statsmodels.tsa.arima_model.ARIMAResults.bic static ARIMAResults.bic()

statsmodels.discrete.discrete_model.LogitResults.summary LogitResults.summary(yname=None, xname=None, title=None, alpha=0.05, yname_list=None) Summarize the Regression Results Parameters: yname : string, optional Default is y xname : list of strings, optional Default is var_## for ## in p the number of regressors title : string, optional Title for the top table. If not None, then this replaces the default title alpha : float significance level for the confidence intervals Returns:

statsmodels.tsa.arima_model.ARMA.geterrors ARMA.geterrors(params) [source] Get the errors of the ARMA process. Parameters: params : array-like The fitted ARMA parameters order : array-like 3 item iterable, with the number of AR, MA, and exogenous parameters, including the trend

statsmodels.sandbox.regression.gmm.LinearIVGMM.momcond LinearIVGMM.momcond(params)

statsmodels.regression.linear_model.RegressionResults.cov_HC3 static RegressionResults.cov_HC3() [source] See statsmodels.RegressionResults

statsmodels.sandbox.regression.gmm.IVRegressionResults.HC2_se static IVRegressionResults.HC2_se() See statsmodels.RegressionResults

statsmodels.stats.diagnostic.het_goldfeldquandt statsmodels.stats.diagnostic.het_goldfeldquandt = see class docstring

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