6.6.3.3.5. statsmodels.sandbox.panel.panel_short.ShortPanelGLS¶
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class
statsmodels.sandbox.panel.panel_short.ShortPanelGLS(endog, exog, group, sigma_i=None)[source]¶ Short Panel with general intertemporal within correlation
assumes data is stacked by individuals, panel is balanced and within correlation structure is identical across individuals.
It looks like this can just inherit GLS and overwrite whiten
6.6.3.3.5.1. Methods¶
__init__(endog, exog, group[, sigma_i]) |
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fit([method, cov_type, cov_kwds, use_t]) |
Full fit of the model. |
fit_iterative([maxiter]) |
Perform an iterative two-step procedure to estimate the GLS model. |
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. |
get_within_cov(resid) |
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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) |
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whiten_groups(x, cholsigmainv_i) |
6.6.3.3.5.2. 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 |