Estimating Choice Models

As of this writing, at least five programs and four estimation techniques* exist for estimating discrete-choice models. Until now, no tutorials existed on how to use this combination of tools, so we have attempted to fill this gap.

Common Data Set

A variety of software and estimation techniques populate this space, but many require intensive study to use. The animations that follow focus just on discrete-choice model estimation using a common data set that is also used to illustrate StatWizards' capabilities. For an introduction to the data set, watch our tutorial "Introduction to the Test Data Set".  In addition to studying the following examples, we encourage you to download free versions of our products, go through the tutorials and make use of our innovative help systems.

Tutorials

To see an animated tutorial on estimating discrete-choice models, choose the program and estimation technique you want to explore from the links below.

Tutorial    
Building a CBC-HB model using Sawtooth Software Program: CBC/HB Technique:  Hierarchical Bayes
Building a mixed logit model using Biogeme Program: Biogeme Technique:  Mixed logit
Building a nested logit model using Biogeme Program: Biogeme Technique:  Nested logit
Building a latent-class choice model using Latent GOLD Program: Latent GOLD Choice Technique:  Latent class choice
Building a random parameters logit model using NLogit Program: Limdep/NLOGIT Technique: Mixed logit
Building a Random Parameters Logit model Program: Limdep/NLOGIT Technique: Nested logit
Building a mixed logit model using R's mlogit package Program: R Technique: Mixed logit
Building a nested logit model using R's mlogit package Program: R Techinque: Nested logit

 

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