fix(lwdid): preserve fitted overall inference

CHANGELOG.md

@@ -8,6 +8,10 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 ## [Unreleased]
 
 ### Added
+- **`LWDiD` (Lee & Wooldridge 2025, 2026 rolling-transformation DiD).** Unit-specific
+  demean/detrend converts panel to cross-section; supports staggered adoption with
+  never-treated / not-yet-treated control groups, RA/IPW/IPWRA estimation, and
+  cluster-robust inference. Alias `LW`.
 - **`RegressionDiscontinuity` - sharp AND fuzzy regression discontinuity estimation
   with robust bias-corrected inference (alias `RDD`).** Local-polynomial RD per
   Calonico, Cattaneo & Titiunik (2014), parity-targeting R `rdrobust` 4.0.0

README.md

@@ -120,6 +120,7 @@ Full guide: `diff_diff.get_llm_guide("practitioner")`.
 - [WooldridgeDiD](https://diff-diff.readthedocs.io/en/stable/api/wooldridge_etwfe.html) - Wooldridge (2023, 2025) ETWFE: saturated OLS, logit/Poisson QMLE (ASF-based ATT). Alias `ETWFE`.
 - [LPDiD](https://diff-diff.readthedocs.io/en/stable/api/lpdid.html) - Dube, Girardi, Jorda & Taylor (2025) Local Projections DiD: per-horizon long-difference event study on clean controls (no negative weighting), variance- or equally-weighted ATT, for absorbing or non-absorbing (reversible) treatment
 - [ChangesInChanges](https://diff-diff.readthedocs.io/en/stable/api/changes_in_changes.html) - Athey & Imbens (2006) nonlinear/distributional DiD for the 2x2 design: full counterfactual distribution and quantile treatment effects via CDF transformation, plus the QDiD comparison estimator; bootstrap inference; R qte parity. Alias `CiC`
+- [LWDiD](https://diff-diff.readthedocs.io/en/stable/api/lwdid.html) - Lee & Wooldridge (2025, 2026) rolling-transformation DiD: unit-specific demean/detrend converts panel to cross-section, staggered adoption, RA/IPW/IPWRA estimation. Alias `LW`.
 - [BaconDecomposition](https://diff-diff.readthedocs.io/en/stable/api/bacon.html) - Goodman-Bacon (2021) decomposition for diagnosing TWFE bias in staggered settings
 
 ## Diagnostics & Sensitivity

diff_diff/__init__.py

@@ -154,6 +154,8 @@
 )
 from diff_diff.lpdid import LPDiD
 from diff_diff.lpdid_results import LPDiDResults
+from diff_diff.lwdid import LWDiD
+from diff_diff.lwdid_results import LWDiDResults
 from diff_diff.power import (
     PowerAnalysis,
     PowerResults,
@@ -315,6 +317,7 @@
 HAD = HeterogeneousAdoptionDiD
 CiC = ChangesInChanges
 RDD = RegressionDiscontinuity
+LW = LWDiD
 
 __version__ = "3.7.0"
 __all__ = [
@@ -406,6 +409,10 @@
     # LPDiD (Local Projections DiD)
     "LPDiD",
     "LPDiDResults",
+    # LWDiD (Lee & Wooldridge rolling transformation DiD)
+    "LWDiD",
+    "LWDiDResults",
+    "LW",
     # Visualization
     "plot_bacon",
     "plot_event_study",

diff_diff/guides/llms.txt

@@ -73,6 +73,7 @@ Full practitioner guide: call `diff_diff.get_llm_guide("practitioner")`
 - [LPDiD](https://diff-diff.readthedocs.io/en/stable/api/lpdid.html): Dube, Girardi, Jorda & Taylor (2025) Local Projections DiD: per-horizon long-difference event study on clean controls (no negative weighting); variance- or equally-weighted ATT, premean differencing, pooled pre/post, fast. Absorbing by default; non-absorbing (reversible) treatment via `non_absorbing="first_entry"` (Eq. 12) or `"effect_stabilization"` (Eq. 13, window `L`). Complex-survey designs (pweight + stratified-PSU TSL SEs) on the default path via `fit(survey_design=...)`.
 - [ChangesInChanges](https://diff-diff.readthedocs.io/en/stable/api/changes_in_changes.html): Athey & Imbens (2006) nonlinear/distributional DiD for the 2x2 design: recovers the treated group's full counterfactual outcome distribution and quantile treatment effects (ATT + QTE grid) via the CDF transformation `F_10(F_00^{-1}(F_01(y)))`; invariant to monotone outcome transformations (unconditional fits; the covariate QR branch is not); bootstrap inference (panel or repeated cross-section resampling); point parity with R `qte::CiC()`, including its covariate branch (`covariates=` -> per-cell linear quantile regression, Melly-Santangelo-style conditional CiC). Continuous outcomes, numeric covariates. Alias `CiC`.
 - [QDiD](https://diff-diff.readthedocs.io/en/stable/api/changes_in_changes.html): Athey & Imbens (2006) quantile DiD comparison estimator (additive quantile-by-quantile DiD, matching R `qte::QDiD()` including its covariate branch via `covariates=`); same bootstrap machinery as ChangesInChanges. The paper recommends CiC over QDiD (scale-dependent model with testable restrictions; a non-monotonicity warning fires when violated - unconditional fits only, the covariate-path counterfactual quantile curve is monotone by construction).
+- [LWDiD](https://diff-diff.readthedocs.io/en/stable/api/lwdid.html): Lee & Wooldridge (2025, 2026) rolling-transformation DiD — unit-specific demean/detrend converts panel to cross-section, supports staggered adoption with flexible control groups and estimation (RA/IPW/IPWRA). Alias: LW
 - [BaconDecomposition](https://diff-diff.readthedocs.io/en/stable/api/bacon.html): Goodman-Bacon (2021) decomposition for diagnosing TWFE bias in staggered settings
 
 ## Diagnostics and Sensitivity Analysis