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Structural Vector Autoregressions and Higher Moments: Challenges and Solutions in Small Samples

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  • Sascha A. Keweloh

Abstract

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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  • Sascha A. Keweloh, 2023. "Structural Vector Autoregressions and Higher Moments: Challenges and Solutions in Small Samples," Papers 2310.08173, arXiv.org.
    • Handle: RePEc:arx:papers:2310.08173
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    References listed on IDEAS

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    1. Drautzburg, Thorsten & Wright, Jonathan H., 2023. "Refining set-identification in VARs through independence," Journal of Econometrics, Elsevier, vol. 235(2), pages 1827-1847.
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