论文标题
边际随机选择
Marginal stochastic choice
论文作者
论文摘要
随机选择的模型通常使用条件选择概率将菜单作为分析的原始性，但是在现场通常很难观察到这些菜单。此外，使用此数据不可能研究对菜单的偏好。我们假设分析师可以观察到选择和可用性的边际频率，但不是条件选择频率，并研究了该数据集的某些随机选择的一些突出模型的可测试含义。我们还分析了这些模型的参数是否可以识别。最后，我们表征了Gul和Pesendorfer [2001]和Kreps [1979]的两个阶段模型下可能出现的边际分布，在选择替代方案之前，代理商选择菜单。
Models of stochastic choice typically use conditional choice probabilities given menus as the primitive for analysis, but in the field these are often hard to observe. Moreover, studying preferences over menus is not possible with this data. We assume that an analyst can observe marginal frequencies of choice and availability, but not conditional choice frequencies, and study the testable implications of some prominent models of stochastic choice for this dataset. We also analyze whether parameters of these models can be identified. Finally, we characterize the marginal distributions that can arise under two-stage models in the spirit of Gul and Pesendorfer [2001] and of kreps [1979] where agents select the menu before choosing an alternative.
