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分类依据与纠错

AI 根据所提供的论文材料归类:摘要明确研究品牌与质量两个维度上的离散选择、条件选择概率和参数识别,直接对应离散选择方向。

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代码、数据与补充材料

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关注这篇论文的作者
Eleni Aristodemou
Adam M. Rosen

部分有序备选项的离散选择模型

研究问题
当消费者在横向无序的品牌之间、以及各品牌内部纵向有序的质量等级之间选择时,如何刻画参数识别集合并进行估计与推断?
方法与思路
作者先利用有序维度的形状限制开展非参数分析,再研究推广常见有序选择模型的参数设定,并给出似然估计和检验所需公式。
材料陈述的贡献
摘要称模型刻画了条件选择概率与参数识别集合,讨论可应对非点识别的准似然比推断,并以品牌内质量分层的刀片购买数据示例。
阅读关注点
应核对纵向形状限制、跨品牌质量偏好的分布设定、点识别与集合识别的边界,以及 Wald 和准似然比推断各自的条件。
阅读边界
仅依据所给 Crossref 摘要;具体识别限制和检验分布未说明,多元正态仅是重点讨论的设定,不能据此推及一般情形或版本变化。

原文摘要

In this paper, we analyze a discrete choice model for partially ordered alternatives. The alternatives are differentiated along two dimensions: the first an unordered “horizontal” dimension, and the second an ordered “vertical” dimension. The model can be used in circumstances in which individuals choose among products of different brands, wherein each brand offers an ordered choice menu, for example, by offering products of varying quality. The unordered–ordered nature of the discrete choice problem is used to characterize the identified set of model parameters. Following an initial nonparametric analysis that relies on shape restrictions inherent in the ordered dimension of the problem, we then provide a specialized analysis for parametric specifications that generalize common ordered choice models. We characterize conditional choice probabilities as a function of model primitives with particular analysis focusing on cases in which unobservable taste for quality of each brand offering is multivariate normally distributed. We provide explicit formulae used for estimation and inference via maximum likelihood, and we consider inference based on Wald and quasi‐likelihood ratio statistics, the latter of which can be robust to a possible lack of point identification. An empirical illustration is conducted using data on razor blade purchases in which each brand has product offerings vertically differentiated by quality.

笔记与分组
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