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Discrete Choice Models Overview

examples/notebooks/discrete_choice_overview.ipynb

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Discrete Choice Models Overview

python
import numpy as np
import statsmodels.api as sm

Data

Load data from Spector and Mazzeo (1980). Examples follow Greene's Econometric Analysis Ch. 21 (5th Edition).

python
spector_data = sm.datasets.spector.load()
spector_data.exog = sm.add_constant(spector_data.exog, prepend=False)

Inspect the data:

python
print(spector_data.exog.head())
print(spector_data.endog.head())

Linear Probability Model (OLS)

python
lpm_mod = sm.OLS(spector_data.endog, spector_data.exog)
lpm_res = lpm_mod.fit()
print("Parameters: ", lpm_res.params[:-1])

Logit Model

python
logit_mod = sm.Logit(spector_data.endog, spector_data.exog)
logit_res = logit_mod.fit(disp=0)
print("Parameters: ", logit_res.params)

Marginal Effects

python
margeff = logit_res.get_margeff()
print(margeff.summary())

As in all the discrete data models presented below, we can print a nice summary of results:

python
print(logit_res.summary())

Probit Model

python
probit_mod = sm.Probit(spector_data.endog, spector_data.exog)
probit_res = probit_mod.fit()
probit_margeff = probit_res.get_margeff()
print("Parameters: ", probit_res.params)
print("Marginal effects: ")
print(probit_margeff.summary())

Multinomial Logit

Load data from the American National Election Studies:

python
anes_data = sm.datasets.anes96.load()
anes_exog = anes_data.exog
anes_exog = sm.add_constant(anes_exog)

Inspect the data:

python
print(anes_data.exog.head())
print(anes_data.endog.head())

Fit MNL model:

python
mlogit_mod = sm.MNLogit(anes_data.endog, anes_exog)
mlogit_res = mlogit_mod.fit()
print(mlogit_res.params)

Poisson

Load the Rand data. Note that this example is similar to Cameron and Trivedi's Microeconometrics Table 20.5, but it is slightly different because of minor changes in the data.

python
rand_data = sm.datasets.randhie.load()
rand_exog = rand_data.exog
rand_exog = sm.add_constant(rand_exog, prepend=False)

Fit Poisson model:

python
poisson_mod = sm.Poisson(rand_data.endog, rand_exog)
poisson_res = poisson_mod.fit(method="newton")
print(poisson_res.summary())

Negative Binomial

The negative binomial model gives slightly different results.

python
mod_nbin = sm.NegativeBinomial(rand_data.endog, rand_exog)
res_nbin = mod_nbin.fit(disp=False)
print(res_nbin.summary())

Alternative solvers

The default method for fitting discrete data MLE models is Newton-Raphson. You can use other solvers by using the method argument:

python
mlogit_res = mlogit_mod.fit(method="bfgs", maxiter=250)
print(mlogit_res.summary())