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Out-of-Sample Forecasts and Nonlinear Model Selection with an Example of the Term Structure of Interest Rates

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  • Yamei Liu

    () (Decision Science Group, 3CCOC)

  • Walter Enders

    () (University of Alabama)

Abstract

It is well known that goodness-of-fit measures lead to overfitting. We compare the small-sample properties of linear and several nonlinear models using a Monte Carlo study. A large number of linear series are generated and conventional methods of fitting nonlinear models are applied to each. The best linear and nonlinear models are compared using in-sample and out-of-sample criteria. Out-of-sample forecasts are shown to be superior for selecting the proper specification. The experiment is repeated using a nonlinear model and the in-sample fit and forecasts of the various models are compared. An example is provided using the term structure of interest rates.

Suggested Citation

  • Yamei Liu & Walter Enders, 2003. "Out-of-Sample Forecasts and Nonlinear Model Selection with an Example of the Term Structure of Interest Rates," Southern Economic Journal, Southern Economic Association, vol. 69(3), pages 520-540, January.
  • Handle: RePEc:sej:ancoec:v:69:3:y:2003:p:520-540
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    Cited by:

    1. Qin, Ting & Enders, Walter, 2008. "In-sample and out-of-sample properties of linear and nonlinear Taylor rules," Journal of Macroeconomics, Elsevier, vol. 30(1), pages 428-443, March.

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