statsmodels.regression.linear_model.GLSAR.loglike GLSAR.loglike(params) 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 : float The value of the log-likelihood function for a GLS Model. Notes The log-likelihood function for the normal distribution is Y and Y-h

statsmodels.regression.linear_model.GLSAR.predict GLSAR.predict(params, exog=None) Return linear predicted values from a design matrix. Parameters: params : array-like Parameters of a linear model exog : array-like, optional. Design / exogenous data. Model exog is used if None. Returns: An array of fitted values : Notes If the model has not yet been fit, params is not optional.

statsmodels.regression.linear_model.GLSAR.iterative_fit GLSAR.iterative_fit(maxiter=3) [source] Perform an iterative two-stage procedure to estimate a GLS model. The model is assumed to have AR(p) errors, AR(p) parameters and regression coefficients are estimated iteratively. Parameters: maxiter : integer, optional the number of iterations

statsmodels.regression.linear_model.GLSAR.information GLSAR.information(params) Fisher information matrix of model Returns -Hessian of loglike evaluated at params.

statsmodels.regression.linear_model.GLSAR.initialize GLSAR.initialize()

statsmodels.regression.linear_model.GLSAR.from_formula classmethod GLSAR.from_formula(formula, data, subset=None, *args, **kwargs) Create a Model from a formula and dataframe. Parameters: formula : str or generic Formula object The formula specifying the model data : array-like The data for the model. See Notes. subset : array-like An array-like object of booleans, integers, or index values that indicate the subset of df to use in the model. Assumes df is a pandas.DataFrame args : ext

statsmodels.regression.linear_model.GLSAR.hessian GLSAR.hessian(params) The Hessian matrix of the model

statsmodels.regression.linear_model.GLSAR.fit_regularized GLSAR.fit_regularized(method='coord_descent', maxiter=1000, alpha=0.0, L1_wt=1.0, start_params=None, cnvrg_tol=1e-08, zero_tol=1e-08, **kwargs) Return a regularized fit to a linear regression model. Parameters: method : string Only the coordinate descent algorithm is implemented. maxiter : integer The maximum number of iteration cycles (an iteration cycle involves running coordinate descent on all variables). alpha : scalar or ar

statsmodels.regression.linear_model.GLS.whiten GLS.whiten(X) [source] GLS whiten method. Parameters: X : array-like Data to be whitened. Returns: np.dot(cholsigmainv,X) : See also regression.GLS

statsmodels.regression.linear_model.GLSAR.fit GLSAR.fit(method='pinv', cov_type='nonrobust', cov_kwds=None, use_t=None, **kwargs) Full fit of the model. The results include an estimate of covariance matrix, (whitened) residuals and an estimate of scale. Parameters: method : str Can be ?pinv?, ?qr?. ?pinv? uses the Moore-Penrose pseudoinverse to solve the least squares problem. ?qr? uses the QR factorization. Returns: A RegressionResults class instance. : See also regression.Regression