Software for discrete choice model estimation
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==Excel== | ==Excel== | ||
| - | Excel is the popular spreadsheet software. With | + | Excel is the popular spreadsheet software. With a good understanding of choice model structure, the discrete choice results can be transformed into a maximum likelihood problem (log-likelihood), which is solvable by Excel's build-in solver. However, such approach is not suitable for logit models with random coefficients since the construction of random draws for simulation is tedious in spreadsheets. |
*[http://www.cmu.edu/me/ddl/resources.html Prof. Jeremy Michalek's website] provides his logit regression spreadsheet for the model in his paper "Linking marketing and engineering product design decisions via analytical target cascading," Journal of Product Innovation Management, 2005, v22 p42-62. | *[http://www.cmu.edu/me/ddl/resources.html Prof. Jeremy Michalek's website] provides his logit regression spreadsheet for the model in his paper "Linking marketing and engineering product design decisions via analytical target cascading," Journal of Product Innovation Management, 2005, v22 p42-62. | ||
Revision as of 10:07, 31 October 2007
Most commercial statistics software packages offer build-in logit model estimation functions. Some academic researchers in economics and marketing science field provide their source codes for academic use.
Contents |
Excel
Excel is the popular spreadsheet software. With a good understanding of choice model structure, the discrete choice results can be transformed into a maximum likelihood problem (log-likelihood), which is solvable by Excel's build-in solver. However, such approach is not suitable for logit models with random coefficients since the construction of random draws for simulation is tedious in spreadsheets.
- Prof. Jeremy Michalek's website provides his logit regression spreadsheet for the model in his paper "Linking marketing and engineering product design decisions via analytical target cascading," Journal of Product Innovation Management, 2005, v22 p42-62.
SAS/MDC
SAS is a powerful software package for statistical application. The MDC (Multinomial Discrete Choice) module is capable to perform choice model regression for various discrete models, such as conditional logit, heteroscedastic extreme value, mixed logit, nested logit, and multinomial probit models. However, beginners without programming experience will spend more time to become familiar with the interface and be able to create codes.
- SAS/MDC Documentation (PDF)
- Logit model regression in SAS in UCLA SAS FAQ website
Stata
Stata is a data analysis and statistical package that provides everything you need for data analysis, data management, and graphics. It is also capable of logit model regression.
LIMDEP/NLOGIT
NLOGIT is an extension program of commercial LIMDEP statistical software. It provides the functions for estimation, model simulation and analysis of multinomial choice data. According to the company website, the latest version of NLOGIT is able to handle heterogeneity in variances of utility functions and mixed logit model.
Sawtooth
Sawtooth is a specialized in marketing research software for discrete choice model and conjoint analysis. It is able to generate conjoint survey questionnaire (on-line or print-out) through a friendly user interface. Sawtooh can measure the perceived values of specific product features, learn demand of a particular product, and forecast market acceptance.
R/bayesm
bayesm is a package written in R language by Peter Rossi and Rob McCulloch for marketing and micro-econometrics research purpose. The package is capable to perform Bayes Regression and Bayes Seemingly Unrelated Regression (SUR) and Bayesian analysis of choice-based conjoint data. It also covers various choice models, such as Multinomial Logit (MNL) and Multinomial Probit (MNP), Multivariate Probit, Negative Binomial (Poisson) Regression, Multivariate Mixtures of Normals.
Train's code
Prof. Ken Train offers his Matlab codes using both maximum likelihood estimation (MLE) and Bayesian approach for mixed logit model. He also provided the old Gauss codes using MLE for mixed logit estimation. Train's Gauss code has been modified and applied to a study about multiparty elections by Prof. Glasgow Garrett, where his code and data are provided.
Lenk's Code
Prof. Peter Lenk offers his Gauss code using Bayesian methods for discrete choice model estimation.
Biogeme
A free software package provided by Prof. Michael Bierlaire using the maximum likelihood estimation for Generalized Extreme Value (GEV) models. It can be used for Multinomial Logit models, Nested logit models and other types of GEV models.
DCM Package
DCM stands for Discrete Choice Model. It is free software package written in Ox (a substitute called OxMetrics has trial version available) which is a commercial statistics programming language. It is provided by Matias Eklof at Uppsala University and Melvyn Weeks at University of Cambridge. However, the package has not been updated since August 2005.
| Software | Type | User Interface | Design | Estimation | Prediction | Scalability | Hierarchical | Cost |
|---|---|---|---|---|---|---|---|---|
| Excel | spreadsheet | graphical | no | yes | maybe | poor | no | $ |
| SAS/MDC | statistics package | code | yes | yes | excellent | no | $ | |
| Stata | statistics package | code | no | yes | no | no | $ | |
| SPSS | statistics package | graphical | no | yes | no | no | $ | |
| LIMDEP/NLOGIT | statistics package | graphical | yes | yes | yes | no | $$$ | |
| Sawtooth | conjoint package | graphical | yes | yes | yes | excellent | available | $$$ |
| R/bayesm | statistics package | code | no | yes | no | poor | yes | free |
| Ken Train's Code | research code | code | no | yes | no | yes | Free - Require Matlab or Gauss | |
| Peter Lenk's Code | research code | code | yes | free - Require GAUSS | ||||
| Biogeme | research program | input file | no | yes | no | free | ||
| DCM package | research program | code | no | yes | no | free - Require Ox or OxMetrics4 |
