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Asymptotic Quasi-likelihood Based on Kernel Smoothing for Nonlinear and Non-Gaussian State-Space Models

This paper considers parameter estima- tion for nonlinear and non-Gaussian state-space mod- els with correlation. We propose an asymptotic quasi- likelihood (AQL) approach which utilises a nonpara- metric kernel estimator of the conditional variance co- variances matrix §t to replace the true §t in the stan- dard quasi-likelihood. The kernel estimation avoids the risk of potential miss-speciflcation of §t and thus make the parameter estimator more robust. This has been further verifled by empirical studies carried out in this paper.