Identification of the Distribution of Random Coefficients in Static and Dynamic Discrete Choice Models
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We show that the distributions of random coefficients in various discrete choice modelsare nonparametrically identified. Our identification results apply to static discrete choicemodels including binary logit, multinomial logit, nested logit, and probit models as well asto dynamic programming discrete choice models. In these models the only key condition weneed to verify for identification is that the type specific model choice probability belongs to aclass of functions that include analytic functions. Therefore our identification results aregeneral enough to include most of commonly used discrete choice models in the literature. Our identification argument builds on insights from nonparametric specification testing. Wefind that the role of analytic function in our identification results is to effectively remove thefull support requirement often exploited in other identification approaches.
Abstract
We show that the distributions of random coefficients in various discrete choice modelsare nonparametrically identified. Our identification results apply to static discrete choicemodels including binary logit, multinomial logit, nested logit, and probit models as well asto dynamic programming discrete choice models. In these models the only key condition weneed to verify for identification is that the type specific model choice probability belongs to aclass of functions that include analytic functions. Therefore our identification results aregeneral enough to include most of commonly used discrete choice models in the literature. Our identification argument builds on insights from nonparametric specification testing. Wefind that the role of analytic function in our identification results is to effectively remove thefull support requirement often exploited in other identification approaches.
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