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Consistent Non-Gaussian Pseudo Maximum Likelihood Estimators

Gabriele Fiorentini () and Enrique Sentana ()

Working Papers from CEMFI

Abstract: We characterise the mean and variance parameters that distributionally misspecified maximum likelihood estimators can consistently estimate in multivariate conditionally heteroskedastic dynamic regression models. We also provide simple closed-form consistent estimators for the rest. The inclusion of means and the explicit coverage of multivariate models make our procedures useful not only for GARCH models but also in many empirically relevant macro and finance applications involving VARs and multivariate regressions. We study the statistical properties of our proposed consistent estimators, as well as their efficiency relative to Gaussian pseudo maximum likelihood procedures. Finally, we provide finite sample results through Monte Carlo simulations.

Keywords: Consistency; efficiency; misspecification. (search for similar items in EconPapers)
JEL-codes: C13 C22 C32 C51 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ets and nep-ore
Date: 2018-01
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Related works:
Working Paper: Consistent non-Gaussian pseudo maximum likelihood estimators (2018) Downloads
Working Paper: Consistent non-Gaussian pseudo maximum likelihood estimators (2018) Downloads
Working Paper: Consistent non-Gaussian pseudo maximum likelihood estimators (2018) Downloads
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