statsmodels.discrete.discrete_model.LogitResults.resid_response static LogitResults.resid_response() The response residuals Notes Response residuals are defined to be where .

statsmodels.tsa.vector_ar.var_model.VARResults.forecast VARResults.forecast(y, steps) Produce linear minimum MSE forecasts for desired number of steps ahead, using prior values y Parameters: y : ndarray (p x k) steps : int Returns: forecasts : ndarray (steps x neqs) Notes Lutkepohl pp 37-38

statsmodels.tools.eval_measures.medianbias statsmodels.tools.eval_measures.medianbias(x1, x2, axis=0) [source] median bias, median error Parameters: x1, x2 : array_like The performance measure depends on the difference between these two arrays. axis : int axis along which the summary statistic is calculated Returns: medianbias : ndarray or float median bias, or median difference along given axis. Notes If x1 and x2 have different shapes, then they need to broadcast. This uses numpy

statsmodels.sandbox.distributions.transformed.LogTransf_gen.freeze LogTransf_gen.freeze(*args, **kwds) Freeze the distribution for the given arguments. Parameters: arg1, arg2, arg3,... : array_like The shape parameter(s) for the distribution. Should include all the non-optional arguments, may include loc and scale. Returns: rv_frozen : rv_frozen instance The frozen distribution.

statsmodels.tsa.arima_model.ARMAResults.conf_int ARMAResults.conf_int(alpha=0.05, cols=None, method='default') Returns the confidence interval of the fitted parameters. Parameters: alpha : float, optional The significance level for the confidence interval. ie., The default alpha = .05 returns a 95% confidence interval. cols : array-like, optional cols specifies which confidence intervals to return method : string Not Implemented Yet Method to estimate the confidence_interval. ?Default?

statsmodels.sandbox.distributions.extras.SkewNorm2_gen.freeze SkewNorm2_gen.freeze(*args, **kwds) Freeze the distribution for the given arguments. Parameters: arg1, arg2, arg3,... : array_like The shape parameter(s) for the distribution. Should include all the non-optional arguments, may include loc and scale. Returns: rv_frozen : rv_frozen instance The frozen distribution.

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

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.discrete.discrete_model.MultinomialResults.get_margeff MultinomialResults.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 each regr

statsmodels.tsa.varma_process.VarmaPoly.getisstationary VarmaPoly.getisstationary(a=None) [source] check whether the auto-regressive lag-polynomial is stationary Returns: isstationary : boolean *attaches* : areigenvalues : complex array eigenvalues sorted by absolute value References formula taken from NAG manual