Joint Bayesian Analysis of Parameters and States in Nonlinear, Non-Gaussian State Space Models

Research output: Contribution to JournalArticleAcademicpeer-review

Abstract

We propose a new methodology for designing flexible proposal densities for the joint posterior density of parameters and states in a nonlinear, non-Gaussian state space model. We show that a highly efficient Bayesian procedure emerges when these proposal densities are used in an independent Metropolis-Hastings algorithm or in importance sampling. Our method provides a computationally more efficient alternative to several recently proposed algorithms. We present extensive simulation evidence for stochastic intensity and stochastic volatility models based on Ornstein-Uhlenbeck processes. For our empirical study, we analyse the performance of our methods for corporate default panel data and stock index returns.
Original languageEnglish
Pages (from-to)1003-1026
Number of pages24
JournalJournal of Applied Econometrics
Volume32
Issue number5
DOIs
Publication statusPublished - 2017

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