linearmodels.iv.results.IVGMMResults.wald_test¶
- IVGMMResults.wald_test(restriction=None, value=None, *, formula=None)¶
Test linear equality constraints using a Wald test
- Parameters
- restriction{
ndarray
,DataFrame
} q by nvar array containing linear weights to apply to parameters when forming the restrictions. It is not possible to use both restriction and formula.
- value{
ndarray
,Series
} q element array containing the restricted values.
- formula
Union
[str
,list
[str
]] formulaic linear constraints. The simplest formats are one of:
A single comma-separated string such as “x1=0, x2+x3=1”
A list of strings where each element is a single constraint such as [“x1=0”, “x2+x3=1”]
A single string without commas to test simple constraints such as “x1=x2=x3=0”
A dictionary where each key is a parameter restriction and the corresponding value is the restriction value, e.g., {“x1”: 0, “x2+x3”: 1}.
It is not possible to use both
restriction
andformula
.
- restriction{
- Returns
WaldTestStatistic
Test statistic for null that restrictions are valid.
Notes
Hypothesis test examines whether \(H_0:C\theta=v\) where the matrix C is
restriction
and v isvalue
. The test statistic has a \(\chi^2_q\) distribution where q is the number of rows in C.Examples
>>> import numpy as np >>> from linearmodels.datasets import wage >>> from linearmodels.iv import IV2SLS >>> data = wage.load() >>> formula = "np.log(wage) ~ 1 + exper + I(exper**2) + brthord + [educ ~ sibs]" >>> res = IV2SLS.from_formula(formula, data).fit()
Testing the experience is not needed in the model
>>> restriction = np.array([[0, 1, 0, 0, 0], ... [0, 0, 1, 0, 0]]) >>> value = np.array([0, 0]) >>> wald_res = res.wald_test(restriction, value)
Using the formula interface to test the same restrictions
>>> formula = "exper = I(exper**2) = 0" >>> wald_res = res.wald_test(formula=formula)
Using the formula interface with a list
>>> wald_res = res.wald_test(formula=["exper = 0", "I(exper**2) = 0"])
- Return type