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Asymptotic quasi-likelihood based on kernel smoothing for multivariate heteroskedastic models with correlation

Journal Article


Abstract


  • This paper considers parameter estimation for nonlinear and non-Gaussian state-space models with correlation. We propose an asymptotic quasilikelihood (AQL) approach which utilises a nonparametric kernel estimator of the conditional variance covariances matrix §t to replace the true §t in the standard quasi-likelihood. The kernel estimation avoids the risk of potential miss-speci¯cation of §t and thus make the parameter estimator more robust. This has been further veri¯ed by empirical studies carried out in this paper.

Authors


  •   Alzghool, Raed (external author)
  •   Lin, Yan-Xia
  •   Chen, Sylvia Xiao (external author)

Publication Date


  • 2010

Citation


  • Alzghool, R., Lin, Y. & Chen, S. (2010). Asymptotic quasi-likelihood based on kernel smoothing for multivariate heteroskedastic models with correlation. American Journal of Mathematical and Management Sciences, 30 (1&2), 147-177.

Scopus Eid


  • 2-s2.0-79958726512

Ro Metadata Url


  • http://ro.uow.edu.au/infopapers/3728

Has Global Citation Frequency


Number Of Pages


  • 30

Start Page


  • 147

End Page


  • 177

Volume


  • 30

Issue


  • 1&2

Abstract


  • This paper considers parameter estimation for nonlinear and non-Gaussian state-space models with correlation. We propose an asymptotic quasilikelihood (AQL) approach which utilises a nonparametric kernel estimator of the conditional variance covariances matrix §t to replace the true §t in the standard quasi-likelihood. The kernel estimation avoids the risk of potential miss-speci¯cation of §t and thus make the parameter estimator more robust. This has been further veri¯ed by empirical studies carried out in this paper.

Authors


  •   Alzghool, Raed (external author)
  •   Lin, Yan-Xia
  •   Chen, Sylvia Xiao (external author)

Publication Date


  • 2010

Citation


  • Alzghool, R., Lin, Y. & Chen, S. (2010). Asymptotic quasi-likelihood based on kernel smoothing for multivariate heteroskedastic models with correlation. American Journal of Mathematical and Management Sciences, 30 (1&2), 147-177.

Scopus Eid


  • 2-s2.0-79958726512

Ro Metadata Url


  • http://ro.uow.edu.au/infopapers/3728

Has Global Citation Frequency


Number Of Pages


  • 30

Start Page


  • 147

End Page


  • 177

Volume


  • 30

Issue


  • 1&2