statsmodels.stats.sandwich_covariance.cov_nw_groupsum statsmodels.stats.sandwich_covariance.cov_nw_groupsum(results, nlags, time, weights_func=, use_correction=0) [source] Driscoll and Kraay Panel robust covariance matrix Robust covariance matrix for panel data of Driscoll and Kraay. Assumes we have a panel of time series where the time index is available. The time index is assumed to represent equal spaced periods. At least one observation per period is required. Parameters: results : resu

statsmodels.sandbox.regression.gmm.IV2SLS.initialize IV2SLS.initialize() [source]

statsmodels.regression.quantile_regression.QuantReg.hessian QuantReg.hessian(params) The Hessian matrix of the model

statsmodels.tsa.arima_model.ARIMAResults.bse static ARIMAResults.bse()

statsmodels.sandbox.distributions.extras.ACSkewT_gen.expect ACSkewT_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 argument.

statsmodels.regression.linear_model.OLSResults.llf static OLSResults.llf()

statsmodels.genmod.families.links.probit.inverse probit.inverse(z) The inverse of the CDF link Parameters: z : array-like The value of the inverse of the link function at p Returns: p : array Mean probabilities. The value of the inverse of CDF link of z Notes g^(-1)(z) = dbn.cdf(z)

statsmodels.nonparametric.kernel_regression.KernelReg.r_squared KernelReg.r_squared() [source] Returns the R-Squared for the nonparametric regression. Notes For more details see p.45 in [2] The R-Squared is calculated by: where is the mean calculated in fit at the exog points.

statsmodels.discrete.discrete_model.CountModel.fit_regularized CountModel.fit_regularized(start_params=None, method='l1', maxiter='defined_by_method', full_output=1, disp=1, callback=None, alpha=0, trim_mode='auto', auto_trim_tol=0.01, size_trim_tol=0.0001, qc_tol=0.03, **kwargs) [source] Fit the model using a regularized maximum likelihood. The regularization method AND the solver used is determined by the argument method. Parameters: start_params : array-like, optional Initial guess of t

statsmodels.discrete.discrete_model.MNLogit.loglike_and_score MNLogit.loglike_and_score(params) [source] Returns log likelihood and score, efficiently reusing calculations. Note that both of these returned quantities will need to be negated before being minimized by the maximum likelihood fitting machinery.