An instrumental variable model of multiple discrete choice: IV model of multiple discrete choice
An instrumental variable model of multiple discrete choice: IV model of multiple discrete choice
复制标题
多重离散选择的工具变量模型：多重离散选择的IV模型
DOI：
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发表时间：
2013
期刊：
影响因子：
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通讯作者：
Konrad Smolinski
中科院分区：
文献类型：
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作者：
A. Chesher;A. Rosen;Konrad Smolinski
本文研究了多个离散选择模型中的识别，其中可能存在内生解释变量，即解释变量不限于独立于潜在效用的未观察到的决定因素而分布。该模型不采用大支持、特殊回归器或控制函数限制；事实上，它没有提及提供内生解释变量值的过程，在这方面它是不完整的。相反，该模型采用工具变量限制，要求存在工具变量，这些变量被排除在潜在效用之外，并且独立于未观察到的效用组成部分进行分布。我们证明该模型提供了潜在效用函数的集合识别和未观察到的异质性的分布，并且我们描述了这些对象的尖锐界限。我们开发了易于计算的外部区域，在参数模型中，与传统的最大似然分析中涉及的计算相比，这些区域几乎不需要更多的计算。使用本质上是 McFadden (1974) 的条件 Logit 模型的模型来说明结果，但具有潜在的内生解释变量和工具变量限制。
This paper studies identification in multiple discrete choice models in which there may be endogenous explanatory variables, that is, explanatory variables that are not restricted to be distributed independently of the unobserved determinants of latent utilities. The model does not employ large support, special regressor, or control function restrictions; indeed, it is silent about the process that delivers values of endogenous explanatory variables, and in this respect it is incomplete. Instead, the model employs instrumental variable restrictions that require the existence of instrumental variables that are excluded from latent utilities and distributed independently of the unobserved components of utilities. We show that the model delivers set identification of latent utility functions and the distribution of unobserved heterogeneity, and we characterize sharp bounds on these objects. We develop easy-to-compute outer regions that, in parametric models, require little more calculation than what is involved in a conventional maximum likelihood analysis. The results are illustrated using a model that is essentially the conditional logit model of McFadden (1974), but with potentially endogenous explanatory variables and instrumental variable restrictions.