statsmodels.regression.linear_model.OLSResults.HC0_se static OLSResults.HC0_se() See statsmodels.RegressionResults

statsmodels.tsa.vector_ar.var_model.VARResults.pvalues static VARResults.pvalues() [source] Two-sided p-values for model coefficients from Student t-distribution

statsmodels.sandbox.regression.gmm.IVRegressionResults.summary2 IVRegressionResults.summary2(yname=None, xname=None, title=None, alpha=0.05, float_format='%.4f') Experimental summary function to summarize the regression results Parameters: xname : List of strings of length equal to the number of parameters Names of the independent variables (optional) yname : string Name of the dependent variable (optional) title : string, optional Title for the top table. If not None, then this replac

statsmodels.tsa.filters.cf_filter.cffilter statsmodels.tsa.filters.cf_filter.cffilter(X, low=6, high=32, drift=True) [source] Christiano Fitzgerald asymmetric, random walk filter Parameters: X : array-like 1 or 2d array to filter. If 2d, variables are assumed to be in columns. low : float Minimum period of oscillations. Features below low periodicity are filtered out. Default is 6 for quarterly data, giving a 1.5 year periodicity. high : float Maximum period of oscillations. Features a

statsmodels.miscmodels.count.PoissonOffsetGMLE.loglike PoissonOffsetGMLE.loglike(params)

statsmodels.discrete.discrete_model.CountResults.bic static CountResults.bic()

statsmodels.discrete.discrete_model.DiscreteResults.wald_test DiscreteResults.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. tuple

statsmodels.tsa.vector_ar.var_model.VARResults.acf VARResults.acf(nlags=None) Compute theoretical autocovariance function Returns: acf : ndarray (p x k x k)

statsmodels.genmod.families.links.Log.deriv Log.deriv(p) [source] Derivative of log transform link function Parameters: p : array-like Mean parameters Returns: g?(p) : array derivative of log transform of x Notes g(x) = 1/x

statsmodels.genmod.generalized_estimating_equations.GEEMargins.pvalues static GEEMargins.pvalues() [source]