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 and macroeconomics . 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 for rich unobserved heterogeneity in potential outcomes and unobserved aggregate shocks.
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.
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the
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novel
formal
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influential
prominent
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macroeconomics
macroeconomic
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IV
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the
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a
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taking into account both