statsmodels.tsa.vector_ar.var_model.VARResults.llf static VARResults.llf() [source] Compute VAR(p) loglikelihood

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

statsmodels.regression.linear_model.GLS.initialize GLS.initialize()

statsmodels.tsa.arima_model.ARIMAResults.normalized_cov_params ARIMAResults.normalized_cov_params()

statsmodels.discrete.discrete_model.Logit.cov_params_func_l1 Logit.cov_params_func_l1(likelihood_model, xopt, retvals) Computes cov_params on a reduced parameter space corresponding to the nonzero parameters resulting from the l1 regularized fit. Returns a full cov_params matrix, with entries corresponding to zero?d values set to np.nan.

statsmodels.miscmodels.tmodel.TLinearModel.score TLinearModel.score(params) Gradient of log-likelihood evaluated at params

statsmodels.tsa.vector_ar.var_model.VAR.initialize VAR.initialize() Initialize (possibly re-initialize) a Model instance. For instance, the design matrix of a linear model may change and some things must be recomputed.

statsmodels.discrete.discrete_model.ProbitResults.f_test ProbitResults.f_test(r_matrix, cov_p=None, scale=1.0, invcov=None) Compute the F-test for a joint linear hypothesis. This is a special case of wald_test that always uses the F distribution. 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

statsmodels.sandbox.regression.gmm.LinearIVGMM class statsmodels.sandbox.regression.gmm.LinearIVGMM(endog, exog, instrument, k_moms=None, k_params=None, missing='none', **kwds) [source] class for linear instrumental variables models estimated with GMM Uses closed form expression instead of nonlinear optimizers for each step of the iterative GMM. The model is assumed to have the following moment condition E( z * (y - x beta)) = 0 Where y is the dependent endogenous variable, x are the explan