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Title: Local Projection Inference Is Simpler and More Robust Than You Think
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.  more » « less
Award ID(s):
1851665
PAR ID:
10281004
Author(s) / Creator(s):
;
Date Published:
Journal Name:
Econometrica
Volume:
89
Issue:
4
ISSN:
0012-9682
Page Range / eLocation ID:
1789 to 1823
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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