7.6. Regression with Discrete Dependent Variable¶

7.6.1. Examples¶

# Load the data from Spector and Mazzeo (1980)
spector_data = sm.datasets.spector.load()
spector_data.exog = sm.add_constant(spector_data.exog)

# Logit Model
logit_mod = sm.Logit(spector_data.endog, spector_data.exog)
logit_res = logit_mod.fit()
print logit_res.summary()

Detailed examples can be found here:

• Discrete Choice Models Overview
• Discrete Choice Models

7.6.2. Technical Documentation¶

Currently all models are estimated by Maximum Likelihood and assume independently and identically distributed errors.

All discrete regression models define the same methods and follow the same structure, which is similar to the regression results but with some methods specific to discrete models. Additionally some of them contain additional model specific methods and attributes.

A.C. Cameron and P.K. Trivedi.  `Regression Analysis of Count Data`.
    Cambridge, 1998

G.S. Madalla. `Limited-Dependent and Qualitative Variables in Econometrics`.
    Cambridge, 1983.

W. Greene. `Econometric Analysis`. Prentice Hall, 5th. edition. 2003.

7.6.3. Module Reference¶

The specific model classes are:

The specific result classes are:

DiscreteModel is a superclass of all discrete regression models. The estimation results are returned as an instance of one of the subclasses of DiscreteResults. Each category of models, binary, count and multinomial, have their own intermediate level of model and results classes. This intermediate classes are mostly to facilitate the implementation of the methods and attributes defined by DiscreteModel and DiscreteResults.