) r1 = estimator_cls(cluster="unit", df_convention="cluster").fit(data, **kw) assert r0.att == r1.att and r0.se == r1.se and r0.t_stat == r1.t_stat G = data["unit"].nunique() @@ -2342,9 +2354,10 @@ def test_twfe_inherits_knob(self): data = self._clustered_panel() data["treated"] = data["group"] * data["post"] kw = dict(outcome="y", treatment="treated", post="post", unit="unit") - r0 = TwoWayFixedEffects().fit(data, **kw) + r0 = TwoWayFixedEffects(df_convention="residual").fit(data, **kw) r1 = TwoWayFixedEffects(df_convention="cluster").fit(data, **kw) assert r0.se == r1.se and r0.t_stat == r1.t_stat + assert r0.p_value != r1.p_value G = data["unit"].nunique() np.testing.assert_allclose(r1.p_value, 2 * stats.t.sf(abs(r1.t_stat), G - 1), rtol=1e-12) @@ -2363,7 +2376,7 @@ def test_event_study_period_effects_follow_convention(self): spec="pooled", post_periods=[2, 3], ) - m0 = TwoWayFixedEffects(cluster="unit").fit(data, **kw) + m0 = TwoWayFixedEffects(cluster="unit", df_convention="residual").fit(data, **kw) m1 = TwoWayFixedEffects(cluster="unit", df_convention="cluster").fit(data, **kw) G = data["unit"].nunique() for i in np.where(~m1.is_reference)[0]: @@ -2409,7 +2422,7 @@ def test_conley_fits_excluded_from_knob(self): conley_cutoff_km=100.0, conley_lag_cutoff=0, ) - r0 = DifferenceInDifferences(**common).fit(data, **kw) + r0 = DifferenceInDifferences(**common, df_convention="residual").fit(data, **kw) r1 = DifferenceInDifferences(**common, df_convention="cluster").fit(data, **kw) assert (r0.p_value, r0.conf_int) == (r1.p_value, r1.conf_int) @@ -2421,7 +2434,9 @@ def test_bm_dof_precedence_over_knob(self): kw = dict(outcome="y", treatment="group", post="post", unit="unit") with warnings.catch_warnings(): warnings.simplefilter("ignore") - r0 = DifferenceInDifferences(cluster="unit", vcov_type="hc2_bm").fit(data, **kw) + r0 = DifferenceInDifferences( + cluster="unit", vcov_type="hc2_bm", df_convention="residual" + ).fit(data, **kw) r1 = DifferenceInDifferences( cluster="unit", vcov_type="hc2_bm", df_convention="cluster" ).fit(data, **kw) @@ -2578,22 +2593,22 @@ def test_validation_and_transactional_set_params(self): est = DifferenceInDifferences() with pytest.raises(ValueError, match="df_convention"): est.set_params(df_convention="bogus") - assert est.df_convention == "residual" # unchanged after failed set - est.set_params(df_convention="cluster") - assert est.df_convention == "cluster" + assert est.df_convention == "cluster" # the 4.0 default, unchanged after failed set + est.set_params(df_convention="residual") + assert est.df_convention == "residual" def test_get_params_roundtrip(self): - est = DifferenceInDifferences(cluster="unit", df_convention="cluster") + est = DifferenceInDifferences(cluster="unit", df_convention="residual") params = est.get_params() - assert params["df_convention"] == "cluster" + assert params["df_convention"] == "residual" clone = DifferenceInDifferences(**params) - assert clone.df_convention == "cluster" + assert clone.df_convention == "residual" def test_unclustered_fit_knob_is_inert(self): """No cluster -> n_clusters_ is None -> knob has zero effect.""" data = self._clustered_panel() kw = dict(outcome="y", treatment="group", post="post") - r0 = DifferenceInDifferences().fit(data, **kw) + r0 = DifferenceInDifferences(df_convention="residual").fit(data, **kw) r1 = DifferenceInDifferences(df_convention="cluster").fit(data, **kw) assert (r0.p_value, r0.conf_int) == (r1.p_value, r1.conf_int) diff --git a/tests/test_event_study_surface.py b/tests/test_event_study_surface.py index ccfd3cd9c..9867f1ed6 100644 --- a/tests/test_event_study_surface.py +++ b/tests/test_event_study_surface.py @@ -1470,8 +1470,8 @@ def test_stacked_hc2_bm_per_row_df(): if et in bm.event_study_df and np.isfinite(bm.event_study_df[et]): assert df_val == bm.event_study_df[et] - # hc1 non-survey (3.9 / M-127): the df_convention default resolves to - # t(pooled residual df), so every finite-p row now carries that df. + # hc1 non-survey (3.9 / M-127; 4.0 default G-1): the df_convention knob + # resolves one df per fit, so every finite-p row carries that df. s_hc1 = build_event_study_surface(hc1) finite_hc1 = np.isfinite(s_hc1.p_value) assert np.isfinite(s_hc1.df[finite_hc1]).all() diff --git a/tests/test_fixest_did_twfe_parity.py b/tests/test_fixest_did_twfe_parity.py index 311ea43fc..d92cdb8cb 100644 --- a/tests/test_fixest_did_twfe_parity.py +++ b/tests/test_fixest_did_twfe_parity.py @@ -183,8 +183,9 @@ class TestFixestTailDfParity: since it was generated, but nothing asserted them until the tail-df consolidation. Two lanes: - - iid arms under the DEFAULT `df_convention="residual"`: fixest's iid - t-reference is the residual df, so Python matches directly (measured + - iid arms (unclustered, so the `df_convention` knob is inert and the + residual df applies under every value): fixest's iid t-reference is + the residual df, so Python matches directly (measured during plan review: Python residual df == fixest implied df on all four arms — did 396, twfe 148, did_hetero 209, twfe_hetero 125; p/CI agree to ~1e-13 relative). @@ -206,7 +207,7 @@ def _fit(self, key, golden, **est_kw): df, outcome="outcome", treatment="treated", post="post", unit="unit" ) - def test_iid_p_and_ci_match_fixest_under_residual_default(self): + def test_iid_p_and_ci_match_fixest_on_the_knob_inert_iid_lane(self): golden = _load_golden() for key, tol in self._P_TOL.items(): res = self._fit(key, golden, vcov_type="classical") diff --git a/tests/test_imputation.py b/tests/test_imputation.py index 4ccbbcbca..be291688b 100644 --- a/tests/test_imputation.py +++ b/tests/test_imputation.py @@ -3214,16 +3214,26 @@ def _lead(self, res): h = min(k for k in effects if k < -1) return h, effects[h] - def test_leads_are_t_residual_not_z(self): - res = ImputationDiD(pretrends=True).fit(self._panel(), **self._kw) + def test_leads_are_t_g_minus_1_not_z(self): + """The 4.0 default (M-131): the lead rows use t(G-1); explicit + "residual" keeps the 3.9 t(lead-regression residual df).""" + data = self._panel() + res = ImputationDiD(pretrends=True).fit(data, **self._kw) h, e = self._lead(res) assert e["p_value"] != self._z_p(e["t_stat"]) + G = data["unit"].nunique() match = [d for d in range(2, 600) if abs(e["p_value"] - self._t_p(e["t_stat"], d)) < 1e-13] - assert len(match) == 1 + assert match == [G - 1], match + rr = ImputationDiD(pretrends=True, df_convention="residual").fit(data, **self._kw) + _, er = self._lead(rr) + match_r = [ + d for d in range(2, 600) if abs(er["p_value"] - self._t_p(er["t_stat"], d)) < 1e-13 + ] + assert len(match_r) == 1 and match_r[0] > G - 1, match_r def test_cluster_matches_g_minus_1_on_leads_only(self): data = self._panel() - r0 = ImputationDiD(pretrends=True).fit(data, **self._kw) + r0 = ImputationDiD(pretrends=True, df_convention="residual").fit(data, **self._kw) rc = ImputationDiD(pretrends=True, df_convention="cluster").fit(data, **self._kw) h, e0 = self._lead(r0) es0, esc = es_dict(r0), es_dict(rc) @@ -3246,7 +3256,11 @@ def test_normal_reproduces_pre39_z_on_leads(self): def test_pretrend_wald_f_is_knob_independent(self): data = self._panel() - pt0 = ImputationDiD(pretrends=True).fit(data, **self._kw).pretrend_test() + pt0 = ( + ImputationDiD(pretrends=True, df_convention="residual") + .fit(data, **self._kw) + .pretrend_test() + ) ptc = ( ImputationDiD(pretrends=True, df_convention="cluster") .fit(data, **self._kw) @@ -3286,17 +3300,22 @@ def