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.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

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

statsmodels.tsa.ar_model.ARResults.f_test ARResults.f_test(r_matrix, cov_p=None, scale=1.0, invcov=None) Compute the F-test for a joint linear hypothesis. This is a special case of wald_test that always uses the F distribution. 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 s

statsmodels.sandbox.regression.gmm.NonlinearIVGMM.start_weights NonlinearIVGMM.start_weights(inv=True)

Linear Regression Linear models with independently and identically distributed errors, and for errors with heteroscedasticity or autocorrelation. This module allows estimation by ordinary least squares (OLS), weighted least squares (WLS), generalized least squares (GLS), and feasible generalized least squares with autocorrelated AR(p) errors. See Module Reference for commands and arguments. Examples # Load modules and data import numpy as np import statsmodels.api as sm spector_data = sm.datas

statsmodels.regression.linear_model.OLSResults.get_robustcov_results OLSResults.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 robust covariance c

statsmodels.miscmodels.count.PoissonGMLE.hessian PoissonGMLE.hessian(params) Hessian of log-likelihood evaluated at params