José Luis Montiel Olea, Mikkel Plagborg-Møller
arXiv 27 Jul 2020 · Econometrics · publishedEconometrica (2021) · 51 citations (OpenAlex)
arXiv:2007.13888 · PDF · DOI · OpenAlex · Extracted main text
Applied macroeconomists often compute confidence intervals for impulse responses using local projections, i.e., 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.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Mikusheva, A (2012) One-Dimensional Inference in Autoregressive Models With the Potential Presence of a Unit Root | 1.000 | 13 | 4 | 100% |
| 2 | Jordà, Ò (2005) Estimation and Inference of Impulse Responses by Local Projections | 1.000 | 6 | 3 | 100% |
| 3 | Stock, J. H. and M. W. Watson (2018) Identification and Estimation of Dynamic Causal Effects in Macroeconomics Using External Instruments | 1.000 | 5 | 3 | 100% |
| 4 | Kilian, L. and H. Lütkepohl (2017) Structural Vector Autoregressive Analysis | 0.950 | 7 | 5 | 86% |
| 5 | Ramey, V. A (2016) Macroeconomic Shocks and Their Propagation, in | 0.928 | 4 | 3 | 100% |
| 6 | Inoue, A. and L. Kilian (2020) The uniform validity of impulse response inference in autoregressions | 0.918 | 31 | 5 | 77% |
| 7 | Hansen, B. E (1999) The Grid Bootstrap and the Autoregressive Model | 0.843 | 3 | 3 | 100% |
| 8 | Herbst, E. P. and B. K. Johannsen (2020) Bias in Local Projections, Board of Governors of the Federal Reserve System Finance and Economics Discussion Series 2020-010 | 0.843 | 3 | 3 | 100% |
| 9 | Inoue, A. and L. Kilian (2002) Bootstrapping Autoregressive Processes with Possible Unit Roots | 0.843 | 3 | 3 | 100% |
| 10 | Wright, J. H (2000) Confidence Intervals for Univariate Impulse Responses With a Near Unit Root | 0.843 | 3 | 3 | 100% |
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