Julien Monardo, Mogens Fosgerau, André de Palma · 2026 · arXiv (Cornell University)
DOI: 10.48550/arxiv.2610.10069 · cited by 0 works (OpenAlex) · open-access PDF
我们为市场级数据引入了一类需求模型。这些模型可以通过线性工具变量回归来估计,同时容纳比他们嵌入的logit和嵌入式logit模型更丰富的替代模式。它们是由封闭式反向市场份额函数构建的,通过一个与McFadden的通用极端价值生成函数类似的发电机,但在市场份额上发挥作用。 建设性结果允许任意嵌套结构,包括重叠巢和部分会员,产生通用极端价值模型的反向份额模型。
Machine translation (Meta M2M100); the English original follows.
We introduce a class of demand models for market-level data. The models can be estimated by linear instrumental variables regression while accommodating substitution patterns far richer than the logit and nested logit models they embed. They are built from closed-form inverse market share functions through a generator analogous to McFadden's generalized extreme value generating function, but acting on market shares. Constructive results allow arbitrary nesting structures, including overlapping nests and partial membership, yielding inverse-share analogs of generalized extreme value models. The class is consistent with utility maximization and strictly larger than the class of regular additive random utility models.
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Topics: Utility maximization, Class (philosophy), Mathematics, Mixed logit, Econometrics, Discrete choice
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