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Is there an Optimal Forecast Combination? A Stochastic Dominance Approach to Forecast Combination Puzzle

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Author Info

  • Mehmet Pinar

    ()
    (University of Guelph; Fondazione Eni Enrico Mattei)

  • Thanasis Stengos

    ()
    (University of Guelph)

  • M. Ege Yazgan

    ()
    (Istanbul Bilgi University)

Abstract

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.

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Paper provided by The Rimini Centre for Economic Analysis in its series Working Paper Series with number 17_12.

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Date of creation: Jun 2012
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Handle: RePEc:rim:rimwps:17_12

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Keywords: Nonparametric Stochastic Dominance; Mixed Integer Programming; Forecast combinations; Forecast combination puzzle;

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  1. Nikolas Topaloglou & Olivier Scaillet & University of Geneva, 2006. "Testing foe Stochastic Dominance Efficiency," Computing in Economics and Finance 2006 74, Society for Computational Economics.
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