igerber · igerber · Jun 28, 2026 · Jun 28, 2026 · Jun 28, 2026 · Jun 28, 2026
diff --git a/CHANGELOG.md b/CHANGELOG.md
@@ -8,6 +8,18 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 ## [Unreleased]
 
 ### Added
+- **`LPDiD` (Local Projections Difference-in-Differences; Dube, Girardi, Jordà & Taylor 2025,
+  *J. Applied Econometrics* 40(5):741-758).** Per-horizon long-difference OLS
+  (`y_{i,t+h} − y_{i,t−1}`) on a clean-control sample (newly-treated + not-yet-treated) with
+  calendar-time fixed effects and no unit FE, so the default variance-weighted estimand has
+  strictly non-negative weights (no TWFE negative-weighting). Options: `reweight=True`
+  (equally-weighted ATT, numerically equivalent to Callaway-Sant'Anna), premean-differenced base
+  period (`pmd`), `no_composition` (fixed post-treatment composition), pooled pre/post estimands,
+  outcome/first-difference lag controls (`ylags`/`dylags`), and a regression-adjustment covariate
+  path (ImputationDiD/BJS-family influence-function cluster variance). Cluster-robust SEs at the
+  unit level by default. This release implements the **absorbing-treatment** path; per-unit
+  interior time gaps are handled by calendar-correct feature construction. Non-absorbing
+  treatment, survey-design support, and external R-package parity are tracked follow-ups.
 - **`placebo_group_test` gained an optional `treatment` parameter.** When supplied, units
   that are ever real-treated are dropped before the placebo so it runs on never-treated units
   only (the uncontaminated design); without it, behavior is unchanged and the caller must pass

diff --git a/README.md b/README.md
@@ -117,6 +117,7 @@ Full guide: `diff_diff.get_llm_guide("practitioner")`.
 - [TROP](https://diff-diff.readthedocs.io/en/stable/api/trop.html) - Triply Robust Panel estimator (Athey et al. 2025) with nuclear norm factor adjustment
 - [StaggeredTripleDifference](https://diff-diff.readthedocs.io/en/stable/api/staggered.html#staggeredtripledifference) - Ortiz-Villavicencio & Sant'Anna (2025) staggered DDD with group-time ATT
 - [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 treatment
 - [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 --git a/diff_diff/__init__.py b/diff_diff/__init__.py
@@ -230,6 +230,8 @@
 from diff_diff.synthetic_control_results import SyntheticControlResults
 from diff_diff.wooldridge import WooldridgeDiD
 from diff_diff.wooldridge_results import WooldridgeDiDResults
+from diff_diff.lpdid import LPDiD
+from diff_diff.lpdid_results import LPDiDResults
 from diff_diff.utils import (
     WildBootstrapResults,
     check_parallel_trends,
@@ -381,6 +383,9 @@
     "WooldridgeDiD",
     "WooldridgeDiDResults",
     "ETWFE",
+    # LPDiD (Local Projections DiD)
+    "LPDiD",
+    "LPDiDResults",
     # Visualization
     "plot_bacon",
     "plot_event_study",

