On Policy Evaluation with Aggregate Time-Series Shocks

Dmitry Arkhangelsky; Vasily Korovkin

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arXiv · 1905.13660

On Policy Evaluation with Aggregate Time-Series Shocks

Dmitry Arkhangelsky·Vasily Korovkin

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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.

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econ.EM econ.GN q-fin.EC

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BibTeX RIS

Dmitry Arkhangelsky, Vasily Korovkin. 2019-05-31. On Policy Evaluation with Aggregate Time-Series Shocks. https://arxiv.org/abs/1905.13660

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