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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

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. Copyright 2006 Royal Statistical Society.

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Bibliographic Info

Article provided by Royal Statistical Society in its journal Journal of the Royal Statistical Society Series C.

Volume (Year): 55 (2006)
Issue (Month): 2 ()
Pages: 201-224

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Handle: RePEc:bla:jorssc:v:55:y:2006:i:2:p:201-224

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Cited by:
  1. Søren Johansen & Marco Riani & Anthony C. Atkinson, 2012. "The Selection of ARIMA Models with or without Regressors," CREATES Research Papers 2012-46, School of Economics and Management, University of Aarhus.
  2. 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.

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