Discrete mixtures of normals pseudo maximum likelihood estimators of structural vector autoregressions

Gabriele Fiorentini and Enrique Sentana

Journal of Econometrics, 2023, vol. 235, issue 2, 643-665

Abstract: Likelihood inference in structural vector autoregressions with independent non-Gaussian shocks leads to parametric identification and efficient estimation at the risk of inconsistencies under distributional misspecification. We prove that autoregressive coefficients and (scaled) impact multipliers remain consistent, but the drifts and shocks’ standard deviations are generally inconsistent. Nevertheless, we show consistency when the non-Gaussian log-likelihood uses a discrete scale mixture of normals in the symmetric case, or an unrestricted finite mixture more generally, and compare the efficiency of these estimators to other consistent two-step proposals, including our own. Finally, our empirical application looks at dynamic linkages between three popular volatility indices.

Keywords: Consistency; Efficiency bound; Finite normal mixtures; Pseudo maximum likelihood estimators; Structural models; Volatility indices (search for similar items in EconPapers)
JEL-codes: C32 C46 C51 C58 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (9)

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Related works:
Working Paper: Discrete Mixtures of Normals Pseudo Maximum Likelihood Estimators of Structural Vector Autoregressions (2020)
Working Paper: Discrete Mixtures of Normals Pseudo Maximum Likelihood Estimators of Structural Vector Autoregressions (2020)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:235:y:2023:i:2:p:643-665

DOI: 10.1016/j.jeconom.2022.02.010

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