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多元有序选择中依赖关系的结构AI 解读

When individuals make simultaneous decisions across multiple ordered dimensions, standard multivariate ordered choice models impose narrow bracketing: each dimension is decided as if the others did not exist. We develop a general rectangular structure model capturing broad bracketing while nesting the standard model. The framework introduces two layers of dependence, one through the decision-threshold structure and the other through the correlation of latent utilities. We…

分位数限制、揭示排序与多项选择的边界AI 解读

This paper analyzes when choice probabilities reveal rankings of deterministic utility indices in semiparametric discrete choice models. It begins with binary choice, where quantile thresholds guarantee ranking recovery, and shows that such thresholds can arise either from behavioral departures from utility maximization (e.g., limited attention) under exchangeable unobservables, or from non-exchangeable unobservables under standard utility maximization. These behavioral and…

不完整数据下的不平等界限AI 解读

We study inequality measures when outcomes are observed only in intervals, as in historical tabulations, privacy-protected grouped data, and modern surveys. We develop a nonparametric framework for sharp identification and inference with grouped and interval-valued data, covering brackets and overlapping intervals. For a class of inequality indices, sharp bounds are attained by discrete distributions with finite support, reducing the problem to optimization;…

部分识别离散响应模型的随机集分位数估计AI 解读

Semiparametric discrete choice models are widely applied in economics, yet a fundamental tension arises when covariates are discrete as regression coefficients that are point identified under continuous regressors may become only partially identified. We show that this is not merely an identification problem but creates serious estimation pathologies. Classical estimators, including the maximum score estimator of Manski (1975), not only have population maximizers that are…

具有格结构的多元有序离散响应模型AI 解读

We analyze multivariate ordered discrete response models with a lattice structure, modeling decision makers who narrowly bracket choices across multiple dimensions. These models map latent continuous processes into discrete responses using functionally independent decision thresholds. In a semiparametric framework, we model latent processes as sums of covariate indices and unobserved errors, deriving conditions for identifying parameters, thresholds, and the joint cumulative…

部分识别离散响应模型的锐性与稳健估计AI 解读

Semiparametric discrete choice models are widely used in a variety of practical applications. While these models are point identified in the presence of continuous covariates, they can become partially identified when covariates are discrete. In this paper we find that classical estimators, including the maximum score estimator, (Manski (1975)), loose their attractive statistical properties without point identification. First of all, they are not sharp with the estimator…

在项目评估与处理选择中纳入社会福利AI 解读

The econometric literature on treatment-effects typically takes functionals of outcome-distributions as `social welfare' and ignores program-impacts on unobserved utilities. We show how to incorporate aggregate utility within econometric program-evaluation and optimal treatment-targeting for a heterogenous population. In the practically important setting of discrete-choice, under unrestricted preference-heterogeneity and income-effects, the indirect-utility distribution…