Evaluating Linear and Non-Linear Time-Varying Forecast-Combination Methods
This paper evaluates linear and non-linear forecast-combination methods. Among the non-linear methods, we propose a nonparametric kernel-regression weighting approach that allows maximum flexibility of the weighting parameters. A Monte Carlo simulation study is performed to compare the performance of the different weighting schemes. The simulation results show that the non-linear combination methods are superior in all scenarios considered. When forecast errors are correlated across models, the nonparametric weighting scheme yields the lowest mean-squared errors. When no such correlation exists, forecasts combined using artificial neural networks are superior.
|Date of creation:||2001|
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- Francis X. Diebold & Peter Pauly, 1986. "Structural change and the combination of forecasts," Special Studies Papers 201, Board of Governors of the Federal Reserve System (U.S.).
- Clemen, Robert T., 1989. "Combining forecasts: A review and annotated bibliography," International Journal of Forecasting, Elsevier, vol. 5(4), pages 559-583.
- Clemon, Robert T & Winkler, Robert L, 1986. "Combining Economic Forecasts," Journal of Business & Economic Statistics, American Statistical Association, vol. 4(1), pages 39-46, January.
- Francis X. Diebold & Jose A. Lopez, 1995.
"Forecast evaluation and combination,"
9525, Federal Reserve Bank of New York.
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