On the efficiency and consistency of likelihood estimation in multivariate conditionally heteroskedastic dynamic regression models∗ Gabriele Fiorentini Università di Firenze and RCEA, Viale Morgagni 59, I-50134 Firenze, Italy <fi
[email protected]fi.it> Enrique Sentana CEMFI, Casado del Alisal 5, E-28014 Madrid, Spain <sentana@cemfi.es> Revised: October 2010 Abstract We rank the efficiency of several likelihood-based parametric and semiparametric estima- tors of conditional mean and variance parameters in multivariate dynamic models with po- tentially asymmetric and leptokurtic strong white noise innovations. We detailedly study the elliptical case, and show that Gaussian pseudo maximum likelihood estimators are inefficient except under normality. We provide consistency conditions for distributionally misspecified maximum likelihood estimators, and show that they coincide with the partial adaptivity con- ditions of semiparametric procedures. We propose Hausman tests that compare Gaussian pseudo maximum likelihood estimators with more efficient but less robust competitors. Fi- nally, we provide finite sample results through Monte Carlo simulations. Keywords: Adaptivity, Elliptical Distributions, Hausman tests, Semiparametric Esti- mators. JEL: C13, C14, C12, C51, C52 ∗We would like to thank Dante Amengual, Manuel Arellano, Nour Meddahi, Javier Mencía, Olivier Scaillet, Paolo Zaffaroni, participants at the European Meeting of the Econometric Society (Stockholm, 2003), the Sympo- sium on Economic Analysis (Seville, 2003), the CAF Conference on Multivariate Modelling in Finance and Risk Management (Sandbjerg, 2006), the Second Italian Congress on Econometrics and Empirical Economics (Rimini, 2007), as well as audiences at AUEB, Bocconi, Cass Business School, CEMFI, CREST, EUI, Florence, NYU, RCEA, Roma La Sapienza and Queen Mary for useful comments and suggestions. Of course, the usual caveat applies.