statsmodels.genmod.generalized_estimating_equations.GEEMargins.conf_int GEEMargins.conf_int(alpha=0.05) [source] Returns the confidence intervals of the marginal effects Parameters: alpha : float Number between 0 and 1. The confidence intervals have the probability 1-alpha. Returns: conf_int : ndarray An array with lower, upper confidence intervals for the marginal effects.

statsmodels.tsa.vector_ar.var_model.VAR class statsmodels.tsa.vector_ar.var_model.VAR(endog, dates=None, freq=None, missing='none') [source] Fit VAR(p) process and do lag order selection Parameters: endog : array-like 2-d endogenous response variable. The independent variable. dates : array-like must match number of rows of endog References Lutkepohl (2005) New Introduction to Multiple Time Series Analysis Methods fit([maxlags, method, ic, trend, verbose]) Fit the VAR model from_f

statsmodels.tsa.vector_ar.var_model.VARProcess.acorr VARProcess.acorr(nlags=None) [source] Compute theoretical autocorrelation function Returns: acorr : ndarray (p x k x k)

statsmodels.tools.eval_measures.aic statsmodels.tools.eval_measures.aic(llf, nobs, df_modelwc) [source] Akaike information criterion Parameters: llf : float value of the loglikelihood nobs : int number of observations df_modelwc : int number of parameters including constant Returns: aic : float information criterion References http://en.wikipedia.org/wiki/Akaike_information_criterion

statsmodels.sandbox.distributions.transformed.Transf_gen.cdf Transf_gen.cdf(x, *args, **kwds) Cumulative distribution function of the given RV. Parameters: x : array_like quantiles 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 scale parameter (default=1) Returns: cdf : ndarray Cumulative distribution fun

statsmodels.tsa.arima_model.ARIMAResults.wald_test ARIMAResults.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 : A tuple of a

statsmodels.regression.linear_model.RegressionResults.rsquared_adj static RegressionResults.rsquared_adj() [source]

statsmodels.sandbox.regression.gmm.IVGMMResults.get_bse IVGMMResults.get_bse(**kwds) standard error of the parameter estimates with options Parameters: kwds : optional keywords options for calculating cov_params Returns: bse : ndarray estimated standard error of parameter estimates

statsmodels.sandbox.regression.gmm.LinearIVGMM.gmmobjective_cu LinearIVGMM.gmmobjective_cu(params, weights_method='cov', wargs=()) objective function for continuously updating GMM minimization Parameters: params : array parameter values at which objective is evaluated Returns: jval : float value of objective function

statsmodels.sandbox.regression.gmm.LinearIVGMM.calc_weightmatrix LinearIVGMM.calc_weightmatrix(moms, weights_method='cov', wargs=(), params=None) calculate omega or the weighting matrix Parameters: moms : array, (nobs, nmoms) moment conditions for all observations evaluated at a parameter value weights_method : string ?cov? If method=?cov? is cov then the matrix is calculated as simple covariance of the moment conditions. see fit method for available aoptions for the weight and covarianc