Behavioral Foundations of Nested Stochastic Choice and Nested Logit
首次为嵌套Logit模型提供了行为特征刻画，提出了非参数版本“嵌套随机选择”，通过一个公理弱化无关选项独立性假设，并给出从选择数据中识别真实嵌套结构的实用算法。
We provide the first behavioral characterization of nested logit, a foundational and widely applied discrete-choice model, through the introduction of a nonparametric version of nested logit that we call “nested stochastic choice” (NSC). NSC is characterized by a single axiom that weakens independence of irrelevant alternatives on the basis of revealed similarity to allow for the similarity effect. Nested logit is characterized by an additional menu-independence axiom. Our axiomatic characterization leads to a practical, data-driven algorithm that identifies the true nest structure from choice data. We also discuss limitations of generalizing nested logit by studying the testable implications of cross-nested logit.