Evaluating Linear and Non-Linear Time-Varying Forecast-Combination Methods
AbstractThis 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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Bibliographic InfoPaper provided by Bank of Canada in its series Working Papers with number 01-12.
Length: 24 pages
Date of creation: 2001
Date of revision:
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Econometric and statistical methods;
Find related papers by JEL classification:
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
- C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
- E27 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Forecasting and Simulation: Models and Applications
This paper has been announced in the following NEP Reports:
- NEP-ALL-2001-12-26 (All new papers)
- NEP-CMP-2001-12-26 (Computational Economics)
- NEP-ECM-2001-12-26 (Econometrics)
- NEP-ETS-2001-12-26 (Econometric Time Series)
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- 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, 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.).
- 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.
- Frédérick Demers & Annie De Champlain, 2005. "Forecasting Core Inflation in Canada: Should We Forecast the Aggregate or the Components?," Working Papers 05-44, Bank of Canada.
- Shamiri, Ahmed & Shaari, Abu Hassan & Isa, Zaidi, 2008. "Comparing the accuracy of density forecasts from competing GARCH models," MPRA Paper 13662, University Library of Munich, Germany.
- Kevin Moran & Veronika Dolar, 2002. "Estimated DGE Models and Forecasting Accuracy: A Preliminary Investigation with Canadian Data," Working Papers 02-18, Bank of Canada.
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