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Efficient Bayesian estimation of multivariate state space models

  • Strickland, Chris M.
  • Turner, Ian. W.
  • Denham, Robert
  • Mengersen, Kerrie L.
Registered author(s):

    A Bayesian Markov chain Monte Carlo methodology is developed for the estimation of multivariate linear Gaussian state space models. In particular, an efficient simulation smoothing algorithm is proposed that makes use of the univariate representation of the state space model. Substantial gains over existing algorithms in computational efficiency are achieved using the new simulation smoother for the analysis of high dimensional multivariate time series. The methodology is used to analyse a multivariate time series dataset of the Normalised Difference Vegetation Index (NDVI), which is a proxy for the level of live vegetation, for a particular grazing property located in Queensland, Australia.

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    File URL: http://www.sciencedirect.com/science/article/B6V8V-4W8VVXN-3/2/dd258f45be8b69881769772b7c7157e2
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    Article provided by Elsevier in its journal Computational Statistics & Data Analysis.

    Volume (Year): 53 (2009)
    Issue (Month): 12 (October)
    Pages: 4116-4125

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    Handle: RePEc:eee:csdana:v:53:y:2009:i:12:p:4116-4125
    Contact details of provider: Web page: http://www.elsevier.com/locate/csda

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    1. Charles S. Bos & Neil Shephard, 2004. "Inference for Adaptive Time Series Models: Stochastic Volatility and Conditionally Gaussian State Space Form," Economics Papers 2004-W02, Economics Group, Nuffield College, University of Oxford.
    2. Donald W.K. Andrews, 1988. "Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation," Cowles Foundation Discussion Papers 877R, Cowles Foundation for Research in Economics, Yale University, revised Jul 1989.
    3. Sangjoon Kim & Neil Shephard & Siddhartha Chib, 1996. "Stochastic Volatility: Likelihood Inference And Comparison With Arch Models," Econometrics 9610002, EconWPA.
    4. Chib, Siddhartha & Greenberg, Edward, 1996. "Markov Chain Monte Carlo Simulation Methods in Econometrics," Econometric Theory, Cambridge University Press, vol. 12(03), pages 409-431, August.
    5. Chris M. Strickland & Catherine S. Forbes & Gael M. Martin, 2003. "Bayesian Analysis of the Stochastic Conditional Duration Model," Monash Econometrics and Business Statistics Working Papers 14/03, Monash University, Department of Econometrics and Business Statistics.
    6. Harvey, A.C. & Trimbur, T.M. & van Dijk, H.K., 2005. "Trends and cycles in economic time series: A Bayesian approach," Econometric Institute Research Papers EI 2005-27, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    7. André Lucas & Siem Jan Koopman, 2005. "Business and default cycles for credit risk," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 20(2), pages 311-323.
    8. Strickland, Chris M. & Martin, Gael M. & Forbes, Catherine S., 2008. "Parameterisation and efficient MCMC estimation of non-Gaussian state space models," Computational Statistics & Data Analysis, Elsevier, vol. 52(6), pages 2911-2930, February.
    9. Durbin, James & Koopman, Siem Jan, 2001. "Time Series Analysis by State Space Methods," OUP Catalogue, Oxford University Press, number 9780198523543, March.
    10. Fernandez, F Javier & Harvey, Andrew C, 1990. "Seemingly Unrelated Time Series Equations and a Test for Homogeneity," Journal of Business & Economic Statistics, American Statistical Association, vol. 8(1), pages 71-81, January.
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