feat(v4): ETWFE per-period comparison support - restore all-eventually-treated fits (W2025 Sec 5.4)

CHANGELOG.md

@@ -65,14 +65,45 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
   in every post cell: `ATT(3,8) = -0.0096`, `ATT(5,9) = -0.2393`, and
   `overall_att = 1.0023`, with **no warning** under
   `rank_deficient_action="silent"`.
-  **This is a capability regression, taken deliberately.** Panels with no
-  never-treated group — where periods in which every unit is treated have no
-  eligible comparison — previously produced those wrong numbers and now fail
-  closed, including under `survey_design=` and `cohort_trends=True`. Wooldridge
-  (2025) Section 5.4 defines the correct treatment (the last cohort serves as
-  the control, its columns dropped deliberately rather than by QR); the library
-  already applies that to the cohort-trend column but not to the cells, and
-  implementing the cell half is tracked to restore these fits.
+  Panels with no never-treated group — where periods in which every unit is
+  treated have no eligible comparison — previously produced those wrong numbers.
+  They briefly failed closed, and now **estimate correctly** via the
+  comparison-support filter below; the gate remains as the backstop for what
+  that filter cannot see.
+- **`WooldridgeDiD` estimates all-eventually-treated panels again, implementing
+  Wooldridge (2025) Section 5.4 for the cell and cohort-trend families.** With
+  no never-treated group the last cohort serves as the reference, so periods at
+  which no unit is untreated carry no identified `ATT(g, t)` and are removed
+  from the estimation sample before the solve. Measured on a `{3, 5, 8}` panel
+  over `t = 1..9` with a true ATT of 1.5: cells `(3, 3..7)` and `(5, 5..7)`,
+  `overall_att = 1.5502`, cohort 8 correctly receiving none. Anchored to Stata
+  `jwdid` on the `mpdta` panel with never-treated counties dropped — identical
+  cell set, identical `N` (764 of 955), ATTs agreeing to ~1e-15.
+  **Nothing is dropped silently.** Every fit that reduces the sample reports the
+  periods, the observation count and the cause, and separately names any cohort
+  left without cells or stripped of every row; neither warning is gated on
+  `rank_deficient_action`. On the `never_treated` OLS branch a filtered period
+  can legitimately move a cohort's reference, renormalizing that cohort's ATTs
+  — this warns by name rather than passing silently. (Stata `jwdid` performs the
+  same reduction and reports only a smaller `N`.)
+  `cohort_trends=True` works on these panels for the first time, surfacing
+  `G - 1` trend slopes with the last cohort absent as the baseline. Under
+  `survey_design=` the combination is refused rather than subsetted, for the
+  same reason as the unidentified-cohort refusal below.
+  On an **unbalanced** panel a unit observed only at unsupported periods is
+  removed with them, which shrinks the cohort-share weight `N_g` (Eqs. 7.4/7.6)
+  below the cohort actually supplied — measured at a 2x swing in
+  `aggregate(weights="cohort_share")`. The paper defines `N_g` on a balanced
+  panel and does not settle which reading applies, so that aggregation now
+  **fails closed** naming the affected cohorts and unit counts instead of
+  choosing one silently. `weights="cell"` (the default) never reads `N_g`, and
+  balanced panels drop whole periods and no units, so neither is affected.
+  Not included: the `D_{G_max} × X` covariate normalization, so *some* covariate
+  specifications on such a panel remain rank-deficient — `exovar` and `xgvar`,
+  and `xtvar` under `demean_covariates=False`. Default `xtvar` (with
+  `demean_covariates=True`) is **full rank** and fits cleanly even under
+  `rank_deficient_action="error"`. Where the deficiency does bite, coefficients
+  are unaffected but `"error"` raises. Tracked in `TODO.md`.
 - **`WooldridgeDiD` refuses `survey_design=` combined with an unidentified
   cohort** (`NotImplementedError`, all three methods). Excluding a cohort under
   a complex survey design is domain estimation, which this path does not

