On Policy Evaluation with Aggregate Time-Series Shocks

Dmitry Arkhangelsky and Vasily Korovkin

CERGE-EI Working Papers from The Center for Economic Research and Graduate Education - Economics Institute, Prague

Abstract: We propose a general strategy for estimating treatment effects, in contexts where the only source of exogenous variation is a sequence of aggregate time-series shocks. We start by arguing that commonly used estimation procedures tend to ignore the crucial time-series aspects of the data. Next, we develop a graphical tool and a novel test to illustrate the issues of the design using data from influential studies in development economics [Nunn and Qian, 2014] and macroeconomics [Nakamura and Steinsson, 2014]. Motivated by these studies, we construct a new estimator, which is based on the time-series model for the aggregate shock. We analyze the statistical properties of our estimator in the practically relevant case, where both cross-sectional and time-series dimensions are of similar size. Finally, to provide causal interpretation for our estimator, we analyze a new causal model that allows taking into account both rich unobserved heterogeneity in potential outcomes and unobserved aggregate shocks.

Keywords: continuous difference in differences; panel data; causal effects; treatment effects; unobserved heterogeneity (search for similar items in EconPapers)
JEL-codes: C18 C21 C23 C26 (search for similar items in EconPapers)
Date: 2020-05
New Economics Papers: this item is included in nep-ore
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Working Paper: On Policy Evaluation with Aggregate Time-Series Shocks (2024)
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