On Policy Evaluation with Aggregate Time-Series Shocks

• Tác giả: Dmitry Arkhangelsky, Vasily Korovkin
• Ngôn ngữ: eng
• Ký hiệu phân loại: 332.46 Monetary policy
• Thông tin xuất bản: 2019
• Bộ sưu tập: Báo, Tạp chí
• ID: 162957

Tóm tắt

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.