test_inert_config_warns_on_explicit_nondefault(self): data = self._panel() base = dict(outcome="outcome", unit="unit", time="time", first_treat="first_treat") with pytest.warns(UserWarning, match="affects only the pretrends"): - ImputationDiD(df_convention="cluster").fit(data, **base) + ImputationDiD(df_convention="residual").fit(data, **base) + # the default itself never warns (it is not an explicitly non-default value) + with warnings.catch_warnings(record=True) as caught: + warnings.simplefilter("always") + ImputationDiD().fit(data, **base) + assert not any("affects only the pretrends" in str(w.message) for w in caught) # pretrends=True, analytical: reachable post-fit -> no warning. with warnings.catch_warnings(record=True) as caught: warnings.simplefilter("always") - ImputationDiD(pretrends=True, df_convention="cluster").fit(data, **base) + ImputationDiD(pretrends=True, df_convention="residual").fit(data, **base) assert not any("affects only the pretrends" in str(w.message) for w in caught) # pretrends=True + bootstrap: the lead surface is materialized at # fit (analytical lead inference) and relayed -> no warning. with warnings.catch_warnings(record=True) as caught: warnings.simplefilter("always") - ImputationDiD(pretrends=True, df_convention="cluster", n_bootstrap=9, seed=1).fit( + ImputationDiD(pretrends=True, df_convention="residual", n_bootstrap=9, seed=1).fit( data, **base ) assert not any("affects only the pretrends" in str(w.message) for w in caught) @@ -3310,7 +3329,7 @@ def test_inert_config_warns_on_explicit_nondefault(self): weight_type="pweight", ) with pytest.warns(UserWarning, match="affects only the pretrends"): - ImputationDiD(pretrends=True, df_convention="cluster").fit( + ImputationDiD(pretrends=True, df_convention="residual").fit( rep_data, survey_design=design, **base ) @@ -3320,7 +3339,7 @@ def test_validation_and_transactional_set_params(self): est = ImputationDiD() with pytest.raises(ValueError, match="df_convention"): est.set_params(df_convention="bogus", alpha=0.10) - assert est.df_convention == "residual" and est.alpha == 0.05 + assert est.df_convention == "cluster" and est.alpha == 0.05 # Valid value + unknown key: the unknown-key rejection must also # leave the estimator fully unchanged (no partial application). before = est.get_params() diff --git a/tests/test_lpdid.py b/tests/test_lpdid.py index 47f1a1aed..460d434fa 100644 --- a/tests/test_lpdid.py +++ b/tests/test_lpdid.py @@ -1979,8 +1979,9 @@ class TestLPDiDDfConvention: """The three-value df_convention knob on LPDiD (3.9 / M-127). LPDiD's DEFAULT is "cluster" — the pre-existing Stata lpdid t(G-1) - reference — so the default moves NOTHING (unlike the "residual"-default - estimators). "residual"/"normal" are new opt-ins; the degenerate lanes + reference, adopted at 3.9 ahead of the library-wide 4.0 flip — so the + 3.9 knob introduction moved NOTHING here. "residual"/"normal" are + opt-ins; the degenerate lanes (unclustered refit, RA G<=1, saturated early return) keep literal df=None under every value. """ diff --git a/tests/test_methodology_twfe.py b/tests/test_methodology_twfe.py index 4b8d93b51..02aecf4de 100644 --- a/tests/test_methodology_twfe.py +++ b/tests/test_methodology_twfe.py @@ -490,13 +490,12 @@ def test_se_matches_r_twfe(self, r_twfe_results, r_benchmark_panel_data): def test_pvalue_matches_r_twfe(self, r_twfe_results, r_benchmark_panel_data): """Both p-values are numerically zero at this effect size. - Python p ~1e-48 (t-dist at residual df=148) vs R ~4e-27 (fixest's - cluster df G-1=49): both sides use t-distributions, but the df - conventions differ — the documented clustered-CR1 inference-df - deviation (REGISTRY §TwoWayFixedEffects) — so at |t|~22 the tails - diverge by ~21 orders and a relative comparison is meaningless. - The assert pins that both are numerically zero (atol=1e-12, - ~15 orders of headroom above the larger tail).""" + Since the 4.0 df_convention flip both sides sit on fixest's cluster + df G-1=49 (Python ~4e-27 like R; under the 3.x residual-df default + it was ~1e-48 at t(148) - the convention itself is pinned by + test_moderate_t_pins_cluster_df_convention below, where the tails + are distinguishable). The assert pins that both are numerically + zero (atol=1e-12).""" data, _ = r_benchmark_panel_data py_results = self._run_python_twfe(data) @@ -508,7 +507,7 @@ def test_pvalue_matches_r_twfe(self, r_twfe_results, r_benchmark_panel_data): err_msg=f"P-value mismatch: Python={py_results.p_value:.6f}, R={r_twfe_results['p_value']:.6f}", ) - def test_moderate_t_pins_residual_df_convention(self): + def test_moderate_t_pins_cluster_df_convention(self): """Locks WHICH df convention clustered-CR1 inference follows, in a regime where the conventions are distinguishable. @@ -516,12 +515,12 @@ def test_moderate_t_pins_residual_df_convention(self): and cluster-df (G-1) p-values are numerically zero, so they cannot tell the conventions apart. This fixture is tuned to a moderate |t|~1.8, where t(residual df=148) and t(G-1=49) p-values differ by - ~5% relative: the estimator's p-value must match the residual-df - tail exactly AND differ measurably from the cluster-df tail — the - documented clustered-CR1 inference-df deviation (REGISTRY - §TwoWayFixedEffects). If the default convention ever flips to - cluster df (the planned opt-in knob's v4 default), this test fails - loudly and must be updated alongside the REGISTRY note.""" + ~5% relative: since the 4.0 flip (rows M-004/M-005) the estimator's + default p-value must match the cluster-df (G-1) tail exactly AND + differ measurably from the residual-df tail, which explicit + df_convention="residual" restores (REGISTRY §TwoWayFixedEffects + clustered-CR1 inference-df note). If the default ever moves again, + this test fails loudly and must be updated alongside that note.""" from scipy import stats as _stats rng = np.random.default_rng(42) @@ -561,27 +560,32 @@ def test_moderate_t_pins_residual_df_convention(self): np.testing.assert_allclose( res.p_value, - p_residual, + p_cluster, rtol=1e-10, - err_msg="clustered-CR1 p-value no longer follows the residual-df convention", + err_msg="clustered-CR1 p-value no longer follows the 4.0 cluster-df (G-1) convention", ) rel_gap = abs(p_cluster - p_residual) / p_residual assert ( rel_gap > 0.03 ), f"fixture no longer distinguishes the df conventions (rel gap {rel_gap:.4f})" assert ( - abs(res.p_value - p_cluster) / p_cluster > 0.03 - ), "p-value matches the cluster-df convention — the documented deviation flipped" + abs(res.p_value - p_residual) / p_residual > 0.03 + ), "p-value matches the residual-df convention — the 4.0 default flipped back" + # explicit "residual" restores the 3.x residual-df tail exactly + res_r = TwoWayFixedEffects(df_convention="residual").fit( + data, outcome="outcome", treatment="treated", post="post", unit="unit" + ) + np.testing.assert_allclose(res_r.p_value, p_residual, rtol=1e-10) def test_ci_matches_r_twfe(self, r_twfe_results, r_benchmark_panel_data): - """CI bounds within the remaining df-convention band (measured ~1.7e-3). - - Post-K_reference the SE side matches fixest at machine precision, so - the bounds' residual gap through att ± crit*se is SOLELY the clustered - inference-df convention (Python t(residual df=148) critical value vs - fixest t(G−1=49); REGISTRY §TwoWayFixedEffects — PR C scope). - Tightened 0.005 → 0.003 accordingly (measured 1.7e-3/1.4e-3). The - ATT itself matches at machine precision.""" + """CI bounds match