statsmodels.tsa.kalmanf.kalmanfilter.KalmanFilter.loglike classmethod KalmanFilter.loglike(params, arma_model, set_sigma2=True) [source] The loglikelihood for an ARMA model using the Kalman Filter recursions. Parameters: params : array The coefficients of the ARMA model, assumed to be in the order of trend variables and k exogenous coefficients, the p AR coefficients, then the q MA coefficients. arma_model : statsmodels.tsa.arima.ARMA instance A reference to the ARMA model instance. set

statsmodels.tsa.kalmanf.kalmanfilter.KalmanFilter.geterrors classmethod KalmanFilter.geterrors(y, k, k_ar, k_ma, k_lags, nobs, Z_mat, m, R_mat, T_mat, paramsdtype) [source] Returns just the errors of the Kalman Filter

statsmodels.sandbox.regression.gmm.IVRegressionResults.wald_test IVRegressionResults.wald_test(r_matrix, cov_p=None, scale=1.0, invcov=None, use_f=None) Compute a Wald-test for a joint linear hypothesis. Parameters: r_matrix : array-like, str, or tuple array : An r x k array where r is the number of restrictions to test and k is the number of regressors. It is assumed that the linear combination is equal to zero. str : The full hypotheses to test can be given as a string. See the examples.

statsmodels.sandbox.regression.gmm.IVRegressionResults.t_test IVRegressionResults.t_test(r_matrix, cov_p=None, scale=None, use_t=None) Compute a t-test for a each linear hypothesis of the form Rb = q Parameters: r_matrix : array-like, str, tuple array : If an array is given, a p x k 2d array or length k 1d array specifying the linear restrictions. It is assumed that the linear combination is equal to zero. str : The full hypotheses to test can be given as a string. See the examples. tuple

statsmodels.sandbox.regression.gmm.IVRegressionResults.summary2 IVRegressionResults.summary2(yname=None, xname=None, title=None, alpha=0.05, float_format='%.4f') Experimental summary function to summarize the regression results Parameters: xname : List of strings of length equal to the number of parameters Names of the independent variables (optional) yname : string Name of the dependent variable (optional) title : string, optional Title for the top table. If not None, then this replac

statsmodels.sandbox.regression.gmm.IVRegressionResults.summary IVRegressionResults.summary(yname=None, xname=None, title=None, alpha=0.05) [source] Summarize the Regression Results Parameters: yname : string, optional Default is y xname : list of strings, optional Default is var_## for ## in p the number of regressors title : string, optional Title for the top table. If not None, then this replaces the default title alpha : float significance level for the confidence intervals Retu

statsmodels.sandbox.regression.gmm.IVRegressionResults.spec_hausman IVRegressionResults.spec_hausman(dof=None) [source] Hausman?s specification test See also spec_hausman generic function for Hausman?s specification test

statsmodels.sandbox.regression.gmm.IVRegressionResults.save IVRegressionResults.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. No

statsmodels.sandbox.regression.gmm.IVRegressionResults.remove_data IVRegressionResults.remove_data() remove data arrays, all nobs arrays from result and model This reduces the size of the instance, so it can be pickled with less memory. Currently tested for use with predict from an unpickled results and model instance. Warning Since data and some intermediate results have been removed calculating new statistics that require them will raise exceptions. The exception will occur the first time

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