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.


Arkhangelsky, D and V Korovkin (2024), ‘DP18931 On Policy Evaluation With Aggregate Time-Series Instruments‘, CEPR Discussion Paper No. 18931. CEPR Press, Paris & London. https://cepr.org/publications/dp18931