具有序统计边际效用的多重离散选择

S. Webster
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引用次数: 0

摘要

本文提出了一个随机效用最大化模型,用于个体从n个备选项中选择离散量。可以选择具有正数的多个备选方案。通过独立甘贝尔随机变量的序统计量来模拟每种选择的边际效用对数量的递减。该模型简洁——需要n + 1个参数——易于处理——允许选择概率的封闭形式表达式。因此,该模型适用于根据观察到的选择对结构参数进行最大似然估计。概率函数在二元选择和最大数量为一个单位的情况下恢复二元logit概率,并且概率在每种选择的数量上是单调的。单调性很可能限制了模型的应用范围。该性质是冈贝尔序统计概率之间的简单递归关系的一种表现。据我所知,这种关系以前还没有在文献中被确定,并且可能导致以可处理的方式捕获重要复杂性的新模型。
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Multiple Discrete Choice with Order Statistic Marginal Utilities
The paper presents a random utility maximization model for individuals selecting discrete quantities from a set of n alternatives. Multiple alternatives with positive quantities may be selected. Diminishing marginal utility to quantity of each alternative is modeled via order statistics of independent Gumbel random variables. The model is parsimonious — requiring n + 1 parameters — and tractable — admitting closed-form expressions for choice probabilities. As such, the model is amenable to maximum likelihood estimation of structural parameters from observed choices.

Probability functions recover binary logit probabilities under binary choice and a maximum quantity of one unit, and probability is monotonic in the quantity of each alternative. The monotonic property likely restricts the application of the model to a narrow range of settings. The property is a manifestation of a simple recursive relationship among Gumbel order statistic probabilities. This relationship, to my knowledge, has not previously been identified in the literature and may lead to new models for capturing important complexities in a tractable manner.
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