Monte Carlo estimation for nonlinear non-Gaussian state space models

B.M.J.P. Jungbacker, S.J. Koopman

Research output: Contribution to JournalArticleAcademic


We develop a proposal or importance density for state space models with a nonlinear non-Gaussian observation vector y ∼ p(yθ) and an unobserved linear Gaussian signal vector θ ∼ p(θ). The proposal density is obtained from the Laplace approximation of the smoothing density p(θy). We present efficient algorithms to calculate the mode of p(θy) and to sample from the proposal density. The samples can be used for importance sampling and Markov chain Monte Carlo methods. The new results allow the application of these methods to state space models where the observation density p(yθ) is not log-concave. Additional results are presented that lead to computationally efficient implementations. We illustrate the methods for the stochastic volatility model with leverage. © 2007 Biometrika Trust.
Original languageEnglish
Pages (from-to)827-839
Publication statusPublished - 2007


Dive into the research topics of 'Monte Carlo estimation for nonlinear non-Gaussian state space models'. Together they form a unique fingerprint.

Cite this