DEFERRED.md

@@ -46,7 +46,7 @@ exists but parity can't be verified without a local toolchain.
 | `StaggeredTripleDifference` R cross-validation: CSV fixtures not committed (gitignored); tests skip without local R + `triplediff`. Commit fixtures or generate deterministically. | `tests/test_methodology_staggered_triple_diff.py` | #245 | Medium |
 | `StaggeredTripleDifference` R parity: benchmark only tests the no-covariate path (`xformla=~1`). Add covariate-adjusted scenarios + aggregation-SE parity assertions. | `benchmarks/R/benchmark_staggered_triplediff.R` | #245 | Medium |
 | `StaggeredTripleDifference` per-cohort group-effect SEs include WIF (conservative vs R's `wif=NULL`); documented in REGISTRY. Could override the mixin for an exact R match (verification needs R `triplediff`). | `staggered_triple_diff.py` | #245 | Low |
-| **WooldridgeDiD follow-up cluster** (PR-B Stage D/E fail-closed surfaces; re-enable after R/Stata validation): <br>• QMLE sandwich uses `aweight` cluster adjustment `(G/(G-1))·(n-1)/(n-k)` vs Stata's `G/(G-1)` (conservative); add a `qmle` weight type if Stata goldens confirm a material difference (`wooldridge.py`, `linalg.py`). <br>• response-scale APE / log-link coefficient bridge for R `etwfe(family=poisson|logit)` cell-level parity — needs `emfx()` APE extraction or link-inversion with baseline-mean adjustment (`generate_wooldridge_golden.R`, `test_methodology_wooldridge.py`). <br>• `aggregate(weights="cohort_share")` on survey-weighted fits: `_n_g_per_cohort` uses raw `unit.nunique()`; implement design-weighted unit totals per cohort (paper W2025 §7) and lift the `ValueError` gate (`wooldridge.py`, `wooldridge_results.py`). <br>• unconditional inference for `cohort_share` accounting for ω̂_g sampling uncertainty (W2025 §7.5); currently NaN-closed (`wooldridge_results.py`). <br>• `cohort_trends=True × survey_design` and `× control_group="never_treated"` raise `NotImplementedError` (unvalidated TSL variance / trend columns spanned jointly by the placebo cells and the unit FE, which absorb the cohort indicator and recover the omitted reference) (`wooldridge.py`). <br>• Stata `jwdid` golden-value `TestReferenceValues` (`tests/test_wooldridge.py`). | `wooldridge.py`, `wooldridge_results.py`, `linalg.py`, benchmarks | #216 · PR-B | Med-Low |
+| **WooldridgeDiD follow-up cluster** (PR-B Stage D/E fail-closed surfaces; re-enable after R/Stata validation): <br>• QMLE sandwich uses `aweight` cluster adjustment `(G/(G-1))·(n-1)/(n-k)` vs Stata's `G/(G-1)` (conservative); add a `qmle` weight type if Stata goldens confirm a material difference (`wooldridge.py`, `linalg.py`). <br>• response-scale APE / log-link coefficient bridge for R `etwfe(family=poisson|logit)` cell-level parity — needs `emfx()` APE extraction or link-inversion with baseline-mean adjustment (`generate_wooldridge_golden.R`, `test_methodology_wooldridge.py`). <br>• `aggregate(weights="cohort_share")` on survey-weighted fits: `_n_g_per_cohort` uses raw `unit.nunique()`; implement design-weighted unit totals per cohort (paper W2025 §7) and lift the `ValueError` gate (`wooldridge.py`, `wooldridge_results.py`). <br>• unconditional inference for `cohort_share` accounting for ω̂_g sampling uncertainty (W2025 §7.5); currently NaN-closed (`wooldridge_results.py`). <br>• `cohort_trends=True × survey_design` and `× control_group="never_treated"` raise `NotImplementedError` (unvalidated TSL variance / trend columns spanned jointly by the placebo cells and the unit FE, which absorb the cohort indicator and recover the omitted reference) (`wooldridge.py`). <br>• ~~Stata `jwdid` golden-value `TestReferenceValues`~~ RESOLVED: the golden ships four arms pinned by `tests/test_etwfe_cs_stata_parity.py` (no `TestReferenceValues` symbol was ever added). The QMLE bullet above REMAINS OPEN -- every golden arm is linear `jwdid`, so no QMLE cluster-SE reference exists, and SEs are pinned only as a ratio. | `wooldridge.py`, `wooldridge_results.py`, `linalg.py`, benchmarks | #216 · PR-B | Med-Low |
 | Extend `WooldridgeDiD` `method ∈ {logit, poisson}` with `vcov_type ∈ {classical, hc2, hc2_bm}`: composing HC2 leverage + Bell-McCaffrey DOF with the QMLE pseudo-residual sandwich needs derivation + R parity vs `clubSandwich::vcovCR(glm, type="CR2")`. Rejected at `__init__`. | `wooldridge.py` | follow-up | Medium |
 | Multi-constraint CR2 parallel-trends test (AHT/HTZ) for `hc2_bm` fits: DiagnosticReport's PT check routes `vcov_type="hc2_bm"` sources to Bonferroni over the BM-adjusted per-row p-values because the generic chi-square joint Wald would discard the CR2 small-sample correction (see REPORTING.md "hc2_bm parallel-trends policy"). The proper joint test is the AHT/HTZ Wald with a Satterthwaite-style denominator df over the pre-period contrast block; needs derivation for the stacked/pooled WLS-CR2 layout + parity vs `clubSandwich::Wald_test(..., test="HTZ")`. | `diagnostic_report.py`, `linalg.py` | vcov/df round-trip PR | Low |
 | `PreTrendsPower` CS/SA `anticipation=1` R-parity fixture: R `pretrends` has no anticipation parameter, so the Python `_extract_pre_period_params` anticipation filter isn't R-parity-locked. Build a synthetic CS/SA result with `anticipation=1` and assert γ_p matches R's `slope_for_power()`. (Mechanism already covered by MC + full-VCV tests.) | `tests/test_methodology_pretrends.py`, `generate_pretrends_golden.R` | PR-C | Low |

