statsmodels.tsa.vector_ar.var_model.VARProcess.plotsim VARProcess.plotsim(steps=1000) [source] Plot a simulation from the VAR(p) process for the desired number of steps

statsmodels.sandbox.regression.gmm.IVRegressionResults.wald_test IVRegressionResults.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 examples.

statsmodels.discrete.discrete_model.DiscreteResults.pvalues static DiscreteResults.pvalues()

statsmodels.nonparametric.kde.KDEUnivariate.entropy static KDEUnivariate.entropy() [source] Returns the differential entropy evaluated at the support Notes Will not work if fit has not been called. 1e-12 is added to each probability to ensure that log(0) is not called.

statsmodels.genmod.generalized_linear_model.GLMResults.tvalues static GLMResults.tvalues() Return the t-statistic for a given parameter estimate.

statsmodels.tsa.arima_model.ARIMA.loglike_kalman ARIMA.loglike_kalman(params, set_sigma2=True) Compute exact loglikelihood for ARMA(p,q) model by the Kalman Filter.

statsmodels.tsa.vector_ar.irf.IRAnalysis.lr_effect_cov IRAnalysis.lr_effect_cov(orth=False) [source]

statsmodels.sandbox.regression.gmm.GMMResults.normalized_cov_params GMMResults.normalized_cov_params()

statsmodels.discrete.discrete_model.Poisson.fit Poisson.fit(start_params=None, method='newton', maxiter=35, full_output=1, disp=1, callback=None, **kwargs) [source] Fit the model using maximum likelihood. The rest of the docstring is from statsmodels.base.model.LikelihoodModel.fit Fit method for likelihood based models Parameters: start_params : array-like, optional Initial guess of the solution for the loglikelihood maximization. The default is an array of zeros. method : str, optional

statsmodels.tsa.ar_model.ARResults.tvalues static ARResults.tvalues() Return the t-statistic for a given parameter estimate.