Out-of-Sample Forecast Performance as a Test for Nonlinearity in Time Series
AbstractThis 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 InfoArticle provided by American Statistical Association in its journal Journal of Business and Economic Statistics.
Volume (Year): 16 (1998)
Issue (Month): 1 (January)
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- Costas Milas & Ilias Lekkos & Theodore Panagiotidis, 2006.
"Forecasting interest rate swap spreads using domestic and international risk factors: Evidence from linear and non-linear models,"
Keele Economics Research Papers
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2010_08, Department of Economics, University of Macedonia, revised Jun 2010.
- 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.
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