In this paper we construct a parsimonious causal model that addresses multiple issues researchers face when trying to use aggregate time-series shocks for policy evaluation: (a) potential unobserved aggregate confounders, (b) availability of various unit-level characteristics, (c) time and unit-level heterogeneity in treatment effects. We develop a new estimation algorithm that uses insights from treatment effects, panel, and time-series literature. We construct a variance estimatorthat is robust to arbitrary clustering pattern across geographical units. We achieve this by considering a finite population framework, where potential outcomes are treated as fixed, and all randomness comes from the exogenous shocks . Finally, we illustrate our approach using data from a study on the causal relationship between foreign aid and conflict conducted in Nunn and Qian 2014 .
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 .
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In this paper we construct a parsimonious causal model that addresses multiple issues researchers face when trying to use aggregate
We propose a general strategy for estimating treatment effects, in contexts where the only source of exogenous variation is a sequence of aggregate
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shocks for policy evaluation: (a) potential unobserved aggregate confounders, (b) availability of various unit-level characteristics, (c) time and unit-level heterogeneity in treatment effects. We develop a new estimation algorithm that uses insights from treatment effects, panel, and
shocks. We start by arguing that commonly used estimation procedures tend to ignore the crucial
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literature. We construct a variance estimatorthat is robust to arbitrary clustering pattern across geographical units. We achieve this by considering a finite population framework, where potential outcomes are treated as fixed, and all randomness comes from the exogenous shocks
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
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we illustrate our approach using data from a study on the causal relationship between foreign aid and conflict conducted in Nunn and Qian
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2014
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