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On Policy Evaluation with Aggregate Time-Series Shocks

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  • Dmitry Arkhangelsky
  • Vasily Korovkin

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.

Suggested Citation

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

    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C26 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Instrumental Variables (IV) Estimation

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