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Identifying Structural Vector Autoregression via Leptokurtic Economic Shocks

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  • Markku Lanne
  • Keyan Liu
  • Jani Luoto

Abstract

We revisit the generalized method of moments (GMM) estimation of the non-Gaussian structural vector autoregressive (SVAR) model. It is shown that in the n-dimensional SVAR model, global and local identification of the contemporaneous impact matrix is achieved with as few as n2+n(n−1)/2 suitably selected moment conditions, when at least n – 1 of the structural errors are all leptokurtic (or platykurtic). We also relax the potentially problematic assumption of mutually independent structural errors in part of the previous literature to the requirement that the errors be mutually uncorrelated. Moreover, we assume the error term to be only serially uncorrelated, not independent in time, which allows for univariate conditional heteroscedasticity in its components. A small simulation experiment highlights the good properties of the estimator and the proposed moment selection procedure. The use of the methods is illustrated by means of an empirical application to the effect of a tax increase on U.S. gasoline consumption and carbon dioxide emissions.

Suggested Citation

  • Markku Lanne & Keyan Liu & Jani Luoto, 2023. "Identifying Structural Vector Autoregression via Leptokurtic Economic Shocks," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 41(4), pages 1341-1351, October.
  • Handle: RePEc:taf:jnlbes:v:41:y:2023:i:4:p:1341-1351
    DOI: 10.1080/07350015.2022.2134872
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    Cited by:

    1. Christis Katsouris, 2023. "Structural Analysis of Vector Autoregressive Models," Papers 2312.06402, arXiv.org, revised Feb 2024.

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