statsmodels.sandbox.regression.gmm.IVGMMResults.tvalues static IVGMMResults.tvalues() Return the t-statistic for a given parameter estimate.

statsmodels.tsa.vector_ar.var_model.VARResults.reorder VARResults.reorder(order) [source] Reorder variables for structural specification

statsmodels.regression.quantile_regression.QuantReg class statsmodels.regression.quantile_regression.QuantReg(endog, exog, **kwargs) [source] Quantile Regression Estimate a quantile regression model using iterative reweighted least squares. Parameters: endog : array or dataframe endogenous/response variable exog : array or dataframe exogenous/explanatory variable(s) Notes The Least Absolute Deviation (LAD) estimator is a special case where quantile is set to 0.5 (q argument of the fit

statsmodels.sandbox.distributions.extras.SkewNorm_gen.expect SkewNorm_gen.expect(func=None, args=(), loc=0, scale=1, lb=None, ub=None, conditional=False, **kwds) Calculate expected value of a function with respect to the distribution. The expected value of a function f(x) with respect to a distribution dist is defined as: ubound E[x] = Integral(f(x) * dist.pdf(x)) lbound Parameters: func : callable, optional Function for which integral is calculated. Takes only one argumen

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

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

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

statsmodels.sandbox.regression.gmm.GMMResults.jval static GMMResults.jval() [source]