No abstract

This paper considers mixed, or random coecients, multinomial logit (MMNL) models for discrete response, and establishes the following results. Under mild regularity conditions, any discrete choice model derived from random utility maximization has choice probabilities that can be approximated as closely as one pleases by a MMNL model. Practical estimation of a parametric mixing family can be carried out by Maximum Simulated Likelihood Estimation or Method of Simulated Moments, and easily computed instruments are provided that make the latter procedure fairly ecient. The adequacy of a mixing speci®cation can be tested simply as an omitted variable test with appropriately de®ned arti®cial variables. An application to a problem of demand for alternative vehicles shows that MMNL provides a¯exible and computationally practical approach to discrete response analysis.

No abstract

This paper considers mixed, or random coecients, multinomial logit (MMNL) models for discrete response, and establishes the following results. Under mild regularity conditions, any discrete choice model derived from random utility maximization has choice probabilities that can be approximated as closely as one pleases by a MMNL model. Practical estimation of a parametric mixing family can be carried out by Maximum Simulated Likelihood Estimation or Method of Simulated Moments, and easily computed instruments are provided that make the latter procedure fairly ecient. The adequacy of a mixing speci®cation can be tested simply as an omitted variable test with appropriately de®ned arti®cial variables. An application to a problem of demand for alternative vehicles shows that MMNL provides a¯exible and computationally practical approach to discrete response analysis.

No abstract