POSTERIOR MODE ESTIMATION FOR NONLINEAR AND NON-GAUSSIAN STATE SPACE MODELS

In this paper, we develop a posterior mode estimation method for nonlinear and non-Gaussian state space models. By exploiting special structures of the state space models, we derive a modified quadratic hill-climbing procedure which can be implemented efficiently in O(n) operations. The method can b...

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Veröffentlicht in:Statistica Sinica 2003-01, Vol.13 (1), p.255-274
1. Verfasser: So, Mike K. P.
Format: Artikel
Sprache:eng
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Zusammenfassung:In this paper, we develop a posterior mode estimation method for nonlinear and non-Gaussian state space models. By exploiting special structures of the state space models, we derive a modified quadratic hill-climbing procedure which can be implemented efficiently in O(n) operations. The method can be used for estimating the state variable, performing Bayesian inference and carrying out Monte Carlo likelihood inference. Numerical illustrations using simulated and real data demonstrate that our procedure is much more efficient than a common gradient method. It is also evident that our method works very well in a new stochastic volatility model which contains a nonlinear state equation.
ISSN:1017-0405
1996-8507