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.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
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:
Contact details of provider:
Postal: 234 Wellington Street, Ottawa, Ontario, K1A 0G9, Canada
Phone: 613 782-8845
Fax: 613 782-8874
Web page: http://www.bank-banque-canada.ca/
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.:
- 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.
- Clemen, Robert T., 1989. "Combining forecasts: A review and annotated bibliography," International Journal of Forecasting, Elsevier, vol. 5(4), pages 559-583.
- Kevin Moran & Veronika Dolar, 2002. "Estimated DGE Models and Forecasting Accuracy: A Preliminary Investigation with Canadian Data," Working Papers 02-18, 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.
- 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.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: ().
If references are entirely missing, you can add them using this form.