statsmodels.tsa.vector_ar.var_model.VARResults.mse VARResults.mse(steps) Compute theoretical forecast error variance matrices Parameters: steps : int Number of steps ahead Returns: forc_covs : ndarray (steps x neqs x neqs) Notes

statsmodels.miscmodels.count.PoissonGMLE class statsmodels.miscmodels.count.PoissonGMLE(endog, exog=None, loglike=None, score=None, hessian=None, missing='none', extra_params_names=None, **kwds) [source] Maximum Likelihood Estimation of Poisson Model This is an example for generic MLE which has the same statistical model as discretemod.Poisson. Except for defining the negative log-likelihood method, all methods and results are generic. Gradients and Hessian and all resulting statistics are b

statsmodels.sandbox.regression.gmm.NonlinearIVGMM.calc_weightmatrix NonlinearIVGMM.calc_weightmatrix(moms, weights_method='cov', wargs=(), params=None) calculate omega or the weighting matrix Parameters: moms : array, (nobs, nmoms) moment conditions for all observations evaluated at a parameter value weights_method : string ?cov? If method=?cov? is cov then the matrix is calculated as simple covariance of the moment conditions. see fit method for available aoptions for the weight and cov

statsmodels.sandbox.regression.gmm.LinearIVGMM.gmmobjective LinearIVGMM.gmmobjective(params, weights) objective function for GMM minimization Parameters: params : array parameter values at which objective is evaluated weights : array weighting matrix Returns: jval : float value of objective function

statsmodels.sandbox.regression.gmm.IVGMM.fitgmm IVGMM.fitgmm(start, weights=None, optim_method='bfgs', optim_args=None) estimate parameters using GMM Parameters: start : array_like starting values for minimization weights : array weighting matrix for moment conditions. If weights is None, then the identity matrix is used Returns: paramest : array estimated parameters Notes todo: add fixed parameter option, not here ??? uses scipy.optimize.fmin

statsmodels.tsa.arima_model.ARIMAResults.f_test ARIMAResults.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 can be giv

statsmodels.sandbox.regression.gmm.GMMResults.calc_cov_params GMMResults.calc_cov_params(moms, gradmoms, weights=None, use_weights=False, has_optimal_weights=True, weights_method='cov', wargs=()) [source] calculate covariance of parameter estimates not all options tried out yet If weights matrix is given, then the formula use to calculate cov_params depends on whether has_optimal_weights is true. If no weights are given, then the weight matrix is calculated with the given method, and has_opt

statsmodels.stats.diagnostic.compare_cox statsmodels.stats.diagnostic.compare_cox = Cox Test for non-nested models Parameters: results_x : Result instance result instance of first model results_z : Result instance result instance of second model attach : bool Formulas from Greene, section 8.3.4 translated to code : produces correct results for Example 8.3, Greene :

statsmodels.genmod.families.links.Power.inverse_deriv Power.inverse_deriv(z) [source] Derivative of the inverse of the power transform Parameters: z : array-like z is usually the linear predictor for a GLM or GEE model. Returns: The value of the derivative of the inverse of the power transform : function :

statsmodels.stats.power.FTestAnovaPower.power FTestAnovaPower.power(effect_size, nobs, alpha, k_groups=2) [source] Calculate the power of a F-test for one factor ANOVA. Parameters: effect_size : float standardized effect size, mean divided by the standard deviation. effect size has to be positive. nobs : int or float sample size, number of observations. alpha : float in interval (0,1) significance level, e.g. 0.05, is the probability of a type I error, that is wrong rejections if the N