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

statsmodels.sandbox.regression.gmm.IVRegressionResults.mse_total static IVRegressionResults.mse_total()

statsmodels.tsa.arima_model.ARMA.initialize ARMA.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.discrete.discrete_model.NegativeBinomialResults.wald_test NegativeBinomialResults.wald_test(r_matrix, cov_p=None, scale=1.0, invcov=None, use_f=None) Compute a Wald-test for a joint linear hypothesis. Parameters: r_matrix : array-like, str, or tuple array : An r x k array where r is the number of restrictions to test and k is the number of regressors. It is assumed that the linear combination is equal to zero. str : The full hypotheses to test can be given as a string. See the

statsmodels.discrete.discrete_model.BinaryResults.t_test BinaryResults.t_test(r_matrix, cov_p=None, scale=None, use_t=None) Compute a t-test for a each linear hypothesis of the form Rb = q Parameters: r_matrix : array-like, str, tuple array : If an array is given, a p x k 2d array or length k 1d array specifying the linear restrictions. It is assumed that the linear combination is equal to zero. str : The full hypotheses to test can be given as a string. See the examples. tuple : A tuple o

statsmodels.tsa.vector_ar.irf.IRAnalysis.H static IRAnalysis.H() [source]

statsmodels.tsa.varma_process.VarmaPoly.hstackarma_minus1 VarmaPoly.hstackarma_minus1() [source] stack ar and lagpolynomial vertically in 2d array this is the Kalman Filter representation, I think

statsmodels.iolib.table.SimpleTable.pop SimpleTable.pop([index]) ? item -- remove and return item at index (default last). Raises IndexError if list is empty or index is out of range.

statsmodels.sandbox.regression.gmm.IVRegressionResults.cov_HC0 static IVRegressionResults.cov_HC0() See statsmodels.RegressionResults

statsmodels.tsa.arima_model.ARIMAResults.plot_predict ARIMAResults.plot_predict(start=None, end=None, exog=None, dynamic=False, alpha=0.05, plot_insample=True, ax=None) [source] Plot forecasts Parameters: start : int, str, or datetime Zero-indexed observation number at which to start forecasting, ie., the first forecast is start. Can also be a date string to parse or a datetime type. end : int, str, or datetime Zero-indexed observation number at which to end forecasting, ie., the first f