statsmodels.stats.diagnostic.linear_reset¶
- statsmodels.stats.diagnostic.linear_reset(res, power=3, test_type='fitted', use_f=False, cov_type='nonrobust', cov_kwargs=None)[source]¶
Ramsey’s RESET test for neglected nonlinearity
- Parameters:
- res
RegressionResults
A results instance from a linear regression.
- power{
int
,List
[int
]},default
3 The maximum power to include in the model, if an integer. Includes powers 2, 3, …, power. If an list of integers, includes all powers in the list.
- test_type
str
,default
“fitted” The type of augmentation to use:
“fitted” : (default) Augment regressors with powers of fitted values.
“exog” : Augment exog with powers of exog. Excludes binary regressors.
“princomp”: Augment exog with powers of first principal component of exog.
- use_fbool,
default
False
Flag indicating whether an F-test should be used (True) or a chi-square test (False).
- cov_type
str
,default
“nonrobust Covariance type. The default is “nonrobust` which uses the classic OLS covariance estimator. Specify one of “HC0”, “HC1”, “HC2”, “HC3” to use White’s covariance estimator. All covariance types supported by
OLS.fit
are accepted.- cov_kwargs
dict
,default
None
Dictionary of covariance options passed to
OLS.fit
. See OLS.fit for more details.
- res
- Returns:
ContrastResults
Test results for Ramsey’s Reset test. See notes for implementation details.
Notes
The RESET test uses an augmented regression of the form
\[Y = X\beta + Z\gamma + \epsilon\]where \(Z\) are a set of regressors that are one of:
Powers of \(X\hat{\beta}\) from the original regression.
Powers of \(X\), excluding the constant and binary regressors.
Powers of the first principal component of \(X\). If the model includes a constant, this column is dropped before computing the principal component. In either case, the principal component is extracted from the correlation matrix of remaining columns.
The test is a Wald test of the null \(H_0:\gamma=0\). If use_f is True, then the quadratic-form test statistic is divided by the number of restrictions and the F distribution is used to compute the critical value.