statsmodels.regression.linear_model.OLSResults.mse_total static OLSResults.mse_total()

statsmodels.sandbox.regression.gmm.GMM.fitgmm GMM.fitgmm(start, weights=None, optim_method='bfgs', optim_args=None) [source] estimate parameters using GMM Parameters: start : array_like starting values for minimization weights : array weighting matrix for moment conditions. If weights is None, then the identity matrix is used Returns: paramest : array estimated parameters Notes todo: add fixed parameter option, not here ??? uses scipy.optimize.fmin

statsmodels.robust.robust_linear_model.RLM.initialize RLM.initialize() Initialize (possibly re-initialize) a Model instance. For instance, the design matrix of a linear model may change and some things must be recomputed.

statsmodels.regression.quantile_regression.QuantRegResults.get_robustcov_results QuantRegResults.get_robustcov_results(cov_type='HC1', use_t=None, **kwds) create new results instance with robust covariance as default Parameters: cov_type : string the type of robust sandwich estimator to use. see Notes below use_t : bool If true, then the t distribution is used for inference. If false, then the normal distribution is used. kwds : depends on cov_type Required or optional arguments for ro

statsmodels.tsa.arima_model.ARIMA.geterrors ARIMA.geterrors(params) Get the errors of the ARMA process. Parameters: params : array-like The fitted ARMA parameters order : array-like 3 item iterable, with the number of AR, MA, and exogenous parameters, including the trend

statsmodels.discrete.discrete_model.BinaryModel.hessian BinaryModel.hessian(params) The Hessian matrix of the model

statsmodels.iolib.table.SimpleTable.reverse SimpleTable.reverse() L.reverse() ? reverse IN PLACE

statsmodels.sandbox.regression.gmm.IVGMMResults.tvalues static IVGMMResults.tvalues() Return the t-statistic for a given parameter estimate.

statsmodels.tsa.vector_ar.var_model.VARResults.reorder VARResults.reorder(order) [source] Reorder variables for structural specification

statsmodels.discrete.discrete_model.ProbitResults.pred_table ProbitResults.pred_table(threshold=0.5) Prediction table Parameters: threshold : scalar Number between 0 and 1. Threshold above which a prediction is considered 1 and below which a prediction is considered 0. Notes pred_table[i,j] refers to the number of times ?i? was observed and the model predicted ?j?. Correct predictions are along the diagonal.