METHODOLOGY_REVIEW.md

@@ -650,7 +650,7 @@ and covariate-adjusted specifications.)
 **Deviations from the paper / from R / library extensions:** See REGISTRY.md `## WooldridgeDiD (ETWFE)` → `### Deviations from the paper / from R / library extensions` block for the consolidated list (HC1 finite-sample factor, QMLE sandwich `(n-1)/(n-k)` term, nonlinear-vs-fixest direct QMLE, logit cohort+time additive dummies, anticipation + aggregation, cell-count default with opt-in cohort-share).
 
 **Outstanding Concerns:**
-- **Stata `jwdid` golden values** (DEFERRED.md WooldridgeDiD follow-up cluster row): Stata-side parity infrastructure deferred until Stata install is available; R `etwfe` side covered in PR-B Stage D.
+- **Stata `jwdid` golden values**: RESOLVED (#729, extended here). `benchmarks/data/etwfe_cs_stata_golden.json` ships four arms -- `csdid`, `jwdid`, `jwdid_never` (the issue #724 reference-period anchor) and `jwdid_alltreated` (the W2025 Section 5.4 anchor) -- generated with local StataSE 19 and pinned by `tests/test_etwfe_cs_stata_parity.py`. Point estimates match to ~1e-15. What remains open is the `hc1` SE gap (library SEs uniformly SMALLER than jwdid's by a cluster-count-dependent factor: 1.0280@G=20, 1.0132@G=40, 1.00264@G=191, 1.0010@G=500), tracked in TODO.md; each arm pins its own measured ratio because the constant does not transfer between cluster counts. No QMLE (logit/Poisson) golden exists yet -- every arm is linear `jwdid` -- so the QMLE cluster-SE comparison in DEFERRED.md stays open. R `etwfe` side covered in PR-B Stage D.
 - **Response-scale APE / log-link bridge for Poisson + logit R parity** (DEFERRED.md WooldridgeDiD follow-up cluster row, added in PR-B): direct cell-level numerical parity between diff-diff's response-scale ATT and R `etwfe` log-link coefficients requires either `emfx()`-based APE extraction on the R side or link-function inversion with baseline-mean adjustment.
 - **QMLE sandwich Stata-parity `qmle` weight type** (DEFERRED.md WooldridgeDiD follow-up cluster row): diff-diff's `(G/(G-1)) × ((n-1)/(n-k))` is conservative vs Stata's `G/(G-1)` only; awaiting Stata golden values to confirm material difference.
 - **Repeated cross-sections** (paper p. 2581 → Deb et al. 2024): not in 2025 paper's main body; future PR.