Interpretation of point forecasts with unknown directive
扩展了点预测作为预测分布函数（如分位数和期望分位数）的识别方法，提出广义矩估计量并检验最优性，在模拟中比现有方法更灵活、校准更好且功效更高，实证表明经济增长和降水预测存在对极端事件的过度预期。
Abstract Point forecasts can be interpreted as functionals (i.e., point summaries) of predictive distributions. We extend methodology for the identification of the functional based on time series of point forecasts and associated realizations. Focusing on state‐dependent quantiles and expectiles, we provide a generalized method of moments estimator for the functional, along with tests of optimality under general joint hypotheses of functional relationships and information bases. Our tests are more flexible, and in simulations better calibrated and more powerful than existing solutions. In empirical examples, economic growth forecasts and model output for precipitation are indicative of overstatement in anticipation of extreme events.