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Bayesian Subset Model Selection for Time Series

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  • N. K. Unnikrishnan

Abstract

. This paper considers the problem of subset model selection for time series. In general, a few lags which are not necessarily continuous, explain lag structure of a time‐series model. Using the reversible jump Markov chain technique, the paper develops a fully Bayesian solution for the problem. The method is illustrated using the self‐exciting threshold autoregressive (SETAR), bilinear and AR models. The Canadian lynx data, the Wolfe's sunspot numbers and Series A of Box and Jenkins (1976) are analysed in detail.

Suggested Citation

  • N. K. Unnikrishnan, 2004. "Bayesian Subset Model Selection for Time Series," Journal of Time Series Analysis, Wiley Blackwell, vol. 25(5), pages 671-690, September.
    • Handle: RePEc:bla:jtsera:v:25:y:2004:i:5:p:671-690
    DOI: 10.1111/j.1467-9892.2004.01874.x
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    1. Barnett, Glen & Kohn, Robert & Sheather, Simon, 1996. "Bayesian estimation of an autoregressive model using Markov chain Monte Carlo," Journal of Econometrics, Elsevier, vol. 74(2), pages 237-254, October.
    2. Sylvia. Richardson & Peter J. Green, 1997. "On Bayesian Analysis of Mixtures with an Unknown Number of Components (with discussion)," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 59(4), pages 731-792.
    3. Robert E. McCulloch & Ruey S. Tsay, 1994. "Bayesian Analysis Of Autoregressive Time Series Via The Gibbs Sampler," Journal of Time Series Analysis, Wiley Blackwell, vol. 15(2), pages 235-250, March.
    4. Gwo‐Hsing Yu & Yow‐Chang Lin, 1991. "A Methodology For Selecting Subset Autoregressive Time Series Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 12(4), pages 363-373, July.
    5. John Geweke & Nobuhiko Terui, 1993. "Bayesian Threshold Autoregressive Models For Nonlinear Time Series," Journal of Time Series Analysis, Wiley Blackwell, vol. 14(5), pages 441-454, September.
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