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.stats.sandwich_covariance.cov_cluster_2groups statsmodels.stats.sandwich_covariance.cov_cluster_2groups(results, group, group2=None, use_correction=True) [source] cluster robust covariance matrix for two groups/clusters Parameters: results : result instance result of a regression, uses results.model.exog and results.resid TODO: this should use wexog instead use_correction : bool If true (default), then the small sample correction factor is used. Returns: cov_both : ndarray

statsmodels.tsa.vector_ar.var_model.VAR.select_order VAR.select_order(maxlags=None, verbose=True) [source] Compute lag order selections based on each of the available information criteria Parameters: maxlags : int if None, defaults to 12 * (nobs/100.)**(1./4) verbose : bool, default True If True, print table of info criteria and selected orders Returns: selections : dict {info_crit -> selected_order}

statsmodels.regression.linear_model.OLS.predict OLS.predict(params, exog=None) Return linear predicted values from a design matrix. Parameters: params : array-like Parameters of a linear model exog : array-like, optional. Design / exogenous data. Model exog is used if None. Returns: An array of fitted values : Notes If the model has not yet been fit, params is not optional.

statsmodels.sandbox.tsa.fftarma.ArmaFft.spdroots ArmaFft.spdroots(w) [source] spectral density for frequency using polynomial roots builds two arrays (number of roots, number of frequencies)

statsmodels.sandbox.distributions.extras.NormExpan_gen.moment NormExpan_gen.moment(n, *args, **kwds) n?th order non-central moment of distribution. Parameters: n : int, n>=1 Order of moment. arg1, arg2, arg3,... : float The shape parameter(s) for the distribution (see docstring of the instance object for more information). kwds : keyword arguments, optional These can include ?loc? and ?scale?, as well as other keyword arguments relevant for a given distribution.

statsmodels.nonparametric.kde.KDEUnivariate.evaluate KDEUnivariate.evaluate(point) [source] Evaluate density at a single point. Parameters: point : float Point at which to evaluate the density.

statsmodels.sandbox.sysreg.Sem2SLS.fit Sem2SLS.fit() [source]

statsmodels.graphics.regressionplots.abline_plot statsmodels.graphics.regressionplots.abline_plot(intercept=None, slope=None, horiz=None, vert=None, model_results=None, ax=None, **kwargs) [source] Plots a line given an intercept and slope. intercept : float The intercept of the line slope : float The slope of the line horiz : float or array-like Data for horizontal lines on the y-axis vert : array-like Data for verterical lines on the x-axis model_results : statsmodels results insta

statsmodels.genmod.families.family.Family.deviance Family.deviance(endog, mu, scale=1.0) [source] Deviance of (endog,mu) pair. Deviance is usually defined as twice the loglikelihood ratio. Parameters: endog : array-like The endogenous response variable mu : array-like The inverse of the link function at the linear predicted values. scale : float, optional An optional scale argument Returns: Deviance : array The value of deviance function defined below. Notes Deviance is defined