To Combine Forecasts or to Combine Information?
AbstractWhen the objective is to forecast a variable of interest but with many explanatory variables available, one could possibly improve the forecast by carefully integrating them. There are generally two directions one could proceed: combination of forecasts (CF) or combination of information (CI). CF combines forecasts generated from simple models each incorporating a part of the whole information set, while CI brings the entire information set into one super model to generate an ultimate forecast. Through linear regression analysis and simulation, we show the relative merits of each, particularly the circumstances where forecast by CF can be superior to forecast by CI, when CI model is correctly specified and when it is misspecified, and shed some light on the success of equally weighted CF. In our empirical application on prediction of monthly, quarterly, and annual equity premium, we compare the CF forecasts (with various weighting schemes) to CI forecasts (with principal component approach mitigating the problem of parameter proliferation). We find that CF with (close to) equal weights is generally the best and dominates all CI schemes, while also performing substantially better than the historical mean.
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Bibliographic InfoArticle provided by Taylor & Francis Journals in its journal Econometric Reviews.
Volume (Year): 29 (2010)
Issue (Month): 5-6 ()
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- Peter Exterkate & Patrick J.F. Groenen & Christiaan Heij & Dick van Dijk, 2011. "Nonlinear Forecasting with Many Predictors using Kernel Ridge Regression," Tinbergen Institute Discussion Papers 11-007/4, Tinbergen Institute.
- Abdymomunov, Azamat, 2013. "Predicting output using the entire yield curve," Journal of Macroeconomics, Elsevier, vol. 37(C), pages 333-344.
- WAN, Shui-Ki & WANG, Shin-Huei & WOO, Chi-Keung, 2012. "Total tourist arrival forecast: aggregation vs. disaggregation," CORE Discussion Papers 2012039, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
- Peter Exterkate & Patrick J.F. Groenen & Christiaan Heij & Dick van Dijk, 2011.
"Nonlinear Forecasting with Many Predictors using Kernel Ridge Regression,"
Tinbergen Institute Discussion Papers
11-007/4, Tinbergen Institute.
- Peter Exterkate & Patrick J.F. Groenen & Christiaan Heij & Dick van Dijk, 2013. "Nonlinear Forecasting With Many Predictors Using Kernel Ridge Regression," CREATES Research Papers 2013-16, School of Economics and Management, University of Aarhus.
- Cem Cakmakli & Dick van Dijk, 2010. "Getting the Most out of Macroeconomic Information for Predicting Stock Returns and Volatility," Tinbergen Institute Discussion Papers 10-115/4, Tinbergen Institute.
- Mehmet Pinar & Thanasis Stengos & M. Ege Yazgan, 2012.
"Is there an optimal forecast combination? A stochastic dominance approach applied to the forecast combination puzzle,"
1206, University of Guelph, Department of Economics and Finance.
- Mehmet Pinar & Thanasis Stengos & M. Ege Yazgan, 2012. "Is there an Optimal Forecast Combination? A Stochastic Dominance Approach to Forecast Combination Puzzle," Working Paper Series 17_12, The Rimini Centre for Economic Analysis.
- A. Girardi & R. Golinelli & C. Pappalardo, 2014. "The Role of Indicator Selection in Nowcasting Euro Area GDP in Pseudo Real Time," Working Papers wp919, Dipartimento Scienze Economiche, Universita' di Bologna.
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