Simple averages often, but not always, outperform more sophisticated “optimal” forecast composites. We used Bayesian shrinkage techniques to allow the incorporation of prior information into the estimation of combining weights; the estimated combining weights were coaxed or “shrunken” toward equality but were not forced to be exactly equal. The least-squares and prior (i.e., arithmetic average)...

□ This paper focuses on the newly proposed on-line forecast combination algorithms in Sancetta (2010), Yang (2004), and Wei and Yang (2012). We first establish the asymptotic relationship between these new algorithms and the Bates and Granger (1969) method. Then, we show that when implemented on unbalanced panels, different combination algorithms implicitly impute missing data differently, maki...

We develop a non-linear forecast combination rule based on copulas that incorporate the dynamic interaction between individual predictors. This approach is optimal in the sense that the resulting combined forecast produces the highest discriminatory power as measured by the receiver operating characteristic (ROC) curve. Under additional assumptions, this rule is shown to be equivalent to the qu...

This paper offers a theoretical explanation why forecast combination with the estimated optimal weights has often poor performance in applications. The explanation is simple. The properties of the combination are often derived under the assumption that the weights are fixed while in practice they have to be estimated. If the weight estimation is taken into account during the optimality derivati...

This paper examines the role of managerial judgment in forming a final forecast, or judging the achievability of a critical level of sales, when multiple forecasts or opinions are available to the decision maker. Several factors that can help improve the quality of human intervention are identified and incorporated in a decision ard. Experimental results show that aided combination can help the...