statsmodels.tsa.vector_ar.var_model.VARProcess class statsmodels.tsa.vector_ar.var_model.VARProcess(coefs, intercept, sigma_u, names=None) [source] Class represents a known VAR(p) process Parameters: coefs : ndarray (p x k x k) intercept : ndarray (length k) sigma_u : ndarray (k x k) names : sequence (length k) Returns: **Attributes:** : Methods acf([nlags]) Compute theoretical autocovariance function acorr([nlags]) Compute theoretical autocorrelation function forecast(y, steps) Produ

statsmodels.discrete.discrete_model.BinaryResults.llnull static BinaryResults.llnull()

statsmodels.discrete.discrete_model.DiscreteResults.bic static DiscreteResults.bic() [source]

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.graphics.factorplots.interaction_plot statsmodels.graphics.factorplots.interaction_plot(x, trace, response, func=, ax=None, plottype='b', xlabel=None, ylabel=None, colors=[], markers=[], linestyles=[], legendloc='best', legendtitle=None, **kwargs) [source] Interaction plot for factor level statistics. Note. If categorial factors are supplied levels will be internally recoded to integers. This ensures matplotlib compatiblity. uses pandas.DataFrame to calculate an aggregate statist

statsmodels.tsa.vector_ar.var_model.VARResults.tvalues static VARResults.tvalues() [source] Compute t-statistics. Use Student-t(T - Kp - 1) = t(df_resid) to test significance.

statsmodels.tsa.vector_ar.var_model.VARProcess.forecast VARProcess.forecast(y, steps) [source] Produce linear minimum MSE forecasts for desired number of steps ahead, using prior values y Parameters: y : ndarray (p x k) steps : int Returns: forecasts : ndarray (steps x neqs) Notes Lutkepohl pp 37-38

statsmodels.regression.linear_model.RegressionResults.normalized_cov_params RegressionResults.normalized_cov_params()

statsmodels.miscmodels.count.PoissonZiGMLE.fit PoissonZiGMLE.fit(start_params=None, method='nm', maxiter=500, full_output=1, disp=1, callback=None, retall=0, **kwargs) Fit the model using maximum likelihood. The rest of the docstring is from statsmodels.LikelihoodModel.fit

statsmodels.discrete.discrete_model.BinaryResults.llf static BinaryResults.llf()