On Policy Evaluation with Aggregate Time-Series Shocks
CERGE-EI Working Paper Series No. 657, 2020
62 Pages Posted: 29 Jun 2020 Last revised: 24 May 2021
Date Written: May 7, 2020
We propose a new algorithm for estimating treatment effects in contexts where the exogenous variation comes from aggregate time-series shocks. Our estimator combines data-driven unit-level weights with a time-series model. We use the unit weights to control for unobserved aggregate confounders and use the time-series model to extract the quasi-random variation from the observed shock. We examine our algorithm's performance in a simulation based on Nakamura and Steinsson (2014). We provide statistical guarantees for our estimator in a practically relevant regime, where both cross-sectional and time-series dimensions are large, and we show how to use our method to conduct inference.
Keywords: Continuous Difference in Differences, Panel Data, Causal Effects, Treatment Effects, Unobserved Heterogeneity
JEL Classification: C18, C21, C23, C26
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