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Higher Moments and Efficiency Gains in Recursive Structural Vector Autoregressions

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  • Sascha A. Keweloh
  • Shu Wang

Abstract

Recursive SVAR models are identified by covariance conditions derived from the assumption of uncorrelated shocks. Recent literature has advocated using additional higher‐order moment conditions implied by independent shocks. This paper characterizes the redundancy properties of these higher‐order coskewness and cokurtosis conditions by showing that recursive SVAR estimators that rely exclusively on covariance conditions, neglecting the additional identifying information in higher‐order moments, are asymptotically inefficient. Moreover, we prove that some higher‐order moment conditions are always redundant and provide no improvement in asymptotic efficiency. A simulation demonstrates that excluding redundant conditions is essential to achieve performance gains in small samples.

Suggested Citation

  • Sascha A. Keweloh & Shu Wang, 2026. "Higher Moments and Efficiency Gains in Recursive Structural Vector Autoregressions," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 88(1), pages 36-44, February.
    • Handle: RePEc:bla:obuest:v:88:y:2026:i:1:p:36-44
    DOI: 10.1111/obes.70008
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