statsmodels.tsa.arima_model.ARIMAResults.summary ARIMAResults.summary(alpha=0.05) 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.regression.linear_model.OLS.initialize OLS.initialize()

statsmodels.tsa.ar_model.AR.score AR.score(params) [source] Return the gradient of the loglikelihood at params. Parameters: params : array-like The parameter values at which to evaluate the score function. Notes Returns numerical gradient.

statsmodels.tsa.vector_ar.var_model.VARResults.roots static VARResults.roots() [source]

statsmodels.sandbox.regression.gmm.IVRegressionResults.aic static IVRegressionResults.aic()

statsmodels.sandbox.regression.gmm.IVRegressionResults.fittedvalues static IVRegressionResults.fittedvalues()

statsmodels.regression.quantile_regression.QuantRegResults.nobs static QuantRegResults.nobs()

statsmodels.discrete.discrete_model.Poisson.hessian Poisson.hessian(params) [source] Poisson model Hessian matrix of the loglikelihood Parameters: params : array-like The parameters of the model Returns: hess : ndarray, (k_vars, k_vars) The Hessian, second derivative of loglikelihood function, evaluated at params Notes where the loglinear model is assumed

statsmodels.tsa.ar_model.ARResults.initialize ARResults.initialize(model, params, **kwd)

statsmodels.sandbox.tsa.fftarma.ArmaFft.filter2 ArmaFft.filter2(x, pad=0) [source] filter a time series using fftconvolve3 with ARMA filter padding of x currently works only if x is 1d in example it produces same observations at beginning as lfilter even without padding. TODO: this returns 1 additional observation at the end