Nonparametric maximum likelihood methods for binary response models with random coefficients
Jiaying Gu and
Papers from arXiv.org
Single index linear models for binary response with random coefficients have been extensively employed in many econometric settings under various parametric specifications of the distribution of the random coefficients. Nonparametric maximum likelihood estimation (NPMLE) as proposed by Cosslett (1983) and Ichimura and Thompson (1998), in contrast, has received less attention in applied work due primarily to computational difficulties. We propose a new approach to computation of NPMLEs for binary response models that significantly increase their computational tractability thereby facilitating greater flexibility in applications. Our approach, which relies on recent developments involving the geometry of hyperplane arrangements, is contrasted with the recently proposed deconvolution method of Gautier and Kitamura (2013). An application to modal choice for the journey to work in the Washington DC area illustrates the methods.
New Economics Papers: this item is included in nep-dcm and nep-ecm
Date: 2018-11, Revised 2019-06
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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:1811.03329
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