Local Projection Inference Is Simpler and More Robust Than You Think

• 成果类型: Article
• 署名作者: Montiel Olea, Jose Luis; Plagborg-Moller, Mikkel
• 署名单位: Columbia University; Princeton University
• 刊物名称: ECONOMETRICA
• ISSN/ISSBN: 0012-9682
• DOI: 10.3982/ECTA18756
• 发表日期: 2021
• 页码: 1789-1823
• 关键词: SAMPLE CONFIDENCE-INTERVALS; IMPULSE-RESPONSE; time-series; autoregressions; identification; bootstrap; models; sets

摘要

Applied macroeconomists often compute confidence intervals for impulse responses using local projections, that is, direct linear regressions of future outcomes on current covariates. This paper proves that local projection inference robustly handles two issues that commonly arise in applications: highly persistent data and the estimation of impulse responses at long horizons. We consider local projections that control for lags of the variables in the regression. We show that lag-augmented local projections with normal critical values are asymptotically valid uniformly over (i) both stationary and non-stationary data, and also over (ii) a wide range of response horizons. Moreover, lag augmentation obviates the need to correct standard errors for serial correlation in the regression residuals. Hence, local projection inference is arguably both simpler than previously thought and more robust than standard autoregressive inference, whose validity is known to depend sensitively on the persistence of the data and on the length of the horizon.