Istvan Barra
;
Lennart F. Hoogerheide
;
Siem Jan Koopman
;
Andre Lucas

joint bayesian analysis of parameters and states in nonlinear non-gaussian state space models (replication data)

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.

Data and Resources

Suggested Citation

Barra, Istvan; Hoogerheide, Lennart F.; Koopman, Siem Jan; Lucas, Andre (2016): Joint Bayesian Analysis of Parameters and States in Nonlinear non-Gaussian State Space Models (replication data). Version: 1. Journal of Applied Econometrics. Dataset. https://jda-test.zbw.eu/dataset/joint-bayesian-analysis-of-parameters-and-states-in-nonlinear-nongaussian-state-space-models