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Accumulated Prediction Errors, Information Criteria And Optimal Forecasting For Autoregressive Time Series

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Author Info
Ching-Kang Ing (Institute of Statistical Science, Academia Sinica)

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Abstract

The predictive capability of a modification of Rissanen's accumulated prediction error (APE) criterion, APE$_{\delta_{n}}$,is investigated in infinite-order autoregressive (AR($\infty$)) models. Instead of accumulating squares of sequential prediction errors from the beginning, APE$_{\delta_{n}}$ is obtained by summing these squared errors from stage $n\delta_{n}$, where $n$ is the sample size and $0 < \delta_{n} < 1$ may depend on $n$. Under certain regularity conditions, an asymptotic expression is derived for the mean-squared prediction error (MSPE) of an AR predictor with order determined by APE$_{\delta_{n}}$. This expression shows that the prediction performances of APE$_{\delta_{n}}$ can vary dramatically depending on the choice of $\delta_{n}$. Another interesting finding is that when $\delta_{n}$ approaches 1 at a certain rate, APE$_{\delta_{n}}$ can achieve asymptotic efficiency in most practical situations. An asymptotic equivalence between APE$_{\delta_{n}}$ and an information criterion with a suitable penalty term is also established from the MSPE point of view. It offers a new perspective for comparing the information- and prediction-based model selection criteria in AR($\infty$) models. Finally, we provide the first asymptotic efficiency result for the case when the underlying AR($\infty$) model is allowed to degenerate to a finite autoregression.

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Paper provided by EconWPA in its series Econometrics with number 0503020.

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Length: 50 pages
Date of creation: 23 Mar 2005
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Handle: RePEc:wpa:wuwpem:0503020

Note: Type of Document - pdf; pages: 50
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Web page: http://129.3.20.41

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Related research
Keywords: Accumulated prediction errors Asymptotic equivalence Asymptotic efficiency Information criterion Order selection Optimal forecasting

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Find related papers by JEL classification:
C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General
C2 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables
C3 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables
C4 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics
C5 - Mathematical and Quantitative Methods - - Econometric Modeling
C8 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs

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References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
  1. Hansen M. H & Yu B., 2001. "Model Selection and the Principle of Minimum Description Length," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 746-774, June. [Downloadable!] (restricted)
  2. Schorfheide, Frank, 2005. "VAR forecasting under misspecification," Journal of Econometrics, Elsevier, vol. 128(1), pages 99-136, September. [Downloadable!] (restricted)
  3. Ing, Ching-Kang, 2003. "Multistep Prediction In Autoregressive Processes," Econometric Theory, Cambridge University Press, vol. 19(02), pages 254-279, January. [Downloadable!]
  4. T. Speed & Bin Yu, 1993. "Model selection and prediction: Normal regression," Annals of the Institute of Statistical Mathematics, Springer, vol. 45(1), pages 35-54, March. [Downloadable!] (restricted)
  5. West, Kenneth D, 1996. "Asymptotic Inference about Predictive Ability," Econometrica, Econometric Society, vol. 64(5), pages 1067-84, September. [Downloadable!] (restricted)
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  6. Inoue, Atsushi & Kilian, Lutz, 2003. "On the Selection of Forecasting Models," CEPR Discussion Papers 3809, C.E.P.R. Discussion Papers. [Downloadable!] (restricted)
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