Is there an optimal forecast combination? A stochastic dominance approach applied to the forecast combination puzzle
AbstractThe 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.
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Bibliographic InfoPaper provided by University of Guelph, Department of Economics in its series Working Papers with number 1206.
Date of creation: 2012
Date of revision:
Nonparametric Stochastic Dominance; Mixed Integer Programming; Forecast combinations; Forecast combination;
Other versions of this item:
- 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.
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
- C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
- C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
This paper has been announced in the following NEP Reports:
- NEP-ALL-2012-04-23 (All new papers)
- NEP-CBA-2012-04-23 (Central Banking)
- NEP-ECM-2012-04-23 (Econometrics)
- NEP-ETS-2012-04-23 (Econometric Time Series)
- NEP-FOR-2012-04-23 (Forecasting)
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