ON THE RECOVERABILITY OF FORECASTERS’ PREFERENCES
研究了如何从预测值、实际值和预测者的信息集中识别其损失函数，发现非参数偏好恢复在理论上可行，但依赖于条件分布的变化程度，并给出了二元变量预测中损失函数的集合识别结果。
We study the problem of identifying a forecaster’s loss function from observations on forecasts, realizations, and the forecaster’s information set. Essentially different loss functions can lead to the same forecasts in all situations, though within the class of all continuous loss functions, this is strongly nongeneric. With the small set of exceptional cases ruled out, generic nonparametric preference recovery is theoretically possible, but identification depends critically on the amount of variation in the conditional distributions of the process being forecast. There exist processes with sufficient variability to guarantee identification, and much of this variation is also necessary for a process to have universal identifying power. We also briefly address the case in which the econometrician does not fully observe the conditional distributions used by the forecaster, and in this context we provide a practically useful set identification result for loss functions used in forecasting binary variables.