The results classes of most models have several methods for Wald tests.
t_test
is vectorized for single hypothesis.
wald_test
is for joint hypothesis.
wald_test_terms
automatically tests that "terms", i.e. subset of coefficients are jointly zero, similar to a type 3 ANOVA table based on Wald tests.
See for example the docstring for t_test
after OLS, but all models inherit the same method and work in the same way (*).
https://www.statsmodels.org/dev/generated/statsmodels.regression.linear_model.OLSResults.t_test.html
for example
>>> t_test = results.t_test("income = education")
>>> print(t_test)
(*) There are a few models that do not follow the standard pattern where these wald tests are not yet available.
The t_test use either the normal or the t distribution, the other two wald tests use either chisquare or F distribution. The distribution can be selected using the use_t
keyword in model.fit
.
If use_t=True
then t and F distributions are used. if it is False
, then the normal and chisquare distributions are used. The default is t and F for linear regression models and normal and chisquare for all other models.