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On Policy Evaluation with Aggregate Time-Series Shocks


  • Dmitry Arkhangelsky
  • Vasily Korovkin


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.

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  • Dmitry Arkhangelsky & Vasily Korovkin, 2019. "On Policy Evaluation with Aggregate Time-Series Shocks," Papers 1905.13660,, revised Mar 2024.
  • Handle: RePEc:arx:papers:1905.13660

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