diff --git a/diff_diff/guides/llms-full.txt b/diff_diff/guides/llms-full.txt
@@ -895,6 +895,56 @@ results = edid.fit(data, outcome='y', unit='id', time='t',
 results.print_summary()
 ```
 
+### LPDiD
+
+Local Projections DiD (Dube, Girardi, Jorda & Taylor 2025). Estimates a separate OLS at each event-time horizon of a long difference (`y_{i,t+h} - y_{i,t-1}`) on the treatment-switch indicator plus calendar-time fixed effects (no unit FE), restricted to a flexible "clean control" sample of newly-treated and not-yet-treated units. Excluding already-treated units from the control group removes the negative-weighting bias of naive TWFE, so the default (variance-weighted) estimand has strictly non-negative weights. `reweight=True` yields the equally-weighted ATT (numerically equivalent to Callaway-Sant'Anna); covariates then enter via regression adjustment. Standard errors on the default/weighted path are cluster-robust at the unit level (the paper specifies no SE; matches Stata `lpdid` `vce(cluster unit)`); the regression-adjustment covariate path (`reweight=True`) instead reports an influence-function cluster variance (ImputationDiD/BJS family). Scope: binary, absorbing treatment (rejects panels where treatment turns off).
+
+```python
+LPDiD(
+    pre_window: int = 2,                     # Number of pre-treatment horizons (placebos)
+    post_window: int = 0,                    # Number of post-treatment horizons
+    control_group: str = "clean",            # "clean" (not-yet-treated) or "never_treated"
+    reweight: bool = False,                  # True -> equally-weighted ATT (== Callaway-Sant'Anna); False -> variance-weighted
+    no_composition: bool = False,            # Hold the post-treatment composition fixed across post horizons
+    pmd: str | int | None = None,            # Base period: None=first-lag (t-1), "max"=premean over all pretreatment periods, int=last-k premean
+    alpha: float = 0.05,
+    cluster: str | None = None,              # Cluster column for cluster-robust SEs; defaults to the unit identifier
+    rank_deficient_action: str = "warn",     # "warn", "error", or "silent"
+)
+```
+
+**fit() parameters:**
+
+```python
+lpdid.fit(
+    data: pd.DataFrame,
+    outcome: str,
+    unit: str,
+    time: str,
+    treatment: str,                    # Binary, absorbing treatment indicator (0/1)
+    covariates: list[str] = None,      # Direct inclusion (reweight=False) or regression adjustment (reweight=True)
+    ylags: int = 0,                    # Lagged-outcome controls
+    dylags: int = 0,                   # Lagged first-difference controls
+    absorb: list[str] = None,          # Additional absorbed fixed-effect columns
+    post_pooled: int | tuple = None,   # Pooled post-window horizons (int or (start, end))
+    pre_pooled: int | tuple = None,    # Pooled pre-window horizons (int or (start, end))
+    only_event: bool = False,          # Compute only the event-study table
+    only_pooled: bool = False,         # Compute only the pooled pre/post table
+) -> LPDiDResults
+```
+
+**Usage:**
+
+```python
+from diff_diff import LPDiD
+
+lp = LPDiD(pre_window=5, post_window=10)
+results = lp.fit(data, outcome='y', unit='id', time='t', treatment='treated')
+results.print_summary()
+print(results.event_study)   # per-horizon coefficients
+print(results.pooled)        # pooled pre (placebo) / post (ATT) rows
+```
+
 ### TROP
 
 Triply Robust Panel estimator (Athey, Imbens, Qu & Viviano 2025). Combines nuclear norm regularization, distance-based unit weights, and time decay weights.

diff --git a/diff_diff/guides/llms.txt b/diff_diff/guides/llms.txt
@@ -2,7 +2,7 @@
 
 > A Python library for Difference-in-Differences (DiD) causal inference analysis. Provides sklearn-like estimators with statsmodels-style summary output for econometric analysis.
 
-diff-diff offers 18 estimators covering basic 2x2 DiD, modern staggered adoption methods, reversible (non-absorbing) treatments, advanced panel estimators, nonlinear models, and diagnostic tools. It supports robust and cluster-robust standard errors, wild cluster bootstrap, formula and column-name interfaces, fixed effects (dummy and absorbed), complex survey designs (strata/PSU/FPC, replicate weights, design-based variance), and publication-ready output. The optional Rust backend accelerates compute-intensive estimators like Synthetic DiD and TROP.
+diff-diff offers 19 estimators covering basic 2x2 DiD, modern staggered adoption methods, reversible (non-absorbing) treatments, advanced panel estimators, nonlinear models, and diagnostic tools. It supports robust and cluster-robust standard errors, wild cluster bootstrap, formula and column-name interfaces, fixed effects (dummy and absorbed), complex survey designs (strata/PSU/FPC, replicate weights, design-based variance), and publication-ready output. The optional Rust backend accelerates compute-intensive estimators like Synthetic DiD and TROP.
 
 - Install: `pip install diff-diff`
 - License: MIT
@@ -69,6 +69,7 @@ Full practitioner guide: call `diff_diff.get_llm_guide("practitioner")`
 - [TROP](https://diff-diff.readthedocs.io/en/stable/api/trop.html): Triply Robust Panel estimator (Athey et al. 2025) with nuclear norm factor adjustment
 - [StaggeredTripleDifference](https://diff-diff.readthedocs.io/en/stable/api/staggered.html#staggeredtripledifference): Ortiz-Villavicencio & Sant'Anna (2025) staggered DDD with group-time ATT
 - [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, premean differencing, pooled pre/post, fast. Absorbing treatment.
 - [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