fixest at machine precision since the 4.0 flip. + + Post-K_reference the SE side matches fixest at machine precision, and + since the df_convention default flipped to "cluster" (M-005, item 7a) + the critical value is the same t(G−1=49) quantile fixest uses, so the + bounds through att ± crit*se agree to rounding (the 3.x residual-df + default left a ~1.7e-3 / 1.4e-3 band here, which is what the old + rtol=0.003 absorbed). The ATT itself matches at machine precision.""" data, _ = r_benchmark_panel_data py_results = self._run_python_twfe(data) @@ -589,13 +593,13 @@ def test_ci_matches_r_twfe(self, r_twfe_results, r_benchmark_panel_data): np.testing.assert_allclose( py_results.conf_int[0], r_twfe_results["ci_lower"], - rtol=0.003, + rtol=1e-9, err_msg=f"CI lower mismatch: Python={py_results.conf_int[0]:.6f}, R={r_twfe_results['ci_lower']:.6f}", ) np.testing.assert_allclose( py_results.conf_int[1], r_twfe_results["ci_upper"], - rtol=0.003, + rtol=1e-9, err_msg=f"CI upper mismatch: Python={py_results.conf_int[1]:.6f}, R={r_twfe_results['ci_upper']:.6f}", ) diff --git a/tests/test_stacked_did.py b/tests/test_stacked_did.py index 687379c55..8b9c25770 100644 --- a/tests/test_stacked_did.py +++ b/tests/test_stacked_did.py @@ -1964,18 +1964,30 @@ def _z_p(t_stat): return 2 * stats.norm.sf(abs(t_stat)) - def test_residual_default_is_t_with_pinned_df(self): - """The defect fix: hc1 lane moved z -> t(pooled residual df). Assert - the RESOLVED df value directly (a p-movement assertion is vacuous on - stacked designs: the residual df is large, t ~= z).""" + def test_default_is_t_g_minus_1_with_pinned_df(self): + """The 4.0 default (M-130): the hc1 lane uses t(G-1). Assert the + RESOLVED df value directly (a p-movement assertion is vacuous on + stacked designs for the residual arm: that df is large, t ~= z).""" res = StackedDiD(kappa_pre=2, kappa_post=2).fit(self._panel(), **self._kw) + assert res.inference_df == res.n_clusters - 1 + assert res.p_value == pytest.approx(self._t_p(res.t_stat, res.n_clusters - 1), rel=1e-12) + finite = {v for v in res.event_study_df.values() if np.isfinite(v)} + assert finite == {float(res.n_clusters - 1)} + + def test_residual_is_t_with_pinned_df(self): + """The 3.9 defect fix, now behind explicit "residual": the hc1 lane + uses t(pooled residual df), asserted through the RESOLVED df value + (that df is large, so t ~= z and a p-movement assertion is vacuous).""" + res = StackedDiD(kappa_pre=2, kappa_post=2, df_convention="residual").fit( + self._panel(), **self._kw + ) dfres = res.inference_df assert dfres is not None and dfres > res.n_clusters assert res.p_value == pytest.approx(self._t_p(res.t_stat, dfres), rel=1e-12) finite = {v for v in res.event_study_df.values() if np.isfinite(v)} assert finite == {dfres} - def test_small_n_residual_default_moves_off_z(self): + def test_small_n_default_moves_off_z(self): """On a SMALL stacked design the z -> t movement is measurable.""" res = StackedDiD(kappa_pre=1, kappa_post=1).fit(self._panel(n_units=12), **self._kw) assert np.isfinite(res.p_value) @@ -1983,7 +1995,7 @@ def test_small_n_residual_default_moves_off_z(self): def test_cluster_matches_g_minus_1_effective_count(self): data = self._panel() - r0 = StackedDiD(kappa_pre=2, kappa_post=2).fit(data, **self._kw) + r0 = StackedDiD(kappa_pre=2, kappa_post=2, df_convention="residual").fit(data, **self._kw) r1 = StackedDiD(kappa_pre=2, kappa_post=2, df_convention="cluster").fit(data, **self._kw) G = r1.n_clusters assert r1.att == r0.att and r1.se == r0.se @@ -2020,7 +2032,9 @@ def test_normal_reproduces_pre39_z(self): def test_bm_dof_precedence_over_knob(self): data = self._panel() - r_res = StackedDiD(kappa_pre=2, kappa_post=2, vcov_type="hc2_bm").fit(data, **self._kw) + r_res = StackedDiD( + kappa_pre=2, kappa_post=2, vcov_type="hc2_bm", df_convention="residual" + ).fit(data, **self._kw) r_clu = StackedDiD( kappa_pre=2, kappa_post=2, vcov_type="hc2_bm", df_convention="cluster" ).fit(data, **self._kw) @@ -2056,7 +2070,7 @@ def test_validation_and_transactional_set_params(self): est = StackedDiD() with pytest.raises(ValueError, match="df_convention"): est.set_params(df_convention="bogus", alpha=0.10) - assert est.df_convention == "residual" and est.alpha == 0.05 + assert est.df_convention == "cluster" and est.alpha == 0.05 # Valid value + unknown key: the unknown-key rejection must also # leave the estimator fully unchanged (no partial application). before = est.get_params() diff --git a/tests/test_sun_abraham.py b/tests/test_sun_abraham.py index 30c3ef82a..2e0539c7a 100644 --- a/tests/test_sun_abraham.py +++ b/tests/test_sun_abraham.py @@ -1565,7 +1565,7 @@ def test_hc2_bm_contrast_dof_helper_failure_falls_back(self): Distinct from the per-row NaN-DOF case above: when the helper RAISES, SA leaves the ``_compute_iw_effects`` output in place after warning. Since 3.9 (M-127) that output carries the df_convention-resolved - analytical df - FINITE t(residual df) under the default, recorded as + analytical df - FINITE t(G-1) under the 4.0 default, recorded as genuine provenance (previously normal theory with NaN provenance). """ import diff_diff.linalg as _linalg @@ -2094,20 +2094,28 @@ def _panel(seed=3): _kw = dict(outcome="outcome", unit="unit", time="time", first_treat="first_treat") - def test_residual_default_is_t_not_z(self): - """The D4/defect fix: aggregates use t(saturated residual df), not z.""" - res = SunAbraham().fit(self._panel(), **self._kw) + def test_default_is_t_g_minus_1_not_z(self): + """The 4.0 default: aggregates use t(G-1) (M-128), not z and not the + residual df; explicit "residual" keeps the D4-fix t(saturated + residual df).""" + data = self._panel() + res = SunAbraham().fit(data, **self._kw) p_z = 2 * stats.norm.sf(abs(res.t_stat)) assert res.p_value != p_z - dfres = res.inference_df - assert dfres is not None and dfres > 0 - assert res.p_value == pytest.approx(2 * stats.t.sf(abs(res.t_stat), dfres), rel=1e-12) - # every ES row shares the same finite residual df (one df source) - assert set(res.event_study_df.values()) == {dfres} + G = data["unit"].nunique() + assert res.inference_df == G - 1 + assert res.p_value == pytest.approx(2 * stats.t.sf(abs(res.t_stat), G - 1), rel=1e-12) + # every ES row shares the same G-1 df (one df source) + assert set(res.event_study_df.values()) == {float(G - 1)} + rr = SunAbraham(df_convention="residual").fit(data, **self._kw) + dfres = rr.inference_df + assert dfres is not None and dfres > G - 1 + assert rr.p_value == pytest.approx(2 * stats.t.sf(abs(rr.t_stat), dfres), rel=1e-12) + assert set(rr.event_study_df.values()) == {dfres} def test_cluster_matches_hand_reconstructed_g_minus_1(self): data = self._panel() - r0 = SunAbraham().fit(data, **self._kw) + r0 = SunAbraham(df_convention="residual").fit(data, **self._kw) r1 = SunAbraham(df_convention="cluster").fit(data, **self._kw) G = data["unit"].nunique() assert r1.att == r0.att and r1.se == r0.se @@ -2134,7 +2142,7 @@ def test_normal_reproduces_z_on_aggregates(self): def test_bm_dof_precedence_over_knob(self): """hc2_bm BM contrast DOF wins under every knob value.""" data = self._panel() - r_res = SunAbraham(vcov_type="hc2_bm").fit(data, **self._kw) + r_res = SunAbraham(vcov_type="hc2_bm", df_convention="residual").fit(data, **self._kw) r_clu = SunAbraham(vcov_type="hc2_bm", df_convention="cluster").fit(data, **self._kw) assert r_res.p_value == r_clu.p_value assert r_res.conf_int == r_clu.conf_int @@ -2174,7 +2182,7 @@ def