Jean-Marie Dufour, Endong Wang
arXiv 17 Sep 2024 · Econometrics
arXiv:2409.10820 · PDF · DOI · OpenAlex · Extracted main text
This paper introduces a novel two-stage estimation and inference procedure for generalized impulse responses (GIRs). GIRs encompass all coefficients in a multi-horizon linear projection model of future outcomes of y on lagged values (Dufour and Renault, 1998), which include the Sims' impulse response. The conventional use of Least Squares (LS) with heteroskedasticity- and autocorrelation-consistent covariance estimation is less precise and often results in unreliable finite sample tests, further complicated by the selection of bandwidth and kernel functions. Our two-stage method surpasses the LS approach in terms of estimation efficiency and inference robustness. The robustness stems from our proposed covariance matrix estimates, which eliminate the need to correct for serial correlation in the multi-horizon projection residuals. Our method accommodates non-stationary data and allows the projection horizon to grow with sample size. Monte Carlo simulations demonstrate our two-stage method outperforms the LS method. We apply the two-stage method to investigate the GIRs, implement multi-horizon Granger causality test, and find that economic uncertainty exerts both short-run (1-3 months) and long-run (30 months) effects on economic activities.
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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 | Dufour \ Renault (1998) `Short run and long run causality in time series: theory', Econometrica 66(5), 1099–1125 | 1.000 | 6 | 3 | 100% |
| 2 | Xu (2023) `Local projection based inference under general conditions', Technical Report, Indiana University | 1.000 | 6 | 3 | 100% |
| 3 | Dufour, Pelletier \ Renault (2006) `Short run and long run causality in time series: inference', Journal of Econometrics 132(2), 337–362 | 0.965 | 10 | 3 | 90% |
| 4 | Montiel Olea \ Plagborg-Mller (2021) `Local projection inference is simpler and more robust than you think', Econometrica 89(4), 1789–1823 | 0.885 | 13 | 7 | 69% |
| 5 | Toda \ Yamamoto (1995) `Statistical inference in vector autoregressions with possibly integrated processes', Journal of Econometrics 66(1-2), 225–250 | 0.843 | 3 | 3 | 100% |
| 6 | Inoue \ Kilian (2020) `The uniform validity of impulse response inference in autoregressions', Journal of Econometrics 215(2), 450–472 | 0.737 | 3 | 2 | 100% |
| 7 | Benkwitz, Neumann \ Lütekpohl (2000) `Problems related to confidence intervals for impulse responses of autoregressive processes', Econometric Reviews 19(1), 69–103 | 0.644 | 2 | 2 | 100% |
| 8 | Dufour, Renault \ Zinde-Walsh (2023) `Wald tests when restrictions are locally singular', CIREQ Technical Report, McGill University | 0.644 | 2 | 2 | 100% |
| 9 | Hannan \ Rissanen (1982) `Recursive estimation of mixed autoregressive-moving average order', Biometrika 69(1), 81–94 | 0.644 | 2 | 2 | 100% |
| 10 | Kilian \ Kim (2011) `How reliable are local projection estimators of impulse responses?', The Review of Economics and Statistics 93(4), 1460–1466 | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 62 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | 2410.04330 | 1.000 | 5 | 3 |