标题： 可解不确定性分布是类型I极值吗？ 使用选择概率的随机系数模型检验 显示英文标题

标题： Is the distribution of resolvable uncertainty Type I extreme value? A Test for Random Coefficient Models using Choice Probabilities

摘要： 陈述选择概率越来越多地与随机系数模型（RCM）结合使用，以描述个体偏好。 它们允许调查受访者表达对未来或假设情景不完整性的不确定性：可解决的不确定性。 通常会对这种不确定性施加参数假设，例如类型I极值（EV1）分布，以识别和估计相关的RCM。 本文提出了第一个针对这些参数假设的检验，该检验基于可解决不确定性分位数间距总体分布的非参数识别结果。 在所考虑的四个实证应用中，检验结果均强烈反对EV1假设。 显示英文摘要

摘要： Stated choice probabilities are increasingly used in conjunction with the random-coefficient model (RCM) to describe individual preferences. They allow survey respondents to express uncertainty about the future or the incompleteness of a hypothetical scenario: the resolvable uncertainty. Parametric assumptions such as a Type I extreme value (EV1) distribution are almost always imposed on this uncertainty to identify and estimate the associated RCM. This paper proposes the first test for these parametric assumptions, based on a nonparametric identification result for the population distribution of the interquantile range of the resolvable uncertainty. In all four empirical applications considered, the test finds strong evidence against the EV1 assumption.