statsmodels.tsa.arima_model.ARMAResults.plot_predict ARMAResults.plot_predict(start=None, end=None, exog=None, dynamic=False, alpha=0.05, plot_insample=True, ax=None) [source] Plot forecasts Parameters: start : int, str, or datetime Zero-indexed observation number at which to start forecasting, ie., the first forecast is start. Can also be a date string to parse or a datetime type. end : int, str, or datetime Zero-indexed observation number at which to end forecasting, ie., the first for

statsmodels.stats.weightstats._zstat_generic statsmodels.stats.weightstats._zstat_generic(value1, value2, std_diff, alternative, diff=0) [source] generic (normal) z-test to save typing can be used as ztest based on summary statistics

statsmodels.tsa.vector_ar.var_model.VARResults.acorr VARResults.acorr(nlags=None) Compute theoretical autocorrelation function Returns: acorr : ndarray (p x k x k)

statsmodels.tsa.arima_model.ARIMAResults.bic static ARIMAResults.bic()

statsmodels.discrete.discrete_model.LogitResults class statsmodels.discrete.discrete_model.LogitResults(model, mlefit, cov_type='nonrobust', cov_kwds=None, use_t=None) [source] A results class for Logit Model Parameters: model : A DiscreteModel instance params : array-like The parameters of a fitted model. hessian : array-like The hessian of the fitted model. scale : float A scale parameter for the covariance matrix. Returns: *Attributes* : aic : float Akaike information criterion.

statsmodels.sandbox.stats.multicomp.catstack statsmodels.sandbox.stats.multicomp.catstack(args) [source]

statsmodels.tsa.ar_model.ARResults.hqic static ARResults.hqic() [source]

statsmodels.duration.hazard_regression.PHRegResults.initialize PHRegResults.initialize(model, params, **kwd)

statsmodels.sandbox.regression.gmm.NonlinearIVGMM.fit NonlinearIVGMM.fit(start_params=None, maxiter=10, inv_weights=None, weights_method='cov', wargs=(), has_optimal_weights=True, optim_method='bfgs', optim_args=None) Estimate parameters using GMM and return GMMResults TODO: weight and covariance arguments still need to be made consistent with similar options in other models, see RegressionResult.get_robustcov_results Parameters: start_params : array (optional) starting value for parameter

statsmodels.tsa.ar_model.ARResults class statsmodels.tsa.ar_model.ARResults(model, params, normalized_cov_params=None, scale=1.0) [source] Class to hold results from fitting an AR model. Parameters: model : AR Model instance Reference to the model that is fit. params : array The fitted parameters from the AR Model. normalized_cov_params : array inv(dot(X.T,X)) where X is the lagged values. scale : float, optional An estimate of the scale of the model. Returns: **Attributes** : aic