Even though different optimal forecast combination weights are offered for static, dynamic, or time-varying situations, empirical findings support the simple average forecast combination outperforms more sophisticated weighting schemes and/or the best individual model. Using an approach that relies on consistent tests for stochastic dominance efficiency, an alternative optimal weighting scheme is proposed. These tests are considered for a given forecast combination (i.e. equal weighted average of forecasts) with respect to all possible forecast combinations constructed from a set of individual forecasts to obtain optimal or worst forecast combinations. In our empirical applications we find that equally weighted forecast combinations are neither optimal nor the worst forecast combination. For the optimal forecast combination, the best forecasting model, i.e. the model which assumes relatively more weight than other forecast models, differs with the variable being forecasted and for different forecast horizons. On the other hand, random walk is the model that consistently contributes with more than arbitrarily assigned equal weights for the worst forecast combination for all variables being forecasted and for all forecast horizons.
|Publication status||Published - 2012|
|Event||Society for Nonlinear Dynamics and Econometrics 20th Annual Symposium - Istanbul Bilgi University, Istanbul, Turkey|
Duration: 5 Apr 2012 → 6 Apr 2012
|Conference||Society for Nonlinear Dynamics and Econometrics 20th Annual Symposium|
|Period||5/04/12 → 6/04/12|