On Importance Sampling for State Space Models

Autor:	Jungbacker, Borus, Koopman, Siem Jan
Jazyk:	angličtina
Rok vydání:	2005
Předmět:	Stochastic volatility model ddc:330 Likelihood function C15 Kalman filter Newton-Raphson C32 Posterior mode estimation Simulation smoothing Monte Carlo integration
Popis:	We consider likelihood inference and state estimation by means of importance sampling for state space models with a nonlinear non-Gaussian observation y ~ p(y lpha) and a linear Gaussian state alpha ~ p(alpha). The importance density is chosen to be the Laplace approximation of the smoothing density p(alpha ). We show that computationally efficient state space methods can be used to perform all necessary computations in all situations. It requires new derivations of the Kalman filter and smoother and the simulation smoother which do not rely on a linear Gaussian observation equation. Furthermore, results are presented that lead to a more effective implementation of importance sampling for state space models. An illustration is given for the stochastic volatility model with leverage.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=od______1687::73550d6c212ebaa6f8848f89e0c45619 https://hdl.handle.net/10419/86445 Zobrazit plný text záznamu