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 …

Fixed effects (FE) have emerged as a ubiquitous and powerful tool for eliminating unwanted
variation in observational accounting studies. Unwanted variation is plentiful in accounting …

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 …

We provide quasi-experimental estimates of the impact of social media on mental health by
leveraging a unique natural experiment: the staggered introduction of Facebook across US …

This paper analyzes difference-in-differences designs with a continuous treatment. We show
that treatment effect on the treated-type parameters can be identified under a generalized …

A recent literature has shown that when adoption of a treatment is staggered and average
treatment effects vary across groups and over time, difference-in-differences regression …

This paper proposes tools for robust inference in difference-in-differences and event-study
designs where the parallel trends assumption may be violated. Instead of requiring that …

We explain when and how staggered difference-in-differences regression estimators,
commonly applied to assess the impact of policy changes, are biased. These biases are …