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

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  • Jaditz, Ted
  • Sayers, Chera L
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    Abstract

    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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    Bibliographic Info

    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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    Cited by:
    1. Theodore Panagiotidis, 2010. "An Out-of-Sample Test for Nonlinearity in Financial Time Series: An Empirical Application," Computational Economics, Society for Computational Economics, vol. 36(2), pages 121-132, August.
    2. Ilias Lekkos & Costas Milas & Theodore Panagiotidis, 2006. "Forecasting interest rate swap spreads using domestic and international risk factors: Evidence from linear and non-linear models," Discussion Paper Series 2006_6, Department of Economics, Loughborough University, revised Mar 2006.
    3. Fernando Fernández-Rodríguez & Simón Sosvilla-Rivero & Julián Andrada-Félix, . "Nearest-Neighbour Predictions in Foreign Exchange Markets," Working Papers 2002-05, FEDEA.

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