 Identification of the Distribution of Random Coefficients in Static and Dynamic Discrete Choice ModelsKyoo il Kim () Korean Economic Review, 2014, vol. 30, 191-216 Abstract: We show that the distributions of random coefficients in various discrete choice models are nonparametrically identified. Our identification results apply to static discrete choice models including binary logit, multinomial logit, nested logit, and probit models as well as to dynamic programming discrete choice models. In these models the only key condition we need to verify for identification is that the type specific model choice probability belongs to a class of functions that include analytic functions. Therefore our identification results are general enough to include most of commonly used discrete choice models in the literature. Our identification argument builds on insights from nonparametric specification testing. We find that the role of analytic function in our identification results is to effectively remove the full support requirement often exploited in other identification approaches. Keywords: Random Coefficients; Nonparametric Identification; Logit and Probit; Discrete Choice; Dynamic Discrete Choice (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:kea:keappr:ker-20141231-30-2-01 Access Statistics for this article Korean Economic Review is currently edited by Kyung Hwan Baik More articles in Korean Economic Review from Korean Economic Association Contact information at EDIRC. |
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