Job market paper

Estimating Treatment Effects in Event Studies With Autoregressive Errors

This paper proposes a new method for estimating average treatment effects of a binary policy in staggered adoption settings. The method combines modern causal inference methods for policy evaluation with theoretical insights from the large panel data literature. Using theory and data-driven simulations, we show that this estimator improves over the available alternatives in settings with persistent errors. We recommend that applied researchers use the new method for policy evaluation in settings with many units and time periods.

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