On Policy Evaluation With Aggregate Time-Series Instruments

Dmitry Arkhangelsky and Vasily Korovkin

No 18931, CEPR Discussion Papers from Centre for Economic Policy Research

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.

Keywords: Difference in differences; Panel data; Causal effects; Instrumental variables; Treatment effects; Synthetic control (search for similar items in EconPapers)
JEL-codes: C18 C21 C23 C26 (search for similar items in EconPapers)
Date: 2024-03
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