Time-Varying Heterogeneous Treatment Effects in Event Studies

arXiv 17 Sep 2025 · Econometrics

arXiv:2509.13698 · PDF · DOI · OpenAlex · Extracted main text

Abstract

This paper examines the identification and estimation of heterogeneous treatment effects in event studies, emphasizing the importance of both lagged dependent variables and treatment effect heterogeneity. We show that omitting lagged dependent variables can induce omitted variable bias in the estimated time-varying treatment effects. We develop a novel semiparametric approach based on a short-T dynamic linear panel model with correlated random coefficients, where the time-varying heterogeneous treatment effects can be modeled by a time-series process to reduce dimensionality. We construct a two-step estimator employing quasi-maximum likelihood for common parameters and empirical Bayes for the heterogeneous treatment effects. The procedure is flexible, easy to implement, and achieves ratio optimality asymptotically. Our results also provide insights into common assumptions in the event study literature, such as no anticipation, homogeneous treatment effects across treatment timing cohorts, and state dependence structure.

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Most heavily cited references

The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.

	Reference	Intensity	Mentions	Sections	Main text
1	Liu, Moon, and Schorfheide (2020) Forecasting With Dynamic Panel Data Models	0.843	5	3	60%
2	Sun and Abraham (2021) Estimating Dynamic Treatment Effects in Event Studies with Heterogeneous Treatment Effects	0.811	4	2	100%
3	Arellano and Bonhomme (2012) Identifying Distributional Characteristics in Random Coefficients Panel Data Models	0.754	7	3	43%
4	Alvarez and Arellano (2022) Robust Likelihood Estimation of Dynamic Panel Data Models	0.644	2	2	100%
5	Brown and Greenshtein (2009) Nonparametric Empirical Bayes and Compound Decision Approaches to Estimation of a High-Dimensional Vector of Normal Means	0.644	2	2	100%
6	Efron (2011) Tweedie’s Formula and Selection Bias	0.644	2	2	100%
7	Jiang and Zhang (2009) General Maximum Likelihood Empirical Bayes Estimation of Normal Means	0.644	2	2	100%
8	Liu (2023) Density forecasts in panel data models: A semiparametric bayesian perspective self	0.644	2	2	100%
9	Robbins (1951) Asymptotically Subminimax Solutions of Compound Decision Problems	0.644	2	2	100%
10	de Chaisemartin and D'Haultfuille (2023) Two‐Way Fixed Effects and Differences‐in‐Differences with Heterogeneous Treatment Effects: A Survey	0.511	2	1	100%

Showing the top 10 of 26 scored citations.

Cited by, within the corpus

arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.

	Citing paper	Intensity	Mentions	Sections
1	Event Studies with Feedback	0.950	7	5
2	Forecasted Treatment Effects with Short Panels	0.511	2	2
3	Back to Feedback Dynamics and Heterogeneity in Panel Data	0.405	1	1