.. currentmodule:: statsmodels.discrete.discrete_model

.. _discretemod:

Regression with Discrete Dependent Variable

Regression models for limited and qualitative dependent variables. The module currently allows the estimation of models with binary (Logit, Probit), nominal (MNLogit), or count (Poisson, NegativeBinomial) data.

Starting with version 0.9, this also includes new count models, that are still experimental in 0.9, NegativeBinomialP, GeneralizedPoisson and zero-inflated models, ZeroInflatedPoisson, ZeroInflatedNegativeBinomialP and ZeroInflatedGeneralizedPoisson.

See Module Reference_ for commands and arguments.

Examples

.. ipython:: python :okwarning:

Load the data from Spector and Mazzeo (1980)

import statsmodels.api as sm spector_data = sm.datasets.spector.load_pandas() 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:

• Overview <examples/notebooks/generated/discrete_choice_overview.ipynb>_
• Examples <examples/notebooks/generated/discrete_choice_example.ipynb>_

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.

References ^^^^^^^^^^

General references for this class of models are::

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.

Module Reference

.. module:: statsmodels.discrete.discrete_model :synopsis: Models for discrete data

The specific model classes are:

.. autosummary:: :toctree: generated/

Logit Probit MNLogit Poisson NegativeBinomial NegativeBinomialP GeneralizedPoisson

.. currentmodule:: statsmodels.discrete.count_model .. module:: statsmodels.discrete.count_model

.. autosummary:: :toctree: generated/

ZeroInflatedPoisson ZeroInflatedNegativeBinomialP ZeroInflatedGeneralizedPoisson

.. currentmodule:: statsmodels.discrete.truncated_model .. module:: statsmodels.discrete.truncated_model

.. autosummary:: :toctree: generated/

HurdleCountModel TruncatedLFNegativeBinomialP TruncatedLFPoisson

.. currentmodule:: statsmodels.discrete.conditional_models .. module:: statsmodels.discrete.conditional_models

.. autosummary:: :toctree: generated/

ConditionalLogit ConditionalMNLogit ConditionalPoisson

The cumulative link model for an ordinal dependent variable is currently in miscmodels as it subclasses GenericLikelihoodModel. This will change in future versions.

.. currentmodule:: statsmodels.miscmodels.ordinal_model .. module:: statsmodels.miscmodels.ordinal_model

.. autosummary:: :toctree: generated/

OrderedModel

The specific result classes are:

.. currentmodule:: statsmodels.discrete.discrete_model

.. autosummary:: :toctree: generated/

LogitResults ProbitResults CountResults MultinomialResults NegativeBinomialResults GeneralizedPoissonResults

.. currentmodule:: statsmodels.discrete.count_model

.. autosummary:: :toctree: generated/

ZeroInflatedPoissonResults ZeroInflatedNegativeBinomialResults ZeroInflatedGeneralizedPoissonResults

.. currentmodule:: statsmodels.discrete.truncated_model

.. autosummary:: :toctree: generated/

HurdleCountResults TruncatedLFPoissonResults TruncatedNegativeBinomialResults

.. currentmodule:: statsmodels.discrete.conditional_models

.. autosummary:: :toctree: generated/

ConditionalResults

.. currentmodule:: statsmodels.miscmodels.ordinal_model

.. autosummary:: :toctree: generated/

OrderedResults

:class:DiscreteModel is a superclass of all discrete regression models. The estimation results are returned as an instance of one of the subclasses of :class: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 :class:DiscreteModel and :class:DiscreteResults.

.. currentmodule:: statsmodels.discrete.discrete_model

.. autosummary:: :toctree: generated/

DiscreteModel DiscreteResults BinaryModel BinaryResults CountModel MultinomialModel

.. currentmodule:: statsmodels.discrete.count_model

.. autosummary:: :toctree: generated/

GenericZeroInflated