Discussion paper
DP18931 On Policy Evaluation With Aggregate Time-Series Instruments
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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