statsmodels.tsa.vector_ar.var_model.VARResults.cov_params static VARResults.cov_params() [source] Estimated variance-covariance of model coefficients Notes Covariance of vec(B), where B is the matrix [intercept, A_1, ..., A_p] (K x (Kp + 1)) Adjusted to be an unbiased estimator Ref: Lutkepohl p.74-75

statsmodels.sandbox.regression.gmm.IVRegressionResults.predict IVRegressionResults.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 p

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.tsa.filters.filtertools.convolution_filter statsmodels.tsa.filters.filtertools.convolution_filter(x, filt, nsides=2) [source] Linear filtering via convolution. Centered and backward displaced moving weighted average. Parameters: x : array_like data array, 1d or 2d, if 2d then observations in rows filt : array_like Linear filter coefficients in reverse time-order. Should have the same number of dimensions as x though if 1d and x is 2d will be coerced to 2d. nsides : int, opti

statsmodels.tsa.varma_process.VarmaPoly.getisstationary VarmaPoly.getisstationary(a=None) [source] check whether the auto-regressive lag-polynomial is stationary Returns: isstationary : boolean *attaches* : areigenvalues : complex array eigenvalues sorted by absolute value References formula taken from NAG manual

statsmodels.graphics.regressionplots.influence_plot statsmodels.graphics.regressionplots.influence_plot(results, external=True, alpha=0.05, criterion='cooks', size=48, plot_alpha=0.75, ax=None, **kwargs) [source] Plot of influence in regression. Plots studentized resids vs. leverage. Parameters: results : results instance A fitted model. external : bool Whether to use externally or internally studentized residuals. It is recommended to leave external as True. alpha : float The alpha va

statsmodels.sandbox.distributions.extras.NormExpan_gen.stats NormExpan_gen.stats(*args, **kwds) Some statistics of the given RV Parameters: 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 (discrete RVs only) scale parameter (default=1) moments : str, optional composed of letters [?mvsk?] defining which moments

statsmodels.discrete.discrete_model.Probit.score_obs Probit.score_obs(params) [source] Probit model Jacobian 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 Where . This simplification comes from the fact that the normal distribution is symmetric.

statsmodels.tsa.arima_model.ARMAResults.summary ARMAResults.summary(alpha=0.05) [source] Summarize the Model Parameters: alpha : float, optional Significance level for the confidence intervals. Returns: smry : Summary instance This holds the summary table and text, which can be printed or converted to various output formats. See also statsmodels.iolib.summary.Summary

statsmodels.discrete.discrete_model.NegativeBinomialResults.conf_int NegativeBinomialResults.conf_int(alpha=0.05, cols=None, method='default') Returns the confidence interval of the fitted parameters. Parameters: alpha : float, optional The significance level for the confidence interval. ie., The default alpha = .05 returns a 95% confidence interval. cols : array-like, optional cols specifies which confidence intervals to return method : string Not Implemented Yet Method to estimate th