Generalized Method of Moments gmm statsmodels.gmm contains model classes and functions that are based on estimation with Generalized Method of Moments. Currently the general non-linear case is implemented. An example class for the standard linear instrumental variable model is included. This has been introduced as a test case, it works correctly but it does not take the linear structure into account. For the linear case we intend to introduce a specific implementation which will be faster and n

statsmodels.graphics.gofplots.qqplot_2samples statsmodels.graphics.gofplots.qqplot_2samples(data1, data2, xlabel=None, ylabel=None, line=None, ax=None) [source] Q-Q Plot of two samples? quantiles. Can take either two ProbPlot instances or two array-like objects. In the case of the latter, both inputs will be converted to ProbPlot instances using only the default values - so use ProbPlot instances if finer-grained control of the quantile computations is required. Parameters: data1, data2 : a

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

statsmodels.genmod.generalized_linear_model.GLMResults.bic static GLMResults.bic() [source]

statsmodels.miscmodels.count.PoissonZiGMLE.loglikeobs PoissonZiGMLE.loglikeobs(params)

statsmodels.tsa.arima_model.ARMAResults.mafreq static ARMAResults.mafreq() [source] Returns the frequency of the MA roots. This is the solution, x, to z = abs(z)*exp(2j*np.pi*x) where z are the roots.

statsmodels.stats.outliers_influence.OLSInfluence.det_cov_params_not_obsi static OLSInfluence.det_cov_params_not_obsi() [source] (cached attribute) determinant of cov_params of all LOOO regressions uses results from leave-one-observation-out loop

statsmodels.tsa.vector_ar.dynamic.DynamicVAR.forecast DynamicVAR.forecast(steps=1) [source] Produce dynamic forecast Parameters: steps : Returns: forecasts : pandas.DataFrame

statsmodels.stats.outliers_influence.OLSInfluence.dfbetas static OLSInfluence.dfbetas() [source] (cached attribute) dfbetas uses results from leave-one-observation-out loop

statsmodels.regression.linear_model.OLSResults.HC3_se static OLSResults.HC3_se() See statsmodels.RegressionResults