test_validation_and_transactional_set_params(self): est = SunAbraham() with pytest.raises(ValueError, match="df_convention"): est.set_params(df_convention="bogus", alpha=0.10) - assert est.df_convention == "residual" and est.alpha == 0.05 + assert est.df_convention == "cluster" and est.alpha == 0.05 # Valid value + unknown key: the unknown-key rejection must also # leave the estimator fully unchanged (no partial application). before = est.get_params() @@ -2183,15 +2191,15 @@ def test_validation_and_transactional_set_params(self): assert est.get_params() == before def test_get_params_roundtrip_and_repeat_fit(self): - est = SunAbraham(df_convention="cluster") - assert est.get_params()["df_convention"] == "cluster" + est = SunAbraham(df_convention="residual") + assert est.get_params()["df_convention"] == "residual" clone = SunAbraham(**est.get_params()) data = self._panel() r1 = clone.fit(data, **self._kw) p1, d1 = r1.p_value, r1.inference_df r2 = clone.fit(data, **self._kw) assert r2.p_value == p1 and r2.inference_df == d1 - assert clone.df_convention == "cluster" + assert clone.df_convention == "residual" @pytest.fixture(scope="module") diff --git a/tests/test_t24_staggered_vs_collapsed_power_drift.py b/tests/test_t24_staggered_vs_collapsed_power_drift.py index 9744ceda8..286e82e3a 100644 --- a/tests/test_t24_staggered_vs_collapsed_power_drift.py +++ b/tests/test_t24_staggered_vs_collapsed_power_drift.py @@ -231,12 +231,12 @@ def test_rendered_surface_quotes(): "61.754", "94% / 80% / 61%", "~60% of the true lift", - "0.796", # 2x2 coverage, fast + "0.800", # 2x2 coverage, fast (4.0 df_convention flip: t(G-1) CIs; was 0.796) "0.004", # 2x2 coverage, slow ("almost never") "0.924", # CS coverage, fast (~95% regardless of speed) "~81%", # "2x2 reports ~81% of CS's number" # Section 2 headline: paired power + the MDE crossover prose - "2x2 power = 0.90", + "2x2 power = 0.89", # 4.0 df_convention flip (was 0.90 under residual df) "CS power = 0.75", "~0.37", # 2x2 MDE, fast "~0.60", # 2x2 MDE, slow (climbs) diff --git a/tests/test_v4_inference_defaults.py b/tests/test_v4_inference_defaults.py new file mode 100644 index 000000000..665650c61 --- /dev/null +++ b/tests/test_v4_inference_defaults.py @@ -0,0 +1,394 @@ +"""Item 7a of the 4.0 cut: the ``df_convention`` default flip. + +Ledger rows M-004 (DifferenceInDifferences), M-005 (TwoWayFixedEffects), +M-006 (LinearRegression), M-128 (SunAbraham), M-129 (WooldridgeDiD), M-130 +(StackedDiD), M-131 (ImputationDiD): the analytical tail-df convention for +CLUSTERED fits flipped from ``"residual"`` (the fit's ``n - K_full``) to +``"cluster"`` (the Stata/fixest ``G - 1``). LPDiD adopted ``"cluster"`` at 3.9 +and has no row. + +What moves: p-values and confidence intervals on clustered analytical fits +(TWFE / SunAbraham / WooldridgeDiD-OLS / StackedDiD / ImputationDiD-pretrends +cluster at unit BY DEFAULT, so their default fits move; plain +DifferenceInDifferences moves only with an explicit ``cluster=``). What does +not: point estimates, SEs and t-statistics; survey/replicate df and hc2_bm +Bell-McCaffrey DOF (they keep precedence); bootstrap percentile p/CIs; +unclustered and Conley fits (the knob is inert without a cluster count). +``df_convention="residual"`` restores the 3.x inference bit-for-bit. + +Every G-1 pin below takes G from the surface's own clustered sample: +``data[unit].nunique()`` for DiD / TWFE / SunAbraham (their results carry no +analytical ``n_clusters``), ``res.n_clusters`` for WooldridgeDiD and +StackedDiD, and the lead regression's untreated sample for the ImputationDiD +leads (every unit of the shared panel has untreated rows, so that count is +the panel's unit count too). +""" + +from __future__ import annotations + +import inspect +import warnings + +import numpy as np +import pandas as pd +import pytest +from scipy import stats + +from diff_diff import ( + DifferenceInDifferences, + ImputationDiD, + LPDiD, + StackedDiD, + SunAbraham, + SurveyDesign, + SyntheticDiD, + TwoWayFixedEffects, + WooldridgeDiD, + generate_staggered_data, +) +from diff_diff.linalg import LinearRegression + +# The ledger is restricted YAML; the matrix gate parses it without pyyaml (not a +# test dependency) and exposes the parsed rows. +from tests.test_v4_matrix import ROWS as LEDGER_ROWS + +THIS_TEST_REF = "tests/test_v4_inference_defaults.py" + +FLIP_ROWS = { + "M-004": DifferenceInDifferences, + "M-005": TwoWayFixedEffects, + "M-006": LinearRegression, + "M-128": SunAbraham, + "M-129": WooldridgeDiD, + "M-130": StackedDiD, + "M-131": ImputationDiD, +} +# LinearRegression is a plain class (no BaseEstimator mixin): no get/set_params. +BASE_ESTIMATOR_KNOB_CLASSES = [ + DifferenceInDifferences, + TwoWayFixedEffects, + SunAbraham, + WooldridgeDiD, + StackedDiD, + ImputationDiD, + LPDiD, +] + + +def t_tail(t_stat, df): + return 2 * stats.t.sf(abs(t_stat), df) + + +def z_tail(t_stat): + return 2 * stats.norm.sf(abs(t_stat)) + + +@pytest.fixture(scope="module") +def panel(): + """60 units x 8 periods, cohorts {never, 4, 6}; every unit has untreated + rows (treatment starts at period 4 at the earliest).""" + rng = np.random.default_rng(7) + rows = [] + for u in range(60): + ft = [0, 4, 6][u % 3] + lat, lon = 40.0 + 0.05 * (u // 6), -100.0 + 0.05 * (u % 6) + for t in range(1, 9): + eff = 0.6 if (ft and t >= ft) else 0.0 + rows.append( + dict( + unit=u, + time=t, + first_treat=ft, + grp=int(ft > 0), + post=int(t >= 4), + outcome=0.3 * u / 60 + 0.1 * t + eff + rng.standard_normal() * 0.6, + lat=lat, + lon=lon, + ) + ) + df = pd.DataFrame(rows) + df["d"] = df["grp"] * df["post"] + return df + + +SA_KW = dict(outcome="outcome", unit="unit", time="time", first_treat="first_treat") + + +# --------------------------------------------------------------------------- # +# (1) Signature / params pins +# --------------------------------------------------------------------------- # + + +class TestSignatureDefaults: + @pytest.mark.parametrize("cls", list(FLIP_ROWS.values()) + [LPDiD], ids=lambda c: c.__name__) + def test_signature_default_is_cluster(self, cls): + default = inspect.signature(cls.__init__).parameters["df_convention"].default + assert default == "cluster" + + @pytest.mark.parametrize("cls", BASE_ESTIMATOR_KNOB_CLASSES, ids=lambda c: c.__name__) + def test_get_params_reads_cluster_and_set_params_round_trips(self, cls): + est = cls() + assert est.get_params()["df_convention"] == "cluster" + for value in ("residual", "normal", "cluster"): + est.set_params(df_convention=value) + assert est.get_params()["df_convention"] == value + assert cls(**est.get_params()).df_convention == value + + def test_synthetic_did_inherits_the_flip_inertly(self): + """SyntheticDiD subclasses DifferenceInDifferences and never reads the + knob (placebo / bootstrap / jackknife variance); the inherited + attribute flips with the base class and is NOT a SyntheticDiD + parameter.""" + sdid = SyntheticDiD() + assert sdid.df_convention == "cluster" + assert "df_convention" not in sdid.get_params() + + +# --------------------------------------------------------------------------- # +# (2) Default fits sit on t(G-1); "residual" restores the 3.x tail +# --------------------------------------------------------------------------- # + + +class TestDefaultFitsUseClusterDf: + def test_did_explicit_cluster(self, panel): + G = panel["unit"].nunique() + kw = dict(outcome="outcome", treatment="grp", post="post", unit="unit") + res = DifferenceInDifferences(cluster="unit").fit(panel, **kw) + assert res.inference_df == G - 1 + np.testing.assert_allclose(res.p_value, t_tail(res.t_stat, G - 1), rtol=1e-12) + rr = DifferenceInDifferences(cluster="unit", df_convention="residual").fit(panel, **kw) + assert rr.inference_df > G - 1 # n - K_full + assert (rr.att, rr.se, rr.t_stat) == (res.att, res.se, res.t_stat) + assert rr.p_value != res.p_value + np.testing.assert_allclose(rr.p_value, t_tail(rr.t_stat, rr.inference_df), rtol=1e-12) + + def test_twfe_static_auto_cluster(self, panel): + G = panel["unit"].nunique() + kw = dict(outcome="outcome", treatment="d", post="post", unit="unit") + res = TwoWayFixedEffects().fit(panel, **kw) + assert res.inference_df == G - 1 + np.testing.assert_allclose(res.p_value, t_tail(res.t_stat, G - 1), rtol=1e-12) + rr = TwoWayFixedEffects(df_convention="residual").fit(panel, **kw) + assert rr.se == res.se and rr.t_stat == res.t_stat and rr.p_value != res.p_value + + @pytest.mark.parametrize("spec", ["within", "pooled"]) + def test_twfe_event_study_auto_cluster(self, panel, spec): + """Both specs auto-cluster at unit when ``unit=`` is passed (a pooled + fit WITHOUT ``unit=`` is unclustered and the knob is inert there - + that is the committed ``pooled_default`` oracle lane).""" + G = panel["unit"].nunique() + kw = dict( + outcome="outcome", + treatment="grp", + unit="unit", + time="time", + event_study=True, + spec=spec, + post_periods=[4, 5, 6, 7, 8], + reference_period=3, + ) + res = TwoWayFixedEffects().fit(panel, **kw) + est = ~res.is_reference + assert est.sum() == 7 # periods 1, 2 (pre) + 4..8 (post) + np.testing.assert_array_equal(res.df[est], float(G - 1)) + np.testing.assert_allclose( + res.p_value[est], t_tail(res.att[est] / res.se[est], G - 1), rtol=1e-12 + ) + rr = TwoWayFixedEffects(df_convention="residual").fit(panel, **kw) + np.testing.assert_array_equal(rr.se, res.se) + assert (rr.df[est] > G - 1).all() + assert (rr.p_value[est] != res.p_value[est]).all() + + def test_sun_abraham(self, panel): + G = panel["unit"].nunique() + res = SunAbraham().fit(panel, **SA_KW) + assert res.inference_df == G - 1 + np.testing.assert_allclose(res.p_value, t_tail(res.t_stat, G - 1), rtol=1e-12) + assert set(res.event_study_df.values()) == {float(G - 1)} + # every event-study row (the cohort x event-time cells keep only + # coefficient/SE on the public surface) + for eff in res.event_study_effects.values(): + np.testing.assert_allclose(eff["p_value"], t_tail(eff["t_stat"], G - 1), rtol=1e-12) + rr = SunAbraham(df_convention="residual").fit(panel, **SA_KW) + assert rr.inference_df > G - 1 and rr.se == res.se and rr.p_value != res.p_value + + def test_wooldridge_ols(self, panel): + res = WooldridgeDiD(method="ols").fit(panel, **SA_KW) + G = res.n_clusters + assert G == panel["unit"].nunique() + np.testing.assert_allclose(res.p_value, t_tail(res.t_stat, G - 1), rtol=1e-12) + for eff in res.group_time_effects.values(): + np.testing.assert_allclose(eff["p_value"], t_tail(eff["t_stat"], G - 1), rtol=1e-12) + rr = WooldridgeDiD(method="ols", df_convention="residual").fit(panel, **SA_KW) + assert rr.se == res.se and rr.p_value != res.p_value + assert rr.p_value != t_tail(rr.t_stat, G - 1) + + def test_wooldridge_bootstrap_fit_keeps_analytical_lanes_on_g_minus_1(self, panel): + """Only the overall p/CI are percentile-overridden on a bootstrap fit; + post-fit aggregate('group') re-derives ANALYTICAL inference from the + stored fallback, which now carries G-1.""" + res = WooldridgeDiD(method="ols", n_bootstrap=19, seed=1).fit(panel, **SA_KW) + G = res.n_clusters + for ge in res.aggregate("group").group_effects.values(): + np.testing.assert_allclose(ge["p_value"], t_tail(ge["t_stat"], G - 1), rtol=1e-12) + + def test_wooldridge_glm_inert_config_warning_tracks_the_new_default(self, panel): + data = panel.copy() + data["bin"] = (data["outcome"] > data["outcome"].median()).astype(int) + kw = dict(outcome="bin", unit="unit", time="time", first_treat="first_treat") + with warnings.catch_warnings(record=True) as caught: + warnings.simplefilter("always") + WooldridgeDiD(method="logit").fit(data, **kw) + assert not any("no effect on the logit/poisson" in str(w.message) for w in caught) + with pytest.warns(UserWarning, match="no effect on the logit/poisson"): + WooldridgeDiD(method="logit", df_convention="residual").fit(data, **kw) + + def test_stacked_did(self): + data = generate_staggered_data(n_units=60, n_periods=10, seed=9) + kw = dict(outcome="outcome", unit="unit", time="period", first_treat="first_treat") + res = StackedDiD(kappa_pre=2, kappa_post=2).fit(data, **kw) + G = res.n_clusters + assert res.inference_df == G - 1 + np.testing.assert_allclose(res.p_value, t_tail(res.t_stat, G - 1), rtol=1e-12) + assert set(res.event_study_df.values()) == {float(G - 1)} + surface = res.aggregate("event_study") + rows = ~surface.is_reference & np.isfinite(surface.p_value) + assert rows.any() + np.testing.assert_array_equal(surface.df[rows], float(G - 1)) + rr = StackedDiD(kappa_pre=2, kappa_post=2, df_convention="residual").fit(data, **kw) + assert rr.inference_df > G - 1 and rr.se == res.se + + def test_imputation_pretrends_leads(self, panel): + """The lead regression clusters on the untreated sample; every unit of + the panel has untreated rows, so G_lead is the unit count. The + surface's ``df`` column is NaN on plain fits by design (it carries the + survey df only), so the pin goes through the p-value.""" + G_lead = panel.loc[ + panel["time"] < panel["first_treat"].replace(0, np.inf), "unit" + ].nunique() + assert G_lead == panel["unit"].nunique() + res = ImputationDiD(pretrends=True).fit(panel, **SA_KW) + es = res.aggregate("event_study") + leads = (es.event_time < -1) & np.isfinite(es.p_value) + assert leads.sum() >= 2 + np.testing.assert_allclose( + es.p_value[leads], t_tail(es.att[leads] / es.se[leads], G_lead - 1), rtol=1e-12 + ) + assert np.isnan(np.asarray(es.df, dtype=float)[leads]).all() + rr = ImputationDiD(pretrends=True, df_convention="residual").fit(panel, **SA_KW) + es_r = rr.aggregate("event_study") + np.testing.assert_array_equal(es_r.se[leads], es.se[leads]) + assert (es_r.p_value[leads] != es.p_value[leads]).all() + # post rows are BJS (knob-independent) + post = es.event_time >= 0 + np.testing.assert_array_equal(es_r.p_value[post], es.p_value[post]) + + def test_imputation_bootstrap_fit_keeps_analytical_leads_on_g_minus_1(self, panel): + G_lead = panel["unit"].nunique() + res = ImputationDiD(pretrends=True, n_bootstrap=19, seed=1).fit(panel, **SA_KW) + es = res.aggregate("event_study") + leads = (es.event_time < -1) & np.isfinite(es.p_value) + np.testing.assert_allclose( + es.p_value[leads], t_tail(es.att[leads] / es.se[leads], G_lead - 1), rtol=1e-12 + ) + + def test_imputation_inert_config_warning_tracks_the_new_default(self, panel): + with warnings.catch_warnings(record=True) as caught: + warnings.simplefilter("always") + ImputationDiD().fit(panel, **SA_KW) + assert not any("affects only the pretrends" in str(w.message) for w in caught) + with pytest.warns(UserWarning, match="affects only the pretrends"): + ImputationDiD(df_convention="residual").fit(panel, **SA_KW) + + def test_linear_regression_direct(self): + rng = np.random.default_rng(3) + n, G = 120, 12 + X = np.column_stack([np.ones(n), rng.normal(size=(n, 2))]) + y = X @ np.array([1.0, 0.4, 2.0]) + rng.normal(size=n) + cl = np.repeat(np.arange(G), n // G) + reg = LinearRegression(cluster_ids=cl, include_intercept=False).fit(X, y) + inf = reg.get_inference(1) + assert inf.df == G - 1 + np.testing.assert_allclose(inf.p_value, t_tail(inf.t_stat, G - 1), rtol=1e-12) + reg_r = LinearRegression( + cluster_ids=cl, include_intercept=False, df_convention="residual" + ).fit(X, y) + inf_r = reg_r.get_inference(1) + assert inf_r.df == n - 3 and inf_r.se == inf.se and inf_r.p_value != inf.p_value + + +# --------------------------------------------------------------------------- # +# (3) Where the knob is inert, nothing moved +# --------------------------------------------------------------------------- # + + +class TestInertSurfaces: + def test_unclustered_did_is_bit_identical(self, panel): + kw = dict(outcome="outcome", treatment="grp", post="post") + r0 = DifferenceInDifferences().fit(panel, **kw) + rr = DifferenceInDifferences(df_convention="residual").fit(panel, **kw) + assert (r0.p_value, r0.conf_int, r0.inference_df) == ( + rr.p_value, + rr.conf_int, + rr.inference_df, + ) + + def test_conley_twfe_is_bit_identical(self, panel): + kw = dict(outcome="outcome", treatment="d", post="post", unit="unit") + common = dict( + vcov_type="conley", + conley_coords=("lat", "lon"), + conley_cutoff_km=50.0, + conley_lag_cutoff=0, + ) + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + r0 = TwoWayFixedEffects(**common).fit(panel, **kw) + rr = TwoWayFixedEffects(**common, df_convention="residual").fit(panel, **kw) + assert (r0.p_value, r0.conf_int) == (rr.p_value, rr.conf_int) + + def test_survey_df_keeps_precedence(self, panel): + data = panel.copy() + data["w"] = 1.0 + (data["unit"] % 5) * 0.2 + data["stratum"] = data["unit"] % 4 + design = SurveyDesign(weights="w", strata="stratum", psu="unit") + kw = dict(outcome="outcome", treatment="grp", post="post", unit="unit") + r0 = DifferenceInDifferences().fit(data, survey_design=design, **kw) + rr = DifferenceInDifferences(df_convention="residual").fit(data, survey_design=design, **kw) + assert (r0.p_value, r0.conf_int) == (rr.p_value, rr.conf_int) + assert r0.inference_df is not None and r0.inference_df != panel["unit"].nunique() - 1 + + def test_normal_is_deliberate_z(self, panel): + kw = dict(outcome="outcome", treatment="grp", post="post", unit="unit") + r0 = DifferenceInDifferences(cluster="unit").fit(panel, **kw) + rn = DifferenceInDifferences(cluster="unit", df_convention="normal").fit(panel, **kw) + assert rn.se == r0.se and rn.p_value != r0.p_value + np.testing.assert_allclose(rn.p_value, z_tail(rn.t_stat), rtol=1e-14) + + def test_one_effective_cluster_still_raises_at_the_vcov_layer(self): + """Unchanged from 3.x: a weighted CR1 fit with ONE positive-weight + cluster raises before any df is resolved (under every convention).""" + rng = np.random.default_rng(5) + n = 60 + X = np.column_stack([np.ones(n), rng.normal(size=(n, 2))]) + y = X @ np.array([1.0, 0.5, 2.0]) + rng.normal(size=n) + cl = np.repeat(np.arange(3), 20) + w = np.where(cl == 0, 2.0, 0.0) + with pytest.raises(ValueError, match="at least 2 clusters"): + LinearRegression(cluster_ids=cl, include_intercept=False, weights=w).fit(X, y) + + +# --------------------------------------------------------------------------- # +# (4) Ledger +# --------------------------------------------------------------------------- # + + +def test_ledger_rows_are_done_and_point_here(): + rows = {r["id"]: r for r in LEDGER_ROWS} + for rid, cls in FLIP_ROWS.items(): + row = rows[rid] + assert row["kind"] == "default-flip" + assert row["status"] == "done", rid + assert row["test_ref"] == THIS_TEST_REF, rid + assert row["new_default"] == "'cluster'" + assert row["old"].endswith(f":{cls.__name__}[df_convention]"), (rid, row["old"]) diff --git a/tests/test_variance_conventions.py b/tests/test_variance_conventions.py index fa2703b3a..3612769b3 100644 --- a/tests/test_variance_conventions.py +++ b/tests/test_variance_conventions.py @@ -188,57 +188,60 @@ def snapshot(self): ROWS = [ dict( key="did_absorb_hc1_cluster_unit", - fit=lambda df: diff_diff.DifferenceInDifferences(cluster="unit").fit( - df, outcome="y", treatment="grp", post="post", absorb=["unit", "time"] - ), + fit=lambda df: diff_diff.DifferenceInDifferences( + cluster="unit", df_convention="residual" + ).fit(df, outcome="y", treatment="grp", post="post", absorb=["unit", "time"]), cr1_k=(7,), tail_df=(294.0,), status="legitimate", reason=( "K_reference (D2 fixed): k = 2 visible + rank(time given unit) = 5; " - "matches reghdfe/fixest ssc(K.fixef='nested')" + "matches reghdfe/fixest ssc(K.fixef='nested') Explicit df_convention='residual' since the 4.0 default is G-1 (see the _default_df twin)." ), ), dict( key="did_fixed_effects_hc1_cluster_unit", - fit=lambda df: diff_diff.DifferenceInDifferences(cluster="unit").fit( - df, outcome="y", treatment="grp", post="post", fixed_effects=["unit", "time"] - ), + fit=lambda df: diff_diff.DifferenceInDifferences( + cluster="unit", df_convention="residual" + ).fit(df, outcome="y", treatment="grp", post="post", fixed_effects=["unit", "time"]), cr1_k=(7,), tail_df=(294.0,), status="legitimate", reason=( "K_reference (D1 fixed): 66 visible minus the 59 cluster-nested " "unit dummies -> identical SE to did_absorb (documented deviation " - "from a literal explicit-dummy R comparison, which counts all 66)" + "from a literal explicit-dummy R comparison, which counts all 66) Explicit df_convention='residual' since the 4.0 default is G-1 (see the _default_df twin)." ), ), dict( key="did_plain_hc1_cluster_unit", - fit=lambda df: diff_diff.DifferenceInDifferences(cluster="unit").fit( - df, outcome="y", treatment="grp", post="post" - ), + fit=lambda df: diff_diff.DifferenceInDifferences( + cluster="unit", df_convention="residual" + ).fit(df, outcome="y", treatment="grp", post="post"), cr1_k=(4,), tail_df=(356.0,), status="legitimate", - reason="no absorbed FE: visible k is the whole design; nothing is omitted", + reason=( + "no absorbed FE: visible k is the whole design; nothing is omitted." + " Explicit df_convention='residual' since the 4.0 default is G-1 (see the _default_df twin)." + ), ), dict( key="twfe_hc1_cluster_unit_time_post", - fit=lambda df: diff_diff.TwoWayFixedEffects(vcov_type="hc1", cluster="unit").fit( - df, outcome="y", treatment="grp", post="post", unit="unit" - ), + fit=lambda df: diff_diff.TwoWayFixedEffects( + vcov_type="hc1", cluster="unit", df_convention="residual" + ).fit(df, outcome="y", treatment="grp", post="post", unit="unit"), cr1_k=(3,), tail_df=(298.0,), status="legitimate", reason=( "K_reference (D2 fixed): 2 visible + rank(post given unit) = 1; " - "matches fixest cluster arm at rel 0 (committed golden)" + "matches fixest cluster arm at rel 0 (committed golden) Explicit df_convention='residual' since the 4.0 default is G-1 (see the _default_df twin)." ), ), dict( key="wooldridge_hc1_within", - fit=lambda df: diff_diff.WooldridgeDiD(method="ols").fit( + fit=lambda df: diff_diff.WooldridgeDiD(method="ols", df_convention="residual").fit( df, outcome="y", unit="unit", time="time", first_treat="first_treat" ), cr1_k=(15,), @@ -249,12 +252,12 @@ def snapshot(self): "col -> +1 term; jwdid arms at ratio 1.0); tail df converged in " "3.9 (M-127): the default-hc1 arms use t(residual df = n - " "k_kept - absorbed rank) via the df_convention knob (was silent " - "normal theory); G-1 under 'cluster', z under 'normal'" + "normal theory); G-1 under 'cluster', z under 'normal' Explicit df_convention='residual' since the 4.0 default is G-1 (see the _default_df