Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/107830
Authors: 
Barra, István
Hoogerheide, Lennart
Koopman, Siem Jan
Lucas, André
Year of Publication: 
2014
Series/Report no.: 
Tinbergen Institute Discussion Paper 14-118/III
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. A particular feature of our approach is that smoothed estimates of the states and the marginal likelihood are obtained directly as an output of the algorithm. Our method provides a computationally efficient alternative to several recently proposed algorithms. We present extensive simulation evidence for stochastic volatility and stochastic intensity models. For our empirical study, we analyse the performance of our method for stock returns and corporate default panel data. (This paper is an updated version of the paper that appeared earlier as Barra, I., Hoogerheide, L.F., Koopman, S.J., and Lucas, A. (2013) "Joint Independent Metropolis-Hastings Methods for Nonlinear Non-Gaussian State Space Models". TI Discussion Paper 13-050/III. Amsterdam: Tinbergen Institute.)
Subjects: 
Bayesian inference
importance sampling
Monte Carlo estimation
Metropolis-Hastings algorithm
mixture of Student's t-distributions
JEL: 
C11
C15
C22
C32
C58
Document Type: 
Working Paper

Files in This Item:
File
Size
480.34 kB





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.