I provide general frequentist framework to elicit the forecaster’s expected utility based on a Lagrange Multiplier-type test for the null of locality of the scoring rules associated to the probabilistic forecast. These are assumed to be observed transition variables in a nonlinear autoregressive model to ease the statistical inference. A simulation study reveals that the test behaves consistently with the requirements of the theoretical literature. The locality of the scoring rule is fundamental to set dating algorithms to measure and forecast probability of recession in US business cycle. An investigation of Bank of Norway’s forecasts on output growth leads us to conclude that forecasts are often suboptimal with respect to some simplistic benchmark if forecaster’s reward is not properly evaluated.

我提供了一般的频率学框架来引出预报员的预期效用，它基于拉格朗日乘数类型的测试，用于检验与概率预测相关的评分规则的局部性的空值。这些假设是观察过渡变量在一个非线性自回归模型，以减轻统计推断。仿真研究表明，试验结果符合理论文献的要求。评分规则的局部性对于设定日期算法来衡量和预测美国商业周期中衰退的可能性是至关重要的。对挪威央行(Bank of Norway)对产出增长预测的调查，让我们得出这样的结论:如果预测者的回报没有得到恰当评估，相对于某些过于简单的基准，预测往往不够理想。