twin)." ), ), dict( key="sun_abraham_hc1", - fit=lambda df: diff_diff.SunAbraham().fit( + fit=lambda df: diff_diff.SunAbraham(df_convention="residual").fit( df, outcome="y", unit="unit", time="time", first_treat="first_treat" ), cr1_k=(21,), @@ -265,12 +268,12 @@ def snapshot(self): "fixest sunab parity ~5e-15); D4 fixed in 3.9 (M-127): cells AND " "aggregates share the saturated fit's residual df under the " "df_convention knob (aggregates previously dropped to normal " - "theory inside the same fit)" + "theory inside the same fit) Explicit df_convention='residual' since the 4.0 default is G-1 (see the _default_df twin)." ), ), dict( key="stacked_did_hc1", - fit=lambda df: diff_diff.StackedDiD().fit( + fit=lambda df: diff_diff.StackedDiD(df_convention="residual").fit( df, outcome="y", unit="unit", time="time", first_treat="first_treat" ), cr1_k=(6,), @@ -285,7 +288,7 @@ def snapshot(self): "pins vcovCR(type='CR1S') at atol=1e-10); tail df converged in " "3.9 (M-127): t(pooled residual df n_eff - k_kept) via the " "df_convention knob (was silent normal theory; stacked rows make " - "the residual df large, so z -> t is a convention alignment)" + "the residual df large, so z -> t is a convention alignment) Explicit df_convention='residual' since the 4.0 default is G-1 (see the _default_df twin)." ), ), dict( @@ -298,13 +301,13 @@ def snapshot(self): status="legitimate", reason=( "L2: G-1 tail df (Stata/fixest convention) — since 3.9 the " - "df_convention='cluster' DEFAULT on LPDiD (bit-identical; the " - "one surface already at the v4 target)" + "df_convention='cluster' DEFAULT on LPDiD (bit-identical; adopted " + "at 3.9, the library-wide default since the 4.0 flip)" ), ), dict( key="twfe_es_within_hc1_cluster_unit", - fit=lambda df: diff_diff.TwoWayFixedEffects(cluster="unit").fit( + fit=lambda df: diff_diff.TwoWayFixedEffects(cluster="unit", df_convention="residual").fit( df, outcome="y", treatment="grp", @@ -324,12 +327,12 @@ def snapshot(self): "TWFE event-study, within spec (4.0 cut, PR 4c-i): K_reference = 6 " "visible (intercept + 5 interactions) + rank(time given the " "absorbed, cluster-nested unit FE) = 5, i.e. 11; the full-dummy form " - "would read 70 visible minus the 59 cluster-nested unit dummies = 11" + "would read 70 visible minus the 59 cluster-nested unit dummies = 11 Explicit df_convention='residual' since the 4.0 default is G-1 (see the _default_df twin)." ), ), dict( key="twfe_es_pooled_hc1_cluster_time", - fit=lambda df: diff_diff.TwoWayFixedEffects(cluster="time").fit( + fit=lambda df: diff_diff.TwoWayFixedEffects(cluster="time", df_convention="residual").fit( df, outcome="y", treatment="grp", @@ -346,7 +349,7 @@ def snapshot(self): "TWFE event-study, pooled spec (4.0 cut, PR 4c-i): the NESTED " "orientation of the built-in period dummies under a time cluster - 12 " "visible minus their rank 5 = 7 (under-subtraction is caught here; the " - "unit-cluster within row catches over-)" + "unit-cluster within row catches over-) Explicit df_convention='residual' since the 4.0 default is G-1 (see the _default_df twin)." ), ), dict( @@ -383,14 +386,16 @@ def snapshot(self): key="imputation_pretrends_event_study", # The pretrends lead regression runs on the post-fit event-study # route (4.0: no fit-time aggregate=), so the row spies fit + aggregate. - fit=lambda df: diff_diff.ImputationDiD(pretrends=True) + fit=lambda df: diff_diff.ImputationDiD(pretrends=True, df_convention="residual") .fit(df, outcome="y", unit="unit", time="time", first_treat="first_treat") .aggregate("event_study"), cr1_k=None, # contract row: shared CR1 REACHED; exact k is config-detail # Pinned since 3.9 (M-127): the three lead calls carry the knob- # resolved residual df (157 = n_untreated - k_kept - absorbed rank - # on the audit panel); the None entries are the knob-INDEPENDENT - # BJS overall/event-study calls (L3) captured by the same spy. + # on the audit panel; explicit 'residual' since the 4.0 default is + # G-1 - see the _default_df twin); the None entries are the + # knob-INDEPENDENT BJS overall/event-study calls (L3) captured by + # the same spy. tail_df=(157.0,) * 3 + (None,) * 5, # The increment IS pinned even though k is not: every clustered-hc1 # call on this surface must carry the [time, unit] no-intercept @@ -540,6 +545,133 @@ def snapshot(self): "no CR1 sandwich, normal-theory safe_inference gate only" ), ), + # ---- 4.0 default twins: identical fits WITHOUT the explicit knob, so the + # tail df is G-1 (G = clusters with positive weight on each surface's + # clustered sample). The cr1_k literals are copied from the residual twins + # (the knob never touches the sandwich). + dict( + key="did_absorb_hc1_cluster_unit_default_df", + fit=lambda df: diff_diff.DifferenceInDifferences(cluster="unit").fit( + df, outcome="y", treatment="grp", post="post", absorb=["unit", "time"] + ), + cr1_k=(7,), + tail_df=(59.0,), + status="legitimate", + reason=( + "t(G-1): the 4.0 df_convention default (M-004..M-006, M-128..M-131) - " + "same CR1 k as the explicit-'residual' twin, tail df G-1" + ), + ), + dict( + key="twfe_hc1_cluster_unit_default_df", + fit=lambda df: diff_diff.TwoWayFixedEffects(vcov_type="hc1", cluster="unit").fit( + df, outcome="y", treatment="grp", post="post", unit="unit" + ), + cr1_k=(3,), + tail_df=(59.0,), + status="legitimate", + reason=( + "t(G-1): the 4.0 df_convention default (M-004..M-006, M-128..M-131) - " + "same CR1 k as the explicit-'residual' twin, tail df G-1" + ), + ), + dict( + key="wooldridge_hc1_within_default_df", + fit=lambda df: diff_diff.WooldridgeDiD(method="ols").fit( + df, outcome="y", unit="unit", time="time", first_treat="first_treat" + ), + cr1_k=(15,), + tail_df=(59.0,) * 10, + status="legitimate", + reason=( + "t(G-1): the 4.0 df_convention default (M-004..M-006, M-128..M-131) - " + "same CR1 k as the explicit-'residual' twin, tail df G-1" + ), + ), + dict( + key="sun_abraham_hc1_default_df", + fit=lambda df: diff_diff.SunAbraham().fit( + df, outcome="y", unit="unit", time="time", first_treat="first_treat" + ), + cr1_k=(21,), + tail_df=(59.0,) * 23, + status="legitimate", + reason=( + "t(G-1): the 4.0 df_convention default (M-004..M-006, M-128..M-131) - " + "same CR1 k as the explicit-'residual' twin, tail df G-1" + ), + ), + dict( + key="stacked_did_hc1_default_df", + fit=lambda df: diff_diff.StackedDiD().fit( + df, outcome="y", unit="unit", time="time", first_treat="first_treat" + ), + cr1_k=(6,), + tail_df=(59.0,) * 3, + status="legitimate", + reason=( + "t(G-1): the 4.0 df_convention default (M-004..M-006, M-128..M-131) - " + "same CR1 k as the explicit-'residual' twin, tail df G-1" + ), + ), + dict( + key="twfe_es_within_hc1_cluster_unit_default_df", + fit=lambda df: diff_diff.TwoWayFixedEffects(cluster="unit").fit( + df, + outcome="y", + treatment="grp", + unit="unit", + time="time", + event_study=True, + spec="within", + post_periods=[4, 5, 6], + reference_period=1, + ), + cr1_k=(11,), + tail_df=(59.0,) * 5, + status="legitimate", + reason=( + "t(G-1): the 4.0 df_convention default (M-004..M-006, M-128..M-131) - " + "same CR1 k as the explicit-'residual' twin, tail df G-1" + ), + ), + dict( + key="twfe_es_pooled_hc1_cluster_time_default_df", + fit=lambda df: diff_diff.TwoWayFixedEffects(cluster="time").fit( + df, + outcome="y", + treatment="grp", + time="time", + event_study=True, + spec="pooled", + post_periods=[4, 5, 6], + reference_period=1, + ), + cr1_k=(7,), + tail_df=(5.0,) * 5, + status="legitimate", + reason=( + "t(G-1): the 