statsmodels.tsa.ar_model.ARResults.bse static ARResults.bse() [source]

statsmodels.genmod.generalized_estimating_equations.GEEResults.f_test GEEResults.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 hypothese

statsmodels.discrete.discrete_model.BinaryResults.llf static BinaryResults.llf()

statsmodels.tsa.vector_ar.var_model.VARProcess.forecast VARProcess.forecast(y, steps) [source] 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.sandbox.regression.gmm.LinearIVGMM.fitgmm LinearIVGMM.fitgmm(start, weights=None, optim_method=None, **kwds) [source] estimate parameters using GMM for linear model Uses closed form expression instead of nonlinear optimizers Parameters: start : not used starting values for minimization, not used, only for consistency of method signature weights : array weighting matrix for moment conditions. If weights is None, then the identity matrix is used optim_method : not used, optim

statsmodels.discrete.discrete_model.Logit.score_obs Logit.score_obs(params) [source] Logit model Jacobian of the log-likelihood for each observation Parameters: params: array-like : The parameters of the model Returns: jac : ndarray, (nobs, k_vars) The derivative of the loglikelihood for each observation evaluated at params. Notes for observations

statsmodels.graphics.gofplots.ProbPlot.theoretical_quantiles static ProbPlot.theoretical_quantiles() [source]

Generalized Estimating Equations Generalized Estimating Equations estimate generalized linear models for panel, cluster or repeated measures data when the observations are possibly correlated withing a cluster but uncorrelated across clusters. It supports estimation of the same one-parameter exponential families as Generalized Linear models (GLM). See Module Reference for commands and arguments. Examples The following illustrates a Poisson regression with exchangeable correlation within cluste

statsmodels.tsa.vector_ar.var_model.VAR.score VAR.score(params) Score vector of model. The gradient of logL with respect to each parameter.

statsmodels.regression.linear_model.GLSAR.initialize GLSAR.initialize()