Consistent non-Gaussian pseudo maximum likelihood estimators

• Gabriele Fiorentini ^[1] ; Enrique Sentana ^[2]
  1. [1] Gabriele Fiorentini
  2. [2] CEMFI
• Localización: Documentos de Trabajo ( CEMFI ), Nº. 2 (CEMFI Working Paper No. 1802, January 2018), 2018
• Idioma: inglés
• Enlaces
  • Texto completo (pdf)
• Dialnet Métricas: 4 Citas
• Resumen

  • 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.