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Working Paper
Visualization, Identification, and stimation in the Linear Panel Event-Study Design
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 suggest ways to make them more informative. We examine the economic content of different possible identifying assumptions. We explore the performance of the corresponding estimators in simulations, highlighting that a given estimator can perform well or poorly depending on the economic environment. An accompanying Stata package, xtevent, facilitates adoption of our suggestions.
Working Paper
xtevent: Estimation and Visualization in the Linear Panel Event-Study Design
Linear panel models and the “event-study plots” that often accompany them are popular tools for learning about policy effects. We introduce the Stata package xtevent, which enables the construction of event-study plots following the suggestions in Freyaldenhoven et al. (Forthcoming). The package implements various procedures to estimate the underlying policy effects and allows for nonbinary policy variables and estimation adjusting for pre-event trends.
Working Paper
Pre-event Trends in the Panel Event-study Design
We consider a linear panel event-study design in which unobserved confounds may be related both to the outcome and to the policy variable of interest. We provide sufficient conditions to identify the causal effect of the policy by exploiting covariates related to the policy only through the confounds. Our model implies a set of moment equations that are linear in parameters. The effect of the policy can be estimated by 2SLS, and causal inference is valid even when endogeneity leads to pre-event trends (?pre-trends?) in the outcome. Alternative approaches perform poorly in our simulations
Working Paper
(Visualizing) Plausible Treatment Effect Paths
We consider estimation and inference for treatment effect paths. Examples include dynamic treatment effects, impulse response functions, and event study paths. We present two sets of plausible bounds to help visualize uncertainty associated with these paths. Both plausible bounds are often tighter than traditional confidence intervals, and can provide insights even when traditional (uniform) confidence bands appear uninformative. Our first set of bounds covers the average (or overall) effect rather than the entire path. Our second set of bounds imposes data-driven smoothness restrictions on ...