The dynamic causal effect of an intervention on an outcome is of paramount interest to
applied macro-and microeconomics research. However, this question has been generally …

Difference-in-difference (DID) estimators are a valuable method for identifying causal effects
in the public health researcher's toolkit. A growing methods literature points out potential …

This paper synthesizes recent advances in the econometrics of difference-in-differences
(DiD) and provides concrete recommendations for practitioners. We begin by articulating a …

Linear regressions with period and group fixed effects are widely used to estimate policie's
effects: 26 of the 100 most cited papers published by the American Economic Review from …

The event study model is a powerful econometric tool used for the purpose of estimating
dynamic treatment effects. One of its most appealing features is that it provides a built-in …

Linear regressions with period and group fixed effects are widely used to estimate treatment
effects. We show that they estimate weighted sums of the average treatment effects (ATE) in …

We develop a framework for difference-in-differences designs with staggered treatment
adoption and heterogeneous causal effects. We show that conventional regression-based …

Linear panel models, and the “event-study plots” that often accompany them, are popular
tools for learning about policy effects. We discuss the construction of event-study plots and …

In the COVID-19 pandemic many countries required COVID certificates, proving vaccination,
recovery, or a recent negative test, to access public and private venues. We estimate their …

We discuss three important properties of panel data event study designs. First, assuming
constant treatment effects before and/or after some event time, also known as binning, is a …