Share-Ratio Estimation of the Nested Multinomial Logit Model
针对经典多项Logit模型存在无关选项独立性缺陷的问题，本文提出将嵌套多项Logit模型纳入广义最小二乘框架，利用份额比方法从仅含市场份额的大数据集进行估计，并生成对IIA假设的卡方检验。
Recently the nested multinomial logit (NMNL) model has been proposed to remedy the “independence of irrelevant alternatives” (HA) property of the classical multinomial logit (MNL) model. The author brings the NMNL model under generalized least squares theory, thereby enhancing its estimation with large datasets containing only market shares. In this procedure one uses a pairwise approach by constructing share ratios among choice alternatives and subsets of alternatives in a partitioned choice set. An additional advantage of this method is that it generates a chi square test for IIA in the MNL submodel, which is a special case of the NMNL model.