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Best subset selection of autoregressive models with exogenous variables and generalized autoregressive conditional heteroscedasticity errors

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  • Mike K. P. So
  • Cathy W. S. Chen
  • Feng‐Chi Liu

Abstract

Summary. We develop an efficient way to select the best subset autoregressive model with exogenous variables and generalized autoregressive conditional heteroscedasticity errors. One main feature of our method is to select important autoregressive and exogenous variables, and at the same time to estimate the unknown parameters. The method proposed uses the stochastic search idea. By adopting Markov chain Monte Carlo techniques, we can identify the best subset model from a large of number of possible choices. A simulation experiment shows that the method is very effective. Misspecification in the mean equation can also be detected by our model selection method. In the application to the stock‐market data of seven countries, the lagged 1 US return is found to have a strong influence on the other stock‐market returns.

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  • Mike K. P. So & Cathy W. S. Chen & Feng‐Chi Liu, 2006. "Best subset selection of autoregressive models with exogenous variables and generalized autoregressive conditional heteroscedasticity errors," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 55(2), pages 201-224, April.
    • Handle: RePEc:bla:jorssc:v:55:y:2006:i:2:p:201-224
    DOI: 10.1111/j.1467-9876.2006.00535.x
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    Cited by:

    1. Søren Johansen & Marco Riani & Anthony C. Atkinson, 2012. "The Selection of ARIMA Models with or without Regressors," Discussion Papers 12-17, University of Copenhagen. Department of Economics.
    2. Alexander Vosseler & Enzo Weber, 2018. "Forecasting seasonal time series data: a Bayesian model averaging approach," Computational Statistics, Springer, vol. 33(4), pages 1733-1765, December.
    3. Yip, Iris W.H. & So, Mike K.P., 2009. "Simplified specifications of a multivariate generalized autoregressive conditional heteroscedasticity model," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 80(2), pages 327-340.
    4. Rodolfo Angelo Magtanggol Iii De Guzman & Mike K. P. So, 2018. "Empirical Analysis Of Bitcoin Prices Using Threshold Time Series Models," Annals of Financial Economics (AFE), World Scientific Publishing Co. Pte. Ltd., vol. 13(04), pages 1-24, December.
    5. Ayman A. Amin & Walid Emam & Yusra Tashkandy & Christophe Chesneau, 2023. "Bayesian Subset Selection of Seasonal Autoregressive Models," Mathematics, MDPI, vol. 11(13), pages 1-13, June.

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