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Block Sampler and Posterior Mode Estimation for a Nonlinear and Non-Gaussian State-Space Model with Correlated Errors

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  • Yasuhiro Omori

    (Faculty of Economics, The University of Tokyo)

  • Toshiaki Watanabe

    (Faculty of Economics, Tokyo Metropolitan University)

Abstract

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.

Suggested Citation

  • Yasuhiro Omori & Toshiaki Watanabe, 2003. "Block Sampler and Posterior Mode Estimation for a Nonlinear and Non-Gaussian State-Space Model with Correlated Errors," CIRJE F-Series CIRJE-F-221, CIRJE, Faculty of Economics, University of Tokyo.
    • Handle: RePEc:tky:fseres:2003cf221
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    Cited by:

    1. Omori, Yasuhiro & Watanabe, Toshiaki, 2008. "Block sampler and posterior mode estimation for asymmetric stochastic volatility models," Computational Statistics & Data Analysis, Elsevier, vol. 52(6), pages 2892-2910, February.
    2. Siddhartha Chib & Yasuhiro Omori & Manabu Asai, 2007. "Multivariate stochastic volatility (Revised in May 2007, Handbook of Financial Time Series (Published in "Handbook of Financial Time Series" (eds T.G. Andersen, R.A. Davis, Jens-Peter Kreiss," CARF F-Series CARF-F-094, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo.
    3. Trojan, Sebastian, 2013. "Regime Switching Stochastic Volatility with Skew, Fat Tails and Leverage using Returns and Realized Volatility Contemporaneously," Economics Working Paper Series 1341, University of St. Gallen, School of Economics and Political Science, revised Aug 2014.
    4. Catherine S. Forbes & Gael M. Martin & Jill Wright, 2007. "Inference for a Class of Stochastic Volatility Models Using Option and Spot Prices: Application of a Bivariate Kalman Filter," Econometric Reviews, Taylor & Francis Journals, vol. 26(2-4), pages 387-418.

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