Discrete Choice

Discrete Choice

Discrete Choice is a package for the fitting of a variety of models with categorical dependent variables. These models are particularly useful for researchers in the social, behavioral, and biomedical sciences, as well as economics, public choice, education, and marketing.

Output for these models includes full information maximum likelihood estimates with either standard and quasi-maximum likelihood inference. In addition, estimates of marginal effects are computed either as partials of the probabilities with respect to the means of the exogenous variables or optionally as the average partials of the probabilities with respect to the exogenous variables.

Models

Nested logit model

  • Is derived from the assumption that residuals have a generalized extreme value distribution and allows for a general pattern of dependence among the responses thus avoiding the IIA problem, i.e., the “independence of irrelevant alternatives.”

Conditional logit model

  • Includes both variables that are attributes of the responses as well as, optionally, exogenous variables that are properties of cases.

Multinomial logit model

  • Qualitative responses are each modeled with a separate set of regression coefficients

Adjacent category multinomial logit model

  • The log-odds of one category versus the next higher category is linear in the cutpoints and explanatory variables

Stereotype multinomial logit model

  • The coefficients of the regression in each category are linear functions of a reference regression

Poisson regression, left or right truncated, left or right censored, or zero-inflated models

  • Estimates model with Poisson distributed dependent variable. This includes censored models – the dependent variable is not observed but independent variables are available – and truncated models where not even the independent variables are observed. Also, a zero-inflated Poisson model can be estimated where the probability of the zero category is a mixture of a Poisson consistent probability and an excess probability. The mixture coefficient can be a function of independent variables.

Negative binomial regression, left or right truncated, left or right censored, or zero-inflated models

  • Estimates model with negative binomial distributed dependent variable. This includes censored models – the dependent variable is not observed but independent variables are available – and truncated models where not even the independent variables are observed. Also, a zero-inflated negative binomial model can be estimated where the probability of the zero category is a mixture of a negative binomial consistent probability and an excess probability. The mixture coefficient can be a function of independent variables.

Logit, probit models

  • Estimates dichotomous dependent variable with either Normal or extreme value distributions

Ordered logit, probit models

  • Estimates model with an ordered qualitative dependent variable with Normal or extreme value distributions

Platform: Windows, Mac, and Linux.

Requirements: GAUSS/GAUSS Light version 8.0 or higher.