SAS/STAT Software

BCHOICE Procedure

The BCHOICE procedure fits Bayesian discrete choice models by using MCMC methods. The procedure's capabilities include the following:

  • fits the following types of models:
    • multinomial logit
    • multinomial probit
    • nested logit
    • multinomial logit with random effects
    • multinomial probit with random effects
  • samples directly from the full conditional distribution when possible
  • supports the following sampling algorithms:
    • Metropolis-Hastings approach of Gamerman
    • random walk Metropolis
    • latent variables via the data augmentation method
  • provides a variety of Markov chain convergence diagnostics
  • works with the postprocessing autocall macros that are designed for Bayesian posterior samples
  • supports a CLASS statement for specifying classification variables
  • supports a RESTRICT statement, enabling you to specify boundary requirements and order constraints on fixed effects for logit models
  • multithreaded
  • creates an output data set that contains the posterior samples of all parameters
  • creates an output data set that contains random samples from the posterior predictive distribution of the choice probabilities
  • creates an output data set that corresponds to any output table
  • supports BY group processing
  • automatically produces graphs by using ODS Graphics

For further details see the BCHOICE Procedure