statsmodels.regression.linear_model.GLS.loglike

GLS.loglike(params) [source]

Returns the value of the Gaussian log-likelihood function at params.

Given the whitened design matrix, the log-likelihood is evaluated at the parameter vector params for the dependent variable endog.

Parameters:	params (array-like) – The parameter estimates
Returns:	loglike – The value of the log-likelihood function for a GLS Model.
Return type:	float

Notes

The log-likelihood function for the normal distribution is

\[-\frac{n}{2}\log\left(\left(Y-\hat{Y}\right)^{\prime}\left(Y-\hat{Y}\right)\right)-\frac{n}{2}\left(1+\log\left(\frac{2\pi}{n}\right)\right)-\frac{1}{2}\log\left(\left|\Sigma\right|\right)\]

Y and Y-hat are whitened.