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Choosing Lag Lengths in Nonlinear Dynamic Models

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  • Heather M. Anderson

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Abstract

Given that it is quite impractical to use standard model selection criteria in a nonlinear modeling context, the builders of nonlinear models often choose lag length by setting it equal to the lag length chosen for a linear autoregression of the data. This paper studies the performance of this procedure in a variety of circumstances, and then proposes some new and simple model selection procedures, based on linear approximations of the nonlinear forms. The idea here is to apply standard selection criteria to these linear approximations, rather than to autoregressions that make no provision for nonlinear behavior. A simulation study compares the properties of these proposed procedures with the properties of linear selection procedures.

Suggested Citation

  • Heather M. Anderson, 2002. "Choosing Lag Lengths in Nonlinear Dynamic Models," Monash Econometrics and Business Statistics Working Papers 21/02, Monash University, Department of Econometrics and Business Statistics.
  • Handle: RePEc:msh:ebswps:2002-21
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    File URL: http://www.buseco.monash.edu.au/ebs/pubs/wpapers/2002/wp21-02.pdf
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    References listed on IDEAS

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    1. Terasvirta, T & Anderson, H M, 1992. "Characterizing Nonlinearities in Business Cycles Using Smooth Transition Autoregressive Models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 7(S), pages 119-136, Suppl. De.
    2. Beaudry, Paul & Koop, Gary, 1993. "Do recessions permanently change output?," Journal of Monetary Economics, Elsevier, vol. 31(2), pages 149-163, April.
    3. Potter, Simon M, 1995. "A Nonlinear Approach to US GNP," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 10(2), pages 109-125, April-Jun.
    4. Anderson, Heather M. & Vahid, Farshid, 2001. "Predicting The Probability Of A Recession With Nonlinear Autoregressive Leading-Indicator Models," Macroeconomic Dynamics, Cambridge University Press, vol. 5(04), pages 482-505, September.
    5. Lee, Tae-Hwy & White, Halbert & Granger, Clive W. J., 1993. "Testing for neglected nonlinearity in time series models : A comparison of neural network methods and alternative tests," Journal of Econometrics, Elsevier, vol. 56(3), pages 269-290, April.
    6. Hamilton, James D, 1989. "A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle," Econometrica, Econometric Society, vol. 57(2), pages 357-384, March.
    7. C. W. Granger & E. Maasoumi & J. Racine, 2004. "A Dependence Metric for Possibly Nonlinear Processes," Journal of Time Series Analysis, Wiley Blackwell, vol. 25(5), pages 649-669, September.
    8. Philip Rothman, 1998. "Forecasting Asymmetric Unemployment Rates," The Review of Economics and Statistics, MIT Press, vol. 80(1), pages 164-168, February.
    9. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
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    Cited by:

    1. Mavromaras, Kostas & Polidano, Cain, 2011. "NILS Working paper no 165. Improving the employment rates of people with disabilities through vocational education," NILS Working Papers 26068, National Institute of Labour Studies.
    2. Mavromaras, Kostas G. & Polidano, Cain, 2011. "Improving the Employment Rates of People with Disabilities through Vocational Education," IZA Discussion Papers 5548, Institute for the Study of Labor (IZA).

    More about this item

    Keywords

    Nonlinear time series models; Neural networks; Model selection criteria; Polynomial approximations; Volterra expansions.;

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation

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