statsmodels.tsa.vector_ar.dynamic.DynamicVAR.plot_forecast DynamicVAR.plot_forecast(steps=1, figsize=(10, 10)) [source] Plot h-step ahead forecasts against actual realizations of time series. Note that forecasts are lined up with their respective realizations. Parameters: steps : :

statsmodels.regression.linear_model.RegressionResults.cov_HC0 static RegressionResults.cov_HC0() [source] See statsmodels.RegressionResults

statsmodels.nonparametric.kde.KDEUnivariate.cdf static KDEUnivariate.cdf() [source] Returns the cumulative distribution function evaluated at the support. Notes Will not work if fit has not been called.

statsmodels.discrete.discrete_model.LogitResults.tvalues static LogitResults.tvalues() Return the t-statistic for a given parameter estimate.

statsmodels.discrete.discrete_model.MultinomialModel.information MultinomialModel.information(params) Fisher information matrix of model Returns -Hessian of loglike evaluated at params.

statsmodels.stats.sandwich_covariance.cov_hc2 statsmodels.stats.sandwich_covariance.cov_hc2(results) [source] See statsmodels.RegressionResults

statsmodels.sandbox.stats.multicomp.GroupsStats.groupvarwithin GroupsStats.groupvarwithin() [source]

statsmodels.iolib.smpickle.load_pickle statsmodels.iolib.smpickle.load_pickle(fname) [source] Load a previously saved object from file Parameters: fname : str Filename to unpickle Notes This method can be used to load both models and results.

statsmodels.genmod.cov_struct.Autoregressive.initialize Autoregressive.initialize(model) Called by GEE, used by implementations that need additional setup prior to running fit. Parameters: model : GEE class A reference to the parent GEE class instance.

statsmodels.discrete.discrete_model.DiscreteModel.initialize DiscreteModel.initialize() [source] Initialize is called by statsmodels.model.LikelihoodModel.__init__ and should contain any preprocessing that needs to be done for a model.