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.
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- 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.
- Clemen, Robert T., 1989. "Combining forecasts: A review and annotated bibliography," International Journal of Forecasting, Elsevier, vol. 5(4), pages 559-583.
- Francis X. Diebold & Jose A. Lopez, 1995.
"Forecast evaluation and combination,"
9525, Federal Reserve Bank of New York.
- Francis X. Diebold & Jose A. Lopez, 1996. "Forecast Evaluation and Combination," NBER Technical Working Papers 0192, National Bureau of Economic Research, Inc.
- 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.). Full references (including those not matched with items on IDEAS)
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