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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- Durbin, James & Koopman, Siem Jan, 2001.
"Time Series Analysis by State Space Methods,"
Oxford University Press, number 9780198523543, March.
- Tom Doan, . "SEASONALDLM: RATS procedure to create the matrices for the seasonal component of a DLM," Statistical Software Components RTS00251, Boston College Department of Economics.
- Andrews, Donald W K, 1991.
"Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation,"
Econometric Society, vol. 59(3), pages 817-58, 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.
- 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.
- 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.
- Chib, Siddhartha & Greenberg, Edward, 1996.
"Markov Chain Monte Carlo Simulation Methods in Econometrics,"
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 24 Oct 1994.
- Sangjoon Kim, Neil Shephard & Siddhartha Chib, .
"Stochastic volatility: likelihood inference and comparison with ARCH models,"
W26, revised version of W, Economics Group, Nuffield College, University of Oxford.
- Kim, Sangjoon & Shephard, Neil & Chib, Siddhartha, 1998. "Stochastic Volatility: Likelihood Inference and Comparison with ARCH Models," Review of Economic Studies, Wiley Blackwell, vol. 65(3), pages 361-93, July.
- Sangjoon Kim & Neil Shephard & Siddhartha Chib, 1996. "Stochastic Volatility: Likelihood Inference And Comparison With Arch Models," Econometrics 9610002, EconWPA.
- Sangjoon Kim & Neil Shephard, 1994. "Stochastic volatility: likelihood inference and comparison with ARCH models," Economics Papers 3., Economics Group, Nuffield College, University of Oxford.
- 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.
- 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.
- 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.
- 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.
- Charles S. Bos & Neil Shephard, 2004.
"Inference for Adaptive Time Series Models: Stochastic Volatility and Conditionally Gaussian State Space Form,"
2004-W02, Economics Group, Nuffield College, University of Oxford.
- 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.
- Neil Shephard & Charles S. Bos, 2004. "Inference for adaptive time series models: stochastic volatility and conditionally Gaussian state space form," Economics Series Working Papers 2004-W02, University of Oxford, Department of Economics.
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