The mixed logit model is widely used in applied econometrics. Researchers typically rely on
the free choice between the classical and Bayesian estimation approach. However …

The nested logit model has become an important tool for the empirical analysis of discrete
outcomes. There is some confusion about its specification of the outcome probabilities. Two …

This paper describes two Stata programs, mclgen and mclest, for imposing special
restrictions on multinomial logistic models. MCL stands for Multinomial Conditional Logit, a …

This paper proposes the use of a quasi-random sequence for the estimation of the mixed
multinomial logit model. The mixed multinomial structure is a flexible discrete choice …

In the mixed logit model, random coefficients are estimated with the use of simulation
methods to approximate the integral of the likelihood over the density of the coefficients …

Class imbalance, where there are great differences between the number of observations
associated with particular discrete outcomes, is common within transportation and other …

We examine the finite sample properties of the maximum likelihood estimator for the binary
logit model with random covariates. Previous studies have either relied on large-sample …

A semi-nonparametric generalized multinomial logit model, formulated using orthonormal
Legendre polynomials to extend the standard Gumbel distribution, is presented in this …

Multinomial logit models, especially those calibrated on scanner data, often use explanatory
variables that are nonlinear functions of the parameters to be estimated. A common example …