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Out-of-Sample Forecast Performance as a Test for Nonlinearity in Time Series

  • Jaditz, Ted
  • Sayers, Chera L
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    This article uses a local-information, near-neighbor forecasting methodology as a prediction test for evidence of a noisy, chaotic data-generating process underlying the Divisia monetary-aggregate series. Using a nonparametric method known to perform well with low-dimensional chaotic processes infected by noise, accompanied by a robust test of forecast performance evaluation, the authors compare out-of-sample forecasting accuracy from the local-information method to forecasting accuracy from the best fitting global linear model. Their results fail to substantiate previous claims for determinism in the Divisia monetary-aggregate series because the degree of forecast improvement obtained by the local-information method is not consistent with the hypothesis of a low-dimensional attractor underlying the Divisia data.

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    Article provided by American Statistical Association in its journal Journal of Business and Economic Statistics.

    Volume (Year): 16 (1998)
    Issue (Month): 1 (January)
    Pages: 110-17

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    Handle: RePEc:bes:jnlbes:v:16:y:1998:i:1:p:110-17
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