Autoregressive model selection based on a prediction perspective

Yun Huan Lee, Chun Shu Chen

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


The autoregressive (AR) model is a popular method for fitting and prediction in analyzing time-dependent data, where selecting an accurate model among considered orders is a crucial issue. Two commonly used selection criteria are the Akaike information criterion and the Bayesian information criterion. However, the two criteria are known to suffer potential problems regarding overfit and underfit, respectively. Therefore, using them would perform well in some situations, but poorly in others. In this paper, we propose a new criterion in terms of the prediction perspective based on the concept of generalized degrees of freedom for AR model selection. We derive an approximately unbiased estimator of mean-squared prediction errors based on a data perturbation technique for selecting the order parameter, where the estimation uncertainty involved in a modeling procedure is considered. Some numerical experiments are performed to illustrate the superiority of the proposed method over some commonly used order selection criteria. Finally, the methodology is applied to a real data example to predict the weekly rate of return on the stock price of Taiwan Semiconductor Manufacturing Company and the results indicate that the proposed method is satisfactory.

Original languageEnglish
Pages (from-to)913-922
Number of pages10
JournalJournal of Applied Statistics
Issue number4
StatePublished - Apr 2012


  • Akaike information criterion
  • Bayesian information criterion
  • generalized degrees of freedom
  • mean-squared prediction error
  • time series


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