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

See all articles by Dmitry Arkhangelsky

Dmitry Arkhangelsky

Centre for Monetary and Financial Studies (CEMFI)

Vasily Korovkin

CERGE-EI

Date Written: May 7, 2020

Abstract

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

Suggested Citation

Arkhangelsky, Dmitry and Korovkin, Vasily, On Policy Evaluation with Aggregate Time-Series Shocks (May 7, 2020). CERGE-EI Working Paper Series No. 657, 2020, Available at SSRN: https://ssrn.com/abstract=3619495 or http://dx.doi.org/10.2139/ssrn.3619495

Dmitry Arkhangelsky (Contact Author)

Centre for Monetary and Financial Studies (CEMFI) ( email )

Casado del Alisal 5
28014 Madrid
Spain

Vasily Korovkin

CERGE-EI ( email )

P.O. Box 882
7 Politickych veznu
Prague 1, 111 21
Czech Republic

HOME PAGE: http://www.vaskorovkin.com

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