# statsmodels.discrete.truncated_model.HurdleCountResults.wald_test¶

HurdleCountResults.wald_test(r_matrix, cov_p=`None`, invcov=`None`, use_f=`None`, df_constraints=`None`, scalar=`None`)

Compute a Wald-test for a joint linear hypothesis.

Parameters:
r_matrix

One of:

• array : An r x k array where r is the number of restrictions to test and k is the number of regressors. It is assumed that the linear combination is equal to zero.

• str : The full hypotheses to test can be given as a string. See the examples.

• tuple : A tuple of arrays in the form (R, q), `q` can be either a scalar or a length p row vector.

cov_parray_like, `optional`

An alternative estimate for the parameter covariance matrix. If None is given, self.normalized_cov_params is used.

invcovarray_like, `optional`

A q x q array to specify an inverse covariance matrix based on a restrictions matrix.

use_fbool

If True, then the F-distribution is used. If False, then the asymptotic distribution, chisquare is used. If use_f is None, then the F distribution is used if the model specifies that use_t is True. The test statistic is proportionally adjusted for the distribution by the number of constraints in the hypothesis.

df_constraints`int`, `optional`

The number of constraints. If not provided the number of constraints is determined from r_matrix.

scalarbool, `optional`

Flag indicating whether the Wald test statistic should be returned as a sclar float. The current behavior is to return an array. This will switch to a scalar float after 0.14 is released. To get the future behavior now, set scalar to True. To silence the warning and retain the legacy behavior, set scalar to False.

Returns:
`ContrastResults`

The results for the test are attributes of this results instance.

`f_test`

Perform an F tests on model parameters.

`t_test`

Perform a single hypothesis test.

`statsmodels.stats.contrast.ContrastResults`

Test results.

`patsy.DesignInfo.linear_constraint`

Specify a linear constraint.

Notes

The matrix r_matrix is assumed to be non-singular. More precisely,

r_matrix (pX pX.T) r_matrix.T

is assumed invertible. Here, pX is the generalized inverse of the design matrix of the model. There can be problems in non-OLS models where the rank of the covariance of the noise is not full.

Last update: Jul 16, 2024