On Policy Evaluation with Aggregate Time-Series Shocks

Dmitry Arkhangelsky and Vasily Korovkin

Abstract: We develop an estimator for applications where the variable of interest is endogenous and researchers have access to aggregate instruments. Our method addresses the critical identification challenge -- unobserved confounding, which renders conventional estimators invalid. Our proposal relies on a new data-driven aggregation scheme that eliminates the unobserved confounders. We illustrate the advantages of our algorithm using data from Nakamura and Steinsson (2014) study of local fiscal multipliers. We introduce a finite population model with aggregate uncertainty to analyze our estimator. We establish conditions for consistency and asymptotic normality and show how to use our estimator to conduct valid inference.

Date: 2019-05, Revised 2024-03
New Economics Papers: this item is included in nep-ecm and nep-ets
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