Block Sampler and Posterior Mode Estimation for a Nonlinear and Non-Gaussian State-Space Model with Correlated Errors
In a linear Gaussian state-space time series analysis, a disturbance smoother and a simula-tion smoother are widely used procedures for smoothing and sampling state or disturbance vectors given observations. Several smoothing procedures are also proposed for a non-Gaussian observation process. However, it is assumed that a state equation is linear and that an observation vector and a state vector are conditionally independent. These as-sumptions often need to be relaxed in the analysis of real data. Thus this article considers a general state-space model with a non-Gaussian observation process and a nonlinear state equation where an observation vector and a state vector are allowed to be dependent. We describe a disturbance smoother and a simulation smoother for such models and give numerical examples using simulated data and real data.
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|Date of creation:||May 2003|
|Date of revision:|
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- J. Durbin, 2002. "A simple and efficient simulation smoother for state space time series analysis," Biometrika, Biometrika Trust, vol. 89(3), pages 603-616, August.
- Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, vol. 59(2), pages 347-70, March.
- Durbin, J. & Koopman, S.J.M., 1998.
"Time Series Analysis of Non-Gaussian Observations Based on State Space Models from Both Classical and Bayesian Perspectives,"
1998-142, Tilburg University, Center for Economic Research.
- J. Durbin & S. J. Koopman, 2000. "Time series analysis of non-Gaussian observations based on state space models from both classical and Bayesian perspectives," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 62(1), pages 3-56.
- Sangjoon Kim & Neil Shephard & Siddhartha Chib, 1998. "Stochastic Volatility: Likelihood Inference and Comparison with ARCH Models," Review of Economic Studies, Oxford University Press, vol. 65(3), pages 361-393.
- Fahrmeir, Ludwig & Wagenpfeil, Stefan, 1997. "Penalized likelihood estimation and iterative Kalman smoothing for non-Gaussian dynamic regression models," Computational Statistics & Data Analysis, Elsevier, vol. 24(3), pages 295-320, May.
- Hisashi Tanizaki, 2001. "Nonlinear and Non-Gaussian State Space Modeling Using Sampling Techniques," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 53(1), pages 63-81, March.
- Sanford, Andrew D. & Martin, Gael M., 2005. "Simulation-based Bayesian estimation of an affine term structure model," Computational Statistics & Data Analysis, Elsevier, vol. 49(2), pages 527-554, April.
- Harvey, Andrew C & Shephard, Neil, 1996. "Estimation of an Asymmetric Stochastic Volatility Model for Asset Returns," Journal of Business & Economic Statistics, American Statistical Association, vol. 14(4), pages 429-34, October.
- Jacquier, Eric & Polson, Nicholas G. & Rossi, P.E.Peter E., 2004. "Bayesian analysis of stochastic volatility models with fat-tails and correlated errors," Journal of Econometrics, Elsevier, vol. 122(1), pages 185-212, September.
- Andrew D. Sanford & Gael M. Martin, 2003. "Simulation-Based Bayesian Estimation of Affine Term Structure Models," Monash Econometrics and Business Statistics Working Papers 15/03, Monash University, Department of Econometrics and Business Statistics.
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