Assessing Uncertainty from Point Forecasts
提出一个模型，将多个点预测组合成预测分布，考虑预测间的相关性和参数不确定性，并计算扩增因子。在报童模型中，该方法能提高期望利润。
The paper develops a model for combining point forecasts into a predictive distribution for a variable of interest. Our approach allows for point forecasts to be correlated and admits uncertainty on the distribution parameters given the forecasts. Further, it provides an easy way to compute an augmentation factor needed to equate the dispersion of the point forecasts to that of the predictive distribution, which depends on the correlation between the point forecasts and on the number of forecasts. We show that ignoring dependence or parameter uncertainty can lead to assuming an unrealistically narrow predictive distribution. We further illustrate the implications in a newsvendor context, where our model leads to an order quantity that has higher variance but is biased in the less costly direction, and generates an increase in expected profit relative to other methods. The e-companion is available at https://doi.org/10.1287/mnsc.2017.2936 . This paper was accepted by Vishal Gaur, operations management.