Is there an optimal forecast combination? A stochastic dominance approach applied to the forecast combination puzzle
The forecast combination puzzle refers to the finding that a simple average forecast combination outperforms more sophisticated weighting schemes and/or the best individual model. The paper derives optimal (worst) forecast combinations based on stochastic dominance (SD) analysis with differential forecast weights. For the optimal (worst) forecast combination, this index will minimize (maximize) forecasts errors by combining time-series model based forecasts at a given probability level. By weighting each forecast differently, we find the optimal (worst) forecast combination that does not rely on arbitrary weights. Using two exchange rate series on weekly data for the Japanese Yen/U.S. Dollar and U.S. Dollar/Great Britain Pound for the period from 1975 to 2010 we find that the simple average forecast combination is neither the worst nor the best forecast combination something that provides partial support for the forecast combination puzzle. In that context, the random walk model is the model that consistently contributes with considerably more than an equal weight to the worst forecast combination for all variables being forecasted and for all forecast horizons, whereas a flexible Neural Network autoregressive model and a self-exciting threshold autoregressive model always enter the best forecast combination with much greater than equal weights.
|Date of creation:||2012|
|Date of revision:|
|Contact details of provider:|| Postal: |
Phone: (519) 824-4120 ext. 53898
Fax: (519) 763-8497
Web page: https://www.uoguelph.ca/economics/
More information through EDIRC
References listed on IDEAS
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.:
- Mark W. Watson & James H. Stock, 2004. "Combination forecasts of output growth in a seven-country data set," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 23(6), pages 405-430.
- Anderson, Gordon, 1996. "Nonparametric Tests of Stochastic Dominance in Income Distributions," Econometrica, Econometric Society, vol. 64(5), pages 1183-93, September.
- Huiyu Huang & Tae-Hwy Lee, 2010.
"To Combine Forecasts or to Combine Information?,"
Taylor & Francis Journals, vol. 29(5-6), pages 534-570.
- Olivier Scaillet & Nikolas Topaloglou, 2005.
"Testing for Stochastic Dominance Efficiency,"
FAME Research Paper Series
rp154, International Center for Financial Asset Management and Engineering.
- Clemen, Robert T., 1989. "Combining forecasts: A review and annotated bibliography," International Journal of Forecasting, Elsevier, vol. 5(4), pages 559-583.
- Oliver Linton & Esfandiar Maasoumi & Yoon-Jae Whang, 2003.
"Consistent testing for stochastic dominance under general sampling schemes,"
LSE Research Online Documents on Economics
2208, London School of Economics and Political Science, LSE Library.
- Oliver Linton & Esfandiar Maasoumi & Yoon-Jae Whang, 2005. "Consistent Testing for Stochastic Dominance under General Sampling Schemes," Review of Economic Studies, Oxford University Press, vol. 72(3), pages 735-765.
- Linton, Oliver & Maasoumi, Esfandiar & Whang, Yoon-Jae, 2003. "Consistent Testing for Stochastic Dominance under General Sampling Schemes," SFB 373 Discussion Papers 2003,31, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
- repec:cup:cbooks:9780521634809 is not listed on IDEAS
- Marco Aiolfi & Carlos Capistrán & Allan Timmermann, 2010.
CREATES Research Papers
2010-21, School of Economics and Management, University of Aarhus.
- Jeremy Smith & Kenneth F. Wallis, 2009. "A Simple Explanation of the Forecast Combination Puzzle," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 71(3), pages 331-355, 06.
- Beach, Charles M & Davidson, Russell, 1983. "Distribution-Free Statistical Inference with Lorenz Curves and Income Shares," Review of Economic Studies, Wiley Blackwell, vol. 50(4), pages 723-35, October.
- De Gooijer, Jan G. & De Bruin, Paul T., 1998. "On forecasting SETAR processes," Statistics & Probability Letters, Elsevier, vol. 37(1), pages 7-14, January.
- Elliott, Graham & Timmermann, Allan G, 2007.
CEPR Discussion Papers
6158, C.E.P.R. Discussion Papers.
- Marcellino, Massimiliano & Stock, James H & Watson, Mark W, 2005.
"A Comparison of Direct and Iterated Multistep AR Methods for Forecasting Macroeconomic Time Series,"
CEPR Discussion Papers
4976, C.E.P.R. Discussion Papers.
- Marcellino, Massimiliano & Stock, James H. & Watson, Mark W., 2006. "A comparison of direct and iterated multistep AR methods for forecasting macroeconomic time series," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 499-526.
- Massimiliano Marcellino & James Stock & Mark Watson, 2005. "A Comparison of Direct and Iterated Multistep AR Methods for Forecasting Macroeconomic Time Series," Working Papers 285, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
- Graham Elliott & Allan Timmermann, 2005.
"Optimal Forecast Combination Under Regime Switching ,"
International Economic Review,
Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 46(4), pages 1081-1102, November.
- Elliott, Graham & Timmermann, Allan G, 2004. "Optimal Forecast Combination Under Regime Switching," CEPR Discussion Papers 4649, C.E.P.R. Discussion Papers.
- Anders Bredahl Kock & Timo Teräsvirta, 2010. "Forecasting with nonlinear time series models," CREATES Research Papers 2010-01, School of Economics and Management, University of Aarhus.
- Garry F. Barrett & Stephen G. Donald, 2003. "Consistent Tests for Stochastic Dominance," Econometrica, Econometric Society, vol. 71(1), pages 71-104, January.
- repec:cup:cbooks:9780521632423 is not listed on IDEAS
- Dick van Dijk & Philip Hans Franses & Michael P. Clements & Jeremy Smith, 2003.
"On SETAR non-linearity and forecasting,"
Journal of Forecasting,
John Wiley & Sons, Ltd., vol. 22(5), pages 359-375.
- Davidson, R. & Duclos, J.-Y., 1998.
"Statistical Inference for Stochastic Dominance and for the Measurement of Poverty and Inequality,"
98a14, Universite Aix-Marseille III.
- Russell Davidson & Jean-Yves Duclos, 2000. "Statistical Inference for Stochastic Dominance and for the Measurement of Poverty and Inequality," Econometrica, Econometric Society, vol. 68(6), pages 1435-1464, November.
- Davidson, Russell & Duclos, Jean-Yves, 1998. "Statistical Inference for Stochastic Dominance and for the Measurement of Poverty and Inequality," Cahiers de recherche 9805, Université Laval - Département d'économique.
When requesting a correction, please mention this item's handle: RePEc:gue:guelph:2012-06.. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Stephen Kosempel)
If references are entirely missing, you can add them using this form.