4.0 df_convention default (M-004..M-006, M-128..M-131) - " + "same CR1 k as the explicit-'residual' twin, tail df G-1" + ), + ), + dict( + key="imputation_pretrends_event_study_default_df", + fit=lambda df: diff_diff.ImputationDiD(pretrends=True) + .fit(df, outcome="y", unit="unit", time="time", first_treat="first_treat") + .aggregate("event_study"), + cr1_k=None, + # G-1 of the lead regression's untreated sample (59 on the audit + # panel: every unit has untreated rows); the None entries are the + # knob-independent BJS calls, exactly as on the residual twin. + tail_df=(59.0,) * 3 + (None,) * 5, + expected_adjustment=6, + status="legitimate", + reason=( + "t(G-1): the 4.0 df_convention default (M-004..M-006, M-128..M-131) - " + "same CR1 k as the explicit-'residual' twin, tail df G-1" + ), + ), ] _FAST_KEYS = { @@ -548,6 +680,9 @@ def snapshot(self): "did_fixed_effects_hc1_cluster_unit", "twfe_hc1_cluster_unit_time_post", "wooldridge_hc1_within", + "did_absorb_hc1_cluster_unit_default_df", + "twfe_hc1_cluster_unit_default_df", + "wooldridge_hc1_within_default_df", "lpdid_pre2_post2", "callaway_santanna_default", } @@ -970,15 +1105,21 @@ def test_disconnected_end_to_end_twfe(self): df = self._disconnected(effect=0.5) with warnings.catch_warnings(): warnings.simplefilter("ignore") - tw = diff_diff.TwoWayFixedEffects(vcov_type="hc1", cluster="unit").fit( + tw = diff_diff.TwoWayFixedEffects( + vcov_type="hc1", cluster="unit", df_convention="residual" + ).fit(df, outcome="y", treatment="grp", post="time", unit="unit") + tw_default = diff_diff.TwoWayFixedEffects(vcov_type="hc1", cluster="unit").fit( df, outcome="y", treatment="grp", post="time", unit="unit" ) assert np.isfinite(tw.se) - # Discriminating df pin: the C=2 rank lowers the absorbed adjustment - # by 1, so the reported residual df moves off the legacy value - - # reverting the caller to sum(levels-1) reads 114.0 on this fixture - # and fails here. + # Discriminating df pin (explicit 'residual' since the 4.0 default is + # G-1): the C=2 rank lowers the absorbed adjustment by 1, so the + # reported residual df moves off the legacy value - reverting the + # caller to sum(levels-1) reads 114.0 on this fixture and fails here. assert tw.inference_df == 115.0 + # The 4.0 default on the same fit: t(G-1), G = unique units. + assert tw_default.inference_df == df["unit"].nunique() - 1 + assert tw_default.se == tw.se def test_fail_closed_boundary_moves_both_directions(self): """Lowering the absorbed df by C-1 moves the n - k - adj boundary. diff --git a/tests/test_wooldridge.py b/tests/test_wooldridge.py index 9814f8372..045611bec 100644 --- a/tests/test_wooldridge.py +++ b/tests/test_wooldridge.py @@ -4662,21 +4662,29 @@ def _z_p(t_stat): return 2 * stats.norm.sf(abs(t_stat)) - def test_default_hc1_is_t_residual_not_z(self): - """The defect fix: the auto-clustered hc1 arm moved z -> t(residual).""" - res = WooldridgeDiD().fit(self._panel(), **self._kw) + def test_default_hc1_is_t_g_minus_1_not_z(self): + """The 4.0 default (M-129): the auto-clustered hc1 arm uses t(G-1) - + not z (the pre-3.9 defect) and not the residual df (the 3.x default).""" + data = self._panel() + res = WooldridgeDiD().fit(data, **self._kw) assert res.p_value != self._z_p(res.t_stat) - # residual df = n - k_kept - absorbed [unit, time] rank; recover it + G = res.n_clusters + assert G == data["unit"].nunique() + # recover the df the p-value was computed at: it is exactly G-1 n = 300 match = [d for d in range(2, n) if abs(res.p_value - self._t_p(res.t_stat, d)) < 1e-13] - assert len(match) == 1, match + assert match == [G - 1], match # per-cell inference shares the same df _, eff = next(iter(res.group_time_effects.items())) - assert eff["p_value"] == pytest.approx(self._t_p(eff["t_stat"], match[0]), rel=1e-12) + assert eff["p_value"] == pytest.approx(self._t_p(eff["t_stat"], G - 1), rel=1e-12) + # explicit "residual" is the 3.x residual df (n - k_kept - absorbed rank) + rr = WooldridgeDiD(df_convention="residual").fit(data, **self._kw) + match_r = [d for d in range(2, n) if abs(rr.p_value - self._t_p(rr.t_stat, d)) < 1e-13] + assert len(match_r) == 1 and match_r[0] > G - 1, match_r def test_cluster_matches_g_minus_1(self): data = self._panel() - r0 = WooldridgeDiD().fit(data, **self._kw) + r0 = WooldridgeDiD(df_convention="residual").fit(data, **self._kw) r1 = WooldridgeDiD(df_convention="cluster").fit(data, **self._kw) G = data["unit"].nunique() assert r1.att == r0.att and r1.se == r0.se @@ -4707,10 +4715,12 @@ def test_classical_and_hc2_bit_identical_under_default(self): def test_hc1_cohort_trends_uses_full_dummy_residual_df(self): """The easy-to-miss lane: hc1 + cohort_trends routes FULL-DUMMY, so - its residual df is n - k_kept (no absorbed-rank subtraction).""" + its residual df is n - k_kept (no absorbed-rank subtraction). Both + arms pin the explicit "residual" branch (the 3.x default); the + default twin at the end pins G-1 on the same full-dummy lane.""" data = self._panel() - res = WooldridgeDiD(cohort_trends=True).fit(data, **self._kw) - res_within = WooldridgeDiD().fit(data, **self._kw) + res = WooldridgeDiD(cohort_trends=True, df_convention="residual").fit(data, **self._kw) + res_within = WooldridgeDiD(df_convention="residual").fit(data, **self._kw) _, eff = next(iter(res.group_time_effects.items())) n = len(data) # full-dummy residual df: recover as n - k_kept @@ -4727,6 +4737,12 @@ def test_hc1_cohort_trends_uses_full_dummy_residual_df(self): d for d in range(2, n) if abs(effw["p_value"] - self._t_p(effw["t_stat"], d)) < 1e-13 ] assert match_w and (n - match_fd[0]) != match_w[0] + # default twin: the full-dummy lane also honours the 4.0 G-1 default + res_def = WooldridgeDiD(cohort_trends=True).fit(data, **self._kw) + _, eff_def = next(iter(res_def.group_time_effects.items())) + assert eff_def["p_value"] == pytest.approx( + self._t_p(eff_def["t_stat"], res_def.n_clusters - 1), rel=1e-12 + ) def test_postfit_aggregate_reproduces_fit_time_convention(self): """aggregate() consumes the stored resolved fallback on every OLS arm.""" @@ -4754,7 +4770,7 @@ def test_glm_warns_on_explicit_nondefault_only(self): data["bin"] = (data["outcome"] > data["outcome"].median()).astype(int) kw = dict(outcome="bin", unit="unit", time="time", first_treat="cohort") with pytest.warns(UserWarning, match="no effect on the logit/poisson"): - WooldridgeDiD(method="logit", df_convention="cluster").fit(data, **kw) + WooldridgeDiD(method="logit", df_convention="residual").fit(data, **kw) with warnings.catch_warnings(record=True) as caught: warnings.simplefilter("always") WooldridgeDiD(method="logit").fit(data, **kw) @@ -4808,11 +4824,11 @@ def test_validation_and_transactional_set_params(self): est = WooldridgeDiD() with pytest.raises(ValueError, match="df_convention"): est.set_params(df_convention="bogus", alpha=0.10) - assert est.df_convention == "residual" and est.alpha == 0.05 + assert est.df_convention == "cluster" and est.alpha == 0.05 # Valid value + unknown key: the unknown-key rejection must also # leave the estimator fully unchanged (no partial application). before = est.get_params() with pytest.raises(ValueError, match="Unknown parameter"): est.set_params(df_convention="normal", nonexistent_param=1) assert est.get_params() == before - assert WooldridgeDiD(df_convention="cluster").get_params()["df_convention"] == "cluster" + assert WooldridgeDiD(df_convention="residual").get_params()["df_convention"] == "residual"