Efficient Bayesian estimation of multivariate state space models
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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- Harvey, Andrew C. & Trimbur, Thomas M. & Van Dijk, Herman K., 2007.
"Trends and cycles in economic time series: A Bayesian approach,"
Journal of Econometrics,
Elsevier, vol. 140(2), pages 618-649, October.
- 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.
- 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.
- Chris M Strickland & Gael Martin & Catherine S Forbes, 2006. "Parameterisation and Efficient MCMC Estimation of Non-Gaussian State Space Models," Monash Econometrics and Business Statistics Working Papers 22/06, Monash University, Department of Econometrics and Business Statistics.
- 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.
- Siddhartha Chib & Edward Greenberg, 1994. "Markov Chain Monte Carlo Simulation Methods in Econometrics," Econometrics 9408001, EconWPA, revised 23 Feb 1995.
- 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.
- Siem Jan Koopman & André Lucas, 2003. "Business and Default Cycles for Credit Risk," Tinbergen Institute Discussion Papers 03-062/2, Tinbergen Institute, revised 09 Jan 2003.
- 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.
- Strickland, Chris M. & Forbes, Catherine S. & Martin, Gael M., 2006. "Bayesian analysis of the stochastic conditional duration model," Computational Statistics & Data Analysis, Elsevier, vol. 50(9), pages 2247-2267, May.
- 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.
- Andrews, Donald W K, 1991. "Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation," Econometrica, Econometric Society, vol. 59(3), pages 817-858, May.
- 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.
- Durbin, James & Koopman, Siem Jan, 2012. "Time Series Analysis by State Space Methods," OUP Catalogue, Oxford University Press, edition 2, number 9780199641178.
- Durbin, James & Koopman, Siem Jan, 2001. "Time Series Analysis by State Space Methods," OUP Catalogue, Oxford University Press, number 9780198523543.
- Tom Doan, "undated". "SEASONALDLM: RATS procedure to create the matrices for the seasonal component of a DLM," Statistical Software Components RTS00251, Boston College Department of Economics.
- Charles Bos & Neil Shephard, 2006. "Inference for Adaptive Time Series Models: Stochastic Volatility and Conditionally Gaussian State Space Form," Econometric Reviews, Taylor & Francis Journals, vol. 25(2-3), pages 219-244.
- Charles S. Bos & Neil Shephard, 2004. "Inference for Adaptive Time Series Models: Stochastic Volatility and Conditionally Gaussian State Space form," Tinbergen Institute Discussion Papers 04-015/4, Tinbergen Institute.
- 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.
- 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. Full references (including those not matched with items on IDEAS)
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