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 crucial time-series aspects of the data. Next, we develop a graphical tool and a formal test to illustrate the issues of the design using data from prominent studies in development economics and macroeconomic. Motivated by these studies, we construct a new IV estimator, which is based on the time-series model for the aggregate shock. We analyze the statistical properties of ] our estimator in a practically relevant case , where both cross-sectional and time-series dimensions are of similar size. Finally, to provide a 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 .
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 realistic 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 show how to use it to conduct robust inference .
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We start by arguing that commonly used estimation procedures tend to ignore crucial
Our estimator combines data-driven unit-level weights with a
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aspects of the data. Next, we develop a graphical tool and a formal test to illustrate the issues of the design using data from prominent studies in development economics and macroeconomic. Motivated by these studies, we construct a new IV estimator, which is based on the
model. We use the unit weights to control for unobserved aggregate confounders and use the
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model for the aggregate shock. We analyze the statistical properties of
model to extract the quasi-random variation from the observed shock. We examine our algorithm's performance in a realistic simulation based on Nakamura and Steinsson
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of similar size. Finally, to provide a 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
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