statsmodels.tsa.arima_process.ArmaProcess class statsmodels.tsa.arima_process.ArmaProcess(ar, ma, nobs=100) [source] Represent an ARMA process for given lag-polynomials This is a class to bring together properties of the process. It does not do any estimation or statistical analysis. Parameters: ar : array_like, 1d Coefficient for autoregressive lag polynomial, including zero lag. See the notes for some information about the sign. ma : array_like, 1d Coefficient for moving-average lag po

statsmodels.stats.diagnostic.breaks_cusumolsresid statsmodels.stats.diagnostic.breaks_cusumolsresid(olsresidual, ddof=0) cusum test for parameter stability based on ols residuals Parameters: olsresiduals : ndarray array of residuals from an OLS estimation ddof : int number of parameters in the OLS estimation, used as degrees of freedom correction for error variance. Returns: sup_b : float test statistic, maximum of absolute value of scaled cumulative OLS residuals pval : float Prob

statsmodels.regression.linear_model.GLSAR.hessian GLSAR.hessian(params) The Hessian matrix of the model

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

statsmodels.discrete.discrete_model.CountResults.save CountResults.save(fname, remove_data=False) save a pickle of this instance Parameters: fname : string or filehandle fname can be a string to a file path or filename, or a filehandle. remove_data : bool If False (default), then the instance is pickled without changes. If True, then all arrays with length nobs are set to None before pickling. See the remove_data method. In some cases not all arrays will be set to None. Notes If remove

statsmodels.tsa.x13.x13_arima_select_order statsmodels.tsa.x13.x13_arima_select_order(endog, maxorder=(2, 1), maxdiff=(2, 1), diff=None, exog=None, log=None, outlier=True, trading=False, forecast_years=None, start=None, freq=None, print_stdout=False, x12path=None, prefer_x13=True) [source] Perform automatic seaonal ARIMA order identification using x12/x13 ARIMA. Parameters: endog : array-like, pandas.Series The series to model. It is best to use a pandas object with a DatetimeIndex or Peri

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.genmod.families.family.Poisson.predict Poisson.predict(mu) Linear predictors based on given mu values. Parameters: mu : array The mean response variables Returns: lin_pred : array Linear predictors based on the mean response variables. The value of the link function at the given mu.

statsmodels.discrete.discrete_model.Probit.cov_params_func_l1 Probit.cov_params_func_l1(likelihood_model, xopt, retvals) Computes cov_params on a reduced parameter space corresponding to the nonzero parameters resulting from the l1 regularized fit. Returns a full cov_params matrix, with entries corresponding to zero?d values set to np.nan.

statsmodels.regression.linear_model.RegressionResults.fittedvalues static RegressionResults.fittedvalues() [source]