statsmodels.sandbox.regression.gmm.IV2SLS.loglike IV2SLS.loglike(params) Log-likelihood of model.

statsmodels.miscmodels.count.PoissonZiGMLE.score_obs PoissonZiGMLE.score_obs(params, **kwds) Jacobian/Gradient of log-likelihood evaluated at params for each observation.

statsmodels.genmod.generalized_linear_model.GLMResults.resid_response static GLMResults.resid_response() [source]

statsmodels.sandbox.tsa.fftarma.ArmaFft.arma2ma ArmaFft.arma2ma(nobs=None)

statsmodels.miscmodels.count.PoissonOffsetGMLE.score PoissonOffsetGMLE.score(params) Gradient of log-likelihood evaluated at params

statsmodels.miscmodels.tmodel.TLinearModel.information TLinearModel.information(params) Fisher information matrix of model Returns -Hessian of loglike evaluated at params.

statsmodels.tsa.vector_ar.var_model.VARResults.resid_corr static VARResults.resid_corr() [source] Centered residual correlation matrix

statsmodels.sandbox.regression.gmm.IVRegressionResults.nobs static IVRegressionResults.nobs()

statsmodels.discrete.discrete_model.NegativeBinomialResults.get_margeff NegativeBinomialResults.get_margeff(at='overall', method='dydx', atexog=None, dummy=False, count=False) Get marginal effects of the fitted model. Parameters: at : str, optional Options are: ?overall?, The average of the marginal effects at each observation. ?mean?, The marginal effects at the mean of each regressor. ?median?, The marginal effects at the median of each regressor. ?zero?, The marginal effects at zero for

statsmodels.tsa.vector_ar.irf.IRAnalysis.cum_effect_stderr IRAnalysis.cum_effect_stderr(orth=False) [source]