statsmodels.stats.sandwich_covariance.cov_white_simple statsmodels.stats.sandwich_covariance.cov_white_simple(results, use_correction=True) [source] heteroscedasticity robust covariance matrix (White) Parameters: results : result instance result of a regression, uses results.model.exog and results.resid TODO: this should use wexog instead Returns: cov : ndarray, (k_vars, k_vars) heteroscedasticity robust covariance matrix for parameter estimates See also cov_hc1, cov_hc2, cov_hc3 N

statsmodels.distributions.empirical_distribution.ECDF class statsmodels.distributions.empirical_distribution.ECDF(x, side='right') [source] Return the Empirical CDF of an array as a step function. Parameters: x : array-like Observations side : {?left?, ?right?}, optional Default is ?right?. Defines the shape of the intervals constituting the steps. ?right? correspond to [a, b) intervals and ?left? to (a, b]. Returns: Empirical CDF as a step function. : Examples >>> import nu

statsmodels.genmod.generalized_linear_model.GLMResults.predict GLMResults.predict(exog=None, transform=True, *args, **kwargs) Call self.model.predict with self.params as the first argument. Parameters: exog : array-like, optional The values for which you want to predict. transform : bool, optional If the model was fit via a formula, do you want to pass exog through the formula. Default is True. E.g., if you fit a model y ~ log(x1) + log(x2), and transform is True, then you can pass a dat

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.distributions.extras.SkewNorm2_gen.logpdf SkewNorm2_gen.logpdf(x, *args, **kwds) Log of the probability density function at x of the given RV. This uses a more numerically accurate calculation if available. Parameters: x : array_like quantiles arg1, arg2, arg3,... : array_like The shape parameter(s) for the distribution (see docstring of the instance object for more information) loc : array_like, optional location parameter (default=0) scale : array_like, optional

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.vector_ar.irf.IRAnalysis.err_band_sz3 IRAnalysis.err_band_sz3(orth=False, repl=1000, signif=0.05, seed=None, burn=100, component=None) [source] IRF Sims-Zha error band method 3. Does not assume symmetric error bands around mean. Parameters: orth : bool, default False Compute orthogonalized impulse responses repl : int, default 1000 Number of MC replications signif : float (0 < signif < 1) Significance level for error bars, defaults to 95% CI seed : int, default N

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.sandbox.distributions.extras.ACSkewT_gen.freeze ACSkewT_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.graphics.gofplots.ProbPlot.ppplot ProbPlot.ppplot(xlabel=None, ylabel=None, line=None, other=None, ax=None, **plotkwargs) [source] P-P plot of the percentiles (probabilities) of x versus the probabilities (percetiles) of a distribution. Parameters: xlabel, ylabel : str or None, optional User-provided lables for the x-axis and y-axis. If None (default), other values are used depending on the status of the kwarg other. line : str {?45?, ?s?, ?r?, q?} or None, optional Options f