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The Estimation Of The Order Of An Autoregression Using Recursive Residuals And Cross‐Validation

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  • L. Kavalieris

Abstract

. Several criteria for the estimation of the order of an autoregressive representation of a stationary time series are examined. There need not be a true finite‐order autoregression model for the data, so that the purpose of model identification is to obtain an adequate representation of the data. It is proved that minimizing the sum of squares of recursive residuals (the ‘predictive minimizing description length’) is equivalent to minimizing BIC. The equivalence between the cross‐validation and Akaike information criterion methods of autoregressive modelling is also established.

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  • L. Kavalieris, 1989. "The Estimation Of The Order Of An Autoregression Using Recursive Residuals And Cross‐Validation," Journal of Time Series Analysis, Wiley Blackwell, vol. 10(3), pages 271-281, May.
    • Handle: RePEc:bla:jtsera:v:10:y:1989:i:3:p:271-281
    DOI: 10.1111/j.1467-9892.1989.tb00028.x
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    Cited by:

    1. Eleni Constantinou & Robert Georgiades & Avo Kazandjian & Georgios P. Kouretas, 2006. "Regime switching and artificial neural network forecasting of the Cyprus Stock Exchange daily returns," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 11(4), pages 371-383.
    2. Eleni Constantinou & Robert Georgiades & Avo Kazandjian & George Kouretas, 2005. "Regime Switching and Artificial Neural Network Forecasting," Working Papers 0502, University of Crete, Department of Economics.
    3. F. Gonzalez Miranda & N. Burgess, 1997. "Modelling market volatilities: the neural network perspective," The European Journal of Finance, Taylor & Francis Journals, vol. 3(2), pages 137-157.
    4. Kanas, Angelos & Yannopoulos, Andreas, 2001. "Comparing linear and nonlinear forecasts for stock returns," International Review of Economics & Finance, Elsevier, vol. 10(4), pages 383-398, December.

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