3.9.1.2.3. statsmodels.regression.feasible_gls.GLSHet2¶
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class
statsmodels.regression.feasible_gls.GLSHet2(endog, exog, exog_var, sigma=None)[source]¶ WLS with heteroscedasticity that depends on explanatory variables
note: mixing GLS sigma and weights for heteroscedasticity might not make sense
I think rewriting following the pattern of GLSAR is better stopping criteria: improve in GLSAR also, e.g. change in rho
3.9.1.2.3.1. Methods¶
__init__(endog, exog, exog_var[, sigma]) |
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fit([lambd]) |
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fit_regularized([method, maxiter, alpha, ...]) |
Return a regularized fit to a linear regression model. |
from_formula(formula, data[, subset]) |
Create a Model from a formula and dataframe. |
hessian(params) |
The Hessian matrix of the model |
information(params) |
Fisher information matrix of model |
initialize() |
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loglike(params) |
Returns the value of the Gaussian log-likelihood function at params. |
predict(params[, exog]) |
Return linear predicted values from a design matrix. |
score(params) |
Score vector of model. |
whiten(X) |
GLS whiten method. |
3.9.1.2.3.2. Attributes¶
df_model |
The model degree of freedom, defined as the rank of the regressor matrix minus 1 if a constant is included. |
df_resid |
The residual degree of freedom, defined as the number of observations minus the rank of the regressor matrix. |
endog_names |
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exog_names |