Choosing among Competing Econometric Forecasts: Regression-Based Forecast Combination Using Model Selection
AbstractForecast combination based on a model selection approach is discussed and evaluated. In addition, a combination approach based on ex ante predictive ability is outlined. The model selection approach which we examine is based on the use of Schwarz (SIC) or the Akaike (AIC) Information Criteria. Monte Carlo experiments based on combination forecasts constructed using possibly (misspecified) models suggest that the SIC offers a potentially useful combination approach, and that further investigation is warranted. For example, combination forecasts from a simple averaging approach MSE-dominate SIC combination forecasts less than 25% of the time in most cases, while other "standard" combination approaches fare even worse. Alternative combination approaches are also compared by conducting forecasting experiments using nine US macroeconomic variables. In particular, artificial neural networks (ANN), linear models, and professional forecasts are used to form real-time forecasts of the variables, and it is shown via a series of experiments that SIC, t-statistic, and averaging combination approaches dominate various other combination approaches. An additional finding is that while ANN models may not MSE-dominate simpler linear models, combinations of forecasts from these two models outperform either individual forecast, for a subset of the economic variables examined. Copyright © 2001 by John Wiley & Sons, Ltd.
Download InfoTo our knowledge, this item is not available for download. To find whether it is available, there are three options:
1. Check below under "Related research" whether another version of this item is available online.
2. Check on the provider's web page whether it is in fact available.
3. Perform a search for a similarly titled item that would be available.
Bibliographic InfoArticle provided by John Wiley & Sons, Ltd. in its journal Journal of Forecasting.
Volume (Year): 20 (2001)
Issue (Month): 6 (September)
Contact details of provider:
Web page: http://www3.interscience.wiley.com/cgi-bin/jhome/2966
You can help add them by filling out this form.
CitEc Project, subscribe to its RSS feed for this item.
- Joshua Gallin & Randal Verbrugge, 2007. "Improving the CPI’s Age-Bias Adjustment: Leverage, Disaggregation and Model Averaging," Working Papers 411, U.S. Bureau of Labor Statistics.
- Marco Aiolfi & Carlos Capistrán & Allan Timmermann, 2010.
2010-04, Banco de México.
- Inoue, Atsushi & Kilian, Lutz, 2006.
"On the selection of forecasting models,"
Journal of Econometrics,
Elsevier, vol. 130(2), pages 273-306, February.
- Carlos Capistrán & Allan Timmermann, 2006.
"Forecast Combination with Entry and Exit of Experts,"
2006-08, Banco de México.
- CapistrÃ¡n, Carlos & Timmermann, Allan, 2009. "Forecast Combination With Entry and Exit of Experts," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(4), pages 428-440.
- Carlos Capistrán & Allan Timmermann, 2008. "Forecast Combination With Entry and Exit of Experts," CREATES Research Papers 2008-55, School of Economics and Management, University of Aarhus.
- Antonis Michis, 2012. "Monitoring Forecasting Combinations with Semiparametric Regression Models," Working Papers 2012-02, Central Bank of Cyprus.
- David Jamieson Bolder & Yuliya Romanyuk, 2008. "Combining Canadian Interest-Rate Forecasts," Working Papers 08-34, Bank of Canada.
- Chen Zhuo & Yang Yuhong, 2007. "Time Series Models for Forecasting: Testing or Combining?," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 11(1), pages 56-90, March.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Wiley-Blackwell Digital Licensing) or (Christopher F. Baum).
If references are entirely missing, you can add them using this form.