Asymptotic Quasi-likelihood Based on Kernel Smoothing for Multivariate Heteroskedastic Models with Correlation

Authors

R. Alzghool, Al-Balqa Applied University
Y. X. Lin, University of WollongongFollow
S. X. Chen, Peking University

Publication Date

2009

Recommended Citation

Alzghool, R.; Lin, Y. X.; and Chen, S. X., Asymptotic Quasi-likelihood Based on Kernel Smoothing for Multivariate Heteroskedastic Models with Correlation, Centre for Statistical and Survey Methodology, University of Wollongong, Working Paper 22-09, 2009, 32p.
https://ro.uow.edu.au/cssmwp/42

Abstract

This paper considers parameter estimation in multivariate heteroscedastic models with unspecific correlation. In this paper, we propose an asymptotic quasi-likelihood (AQL) approach which utilises a nonparametric kernel estimator of variance covariances matrix ∑ to replace the true ∑ in the standard quasi-likelihood. The kernel estimation avoids the risk of potential missspecification of ∑ and thus make the parameter estimator more robust. The well developed theory framework for AQL (See Lin, 2000) provides a solid base for ensuring the efficiency of the approach developed in this paper. This has been further verified by empirical studies carried out in this paper.