1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
|
#!/usr/bin/env python
# coding: utf-8
# DO NOT EDIT
# Autogenerated from the notebook discrete_choice_overview.ipynb.
# Edit the notebook and then sync the output with this file.
#
# flake8: noqa
# DO NOT EDIT
# # Discrete Choice Models Overview
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).
spector_data = sm.datasets.spector.load()
spector_data.exog = sm.add_constant(spector_data.exog, prepend=False)
# Inspect the data:
print(spector_data.exog.head())
print(spector_data.endog.head())
# ## Linear Probability Model (OLS)
lpm_mod = sm.OLS(spector_data.endog, spector_data.exog)
lpm_res = lpm_mod.fit()
print("Parameters: ", lpm_res.params[:-1])
# ## Logit Model
logit_mod = sm.Logit(spector_data.endog, spector_data.exog)
logit_res = logit_mod.fit(disp=0)
print("Parameters: ", logit_res.params)
# Marginal Effects
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:
print(logit_res.summary())
# ## Probit Model
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:
anes_data = sm.datasets.anes96.load()
anes_exog = anes_data.exog
anes_exog = sm.add_constant(anes_exog)
# Inspect the data:
print(anes_data.exog.head())
print(anes_data.endog.head())
# Fit MNL model:
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.
rand_data = sm.datasets.randhie.load()
rand_exog = rand_data.exog
rand_exog = sm.add_constant(rand_exog, prepend=False)
# Fit Poisson model:
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.
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:
mlogit_res = mlogit_mod.fit(method="bfgs", maxiter=250)
print(mlogit_res.summary())
|
