Joint Bayesian Analysis of Parameters and States in Nonlinear non-Gaussian State Space Models
Autor: | Istvan Barra, Lennart F. Hoogerheide, Siem Jan Koopman, Andre Lucas |
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Rok vydání: | 2016 |
Předmět: |
Economics and Econometrics
State-space representation Stochastic volatility Computer science Gaussian 05 social sciences Bayesian probability Bayesian inference 01 natural sciences 010104 statistics & probability symbols.namesake Metropolis–Hastings algorithm 0502 economics and business symbols Applied mathematics State space 0101 mathematics Social Sciences (miscellaneous) Importance sampling 050205 econometrics |
Zdroj: | Journal of Applied Econometrics. 32:1003-1026 |
ISSN: | 0883-7252 |
Popis: | Summary 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. Copyright © 2016 John Wiley & Sons, Ltd. |
Databáze: | OpenAIRE |
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