statsmodels.discrete.discrete_model.LogitResults.llr_pvalue static LogitResults.llr_pvalue()

statsmodels.discrete.discrete_model.MultinomialResults.load classmethod MultinomialResults.load(fname) load a pickle, (class method) Parameters: fname : string or filehandle fname can be a string to a file path or filename, or a filehandle. Returns: unpickled instance :

statsmodels.sandbox.distributions.transformed.TransfTwo_gen.stats TransfTwo_gen.stats(*args, **kwds) Some statistics of the given RV Parameters: arg1, arg2, arg3,... : array_like The shape parameter(s) for the distribution (see docstring of the instance object for more information) loc : array_like, optional location parameter (default=0) scale : array_like, optional (discrete RVs only) scale parameter (default=1) moments : str, optional composed of letters [?mvsk?] defining which mo

statsmodels.tsa.vector_ar.irf.IRAnalysis.err_band_sz1 IRAnalysis.err_band_sz1(orth=False, svar=False, repl=1000, signif=0.05, seed=None, burn=100, component=None) [source] IRF Sims-Zha error band method 1. Assumes symmetric error bands around mean. Parameters: orth : bool, default False Compute orthogonalized impulse responses repl : int, default 1000 Number of MC replications signif : float (0 < signif < 1) Significance level for error bars, defaults to 95% CI seed : int, defau

statsmodels.sandbox.distributions.transformed.SquareFunc.squarefunc SquareFunc.squarefunc(x) [source]

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.stats.weightstats.DescrStatsW.cov static DescrStatsW.cov() [source] weighted covariance of data if data is 2 dimensional assumes variables in columns and observations in rows uses default ddof

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.