Semiparametric estimation of the random utility model with rank-ordered choice data
使用排序选择数据半参数估计随机效用模型
We propose semiparametric methods for estimating random utility models using rank-ordered choice data. Our primary method is the generalized maximum score (GMS) estimator. With partially rank-ordered data, the GMS estimator allows for arbitrary forms of interpersonal heteroskedasticity. With fully rank-ordered data, the GMS estimator becomes considerably more flexible, allowing for random coefficients and alternative-specific heteroskedasticity and correlations. The GMS…