arXiv 12 Oct 2023 · Econometrics · 3 citations (OpenAlex)
arXiv:2310.08173 · PDF · DOI · OpenAlex · Extracted main text
Generalized method of moments estimators based on higher-order moment conditions derived from independent shocks can be used to identify and estimate the simultaneous interaction in structural vector autoregressions. This study highlights two problems that arise when using these estimators in small samples. First, imprecise estimates of the asymptotically efficient weighting matrix and the asymptotic variance lead to volatile estimates and inaccurate inference. Second, many moment conditions lead to a small sample scaling bias towards innovations with a variance smaller than the normalizing unit variance assumption. To address the first problem, I propose utilizing the assumption of independent structural shocks to estimate the efficient weighting matrix and the variance of the estimator. For the second issue, I propose incorporating a continuously updated scaling term into the weighting matrix, eliminating the scaling bias. To demonstrate the effectiveness of these measures, I conducted a Monte Carlo simulation which shows a significant improvement in the performance of the estimator.
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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 | Lanne, M. and Luoto, J (2021) Gmm estimation of non-gaussian structural vector autoregression | 1.000 | 7 | 3 | 100% |
| 2 | Han, C. and Phillips, P. C (2006) Gmm with many moment conditions | 0.928 | 4 | 3 | 100% |
| 3 | Lanne, M. and Luoto, J (2022) Statistical identification of economic shocks by signs in structural vector autoregression | 0.843 | 3 | 3 | 100% |
| 4 | Newey, W. K. and Windmeijer, F (2009) Generalized method of moments with many weak moment conditions | 0.843 | 3 | 3 | 100% |
| 5 | Keweloh, S. A (2021) A generalized method of moments estimator for structural vector autoregressions based on higher moments self | 0.811 | 4 | 2 | 100% |
| 6 | Lanne, M., Liu, K., and Luoto, J (2022) Identifying structural vector autoregression via leptokurtic economic shocks | 0.811 | 4 | 2 | 100% |
| 7 | Mesters, G. and Zwiernik, P (2022) Non-independent components analysis | 0.737 | 3 | 2 | 100% |
| 8 | Amengual, D., Fiorentini, G., and Sentana, E (2022) Moment tests of independent components | 0.644 | 2 | 2 | 100% |
| 9 | Guay, A (2021) Identification of structural vector autoregressions through higher unconditional moments | 0.644 | 2 | 2 | 100% |
| 10 | Anttonen, J., Lanne, M., and Luoto, J (2023) Bayesian inference on fully and partially identified structural vector autoregressions | 0.511 | 2 | 1 | 100% |
Showing the top 10 of 21 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Uncertain Short-Run Restrictions and Statistically Identified Structural Vector Autoregressions | 0.811 | 5 | 2 |