On the Efficiency and Consistency of Likelihood Estimation in Multivariate Conditionally Heteroskedastic Dynamic Regression Models

Gabriele Fiorentini and Enrique Sentana

Working Papers from CEMFI

Abstract: We rank the efficiency of several likelihood-based parametric and semiparametric estimators of conditional mean and variance parameters in multivariate dynamic models with i.i.d. spherical innovations, and show that Gaussian pseudo maximum likelihood estimators are inefficient except under normality. We also provide conditions for partial adaptivity of semiparametric procedures, and relate them to the consistency of distributionally misspecified maximum likelihood estimators. We propose Hausman tests that compare Gaussian pseudo maximum likelihood estimators with more efficient but less robust competitors. We also study the efficiency of sequential estimators of the shape parameters. Finally, we provide finite sample results through Monte Carlo simulations.

Date: 2007
New Economics Papers: this item is included in nep-ets and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (25)

Downloads: (external link)
https://www.cemfi.es/ftp/wp/0713.pdf (application/pdf)

Related works:
Working Paper: On the efficiency and consistency of likelihood estimation in multivariate conditionally heteroskedastic dynamic regression models (2007)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:cmf:wpaper:wp2007_0713

Access Statistics for this paper

More papers in Working Papers from CEMFI Contact information at EDIRC.
Bibliographic data for series maintained by Araceli Requerey ().