- مبلغ: ۸۶,۰۰۰ تومان
- مبلغ: ۹۱,۰۰۰ تومان
Numerous forecast combination techniques have been proposed. However, these do not systematically outperform a simple average (SA) of forecasts in empirical studies. Although it is known that this is due to instability of learned weights, managers still have little guidance on how to solve this “forecast combination puzzle”, i.e., which combination method to choose in specific settings. We introduce a model determining the yet unknown asymptotic out-of-sample error variance of the two basic combination techniques: SA, where no weightings are learned, and so-called optimal weights that minimize the in-sample error variance. Using the model, we derive multi-criteria boundaries (considering training sample size and changes of the parameters which are estimated for optimal weights) to decide when to choose SA. We present an empirical evaluation which illustrates how the decision rules can be applied in practice. We find that using the decision rules is superior to all other considered combination strategies.
6. Conclusion and implications
The “forecast combination puzzle” refers to the recurring empirical finding that more sophisticated weight learning models typically do not outperform a simple average (SA) in forecast combination. It is known that estimates of the error variances of individual forecasts and their covariances, the parameters used for weighting the forecasts, are often too unstable because of small training samples or changes in the underlying time series and the corresponding error characteristics. However, models quantifying the error variance with a particular forecast combination method do not exist and managers still have little guidance on which forecast combination method to choose in a specific situation.