Abstrakt
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.