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A Note on the Selection of Time Series Models

Author

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  • Serena Ng

    () (Boston College)

  • Pierre Perron

    () (Boston University
    C.R.D.E., Universite de Montreal)

Abstract

We consider issues related to the order of an autoregression selected using information criteria. We study the sensitivity of the estimated order to i) whether the effective number of observations is held fixed when estimating models of different order, ii) whether the estimate of the variance is adjusted for degrees of freedom, and iii) how the penalty for overfitting is defined in relation to the total sample size. Simulations show that the lag length selected by both the Akaike and the Schwarz information criteria are sensitive to these parameters in finite samples. The methods that give the most precise estimates are those that hold the effective sample size fixed across models to be compared. Theoretical considerations reveal that this is indeed necessary for valid model comparisons. Guides to robust model selection are provided.

Suggested Citation

  • Serena Ng & Pierre Perron, 2001. "A Note on the Selection of Time Series Models," Boston College Working Papers in Economics 500, Boston College Department of Economics.
  • Handle: RePEc:boc:bocoec:500
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    References listed on IDEAS

    as
    1. Geweke, John & Meese, Richard, 1981. "Estimating regression models of finite but unknown order," Journal of Econometrics, Elsevier, vol. 16(1), pages 162-162, May.
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    Keywords

    Information Criteria; AIC; BIC; lag length selection;

    JEL classification:

    • F30 - International Economics - - International Finance - - - General
    • F40 - International Economics - - Macroeconomic Aspects of International Trade and Finance - - - General
    • C2 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables
    • C3 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables
    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling

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