statsmodels.sandbox.regression.gmm.IVGMMResults.wald_test IVGMMResults.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. tuple : A tup

statsmodels.regression.linear_model.GLSAR class statsmodels.regression.linear_model.GLSAR(endog, exog=None, rho=1, missing='none', **kwargs) [source] A regression model with an AR(p) covariance structure. Parameters: endog : array-like 1-d endogenous response variable. The dependent variable. exog : array-like A nobs x k array where nobs is the number of observations and k is the number of regressors. An intercept is not included by default and should be added by the user. See statsmodel

statsmodels.graphics.functional.fboxplot statsmodels.graphics.functional.fboxplot(data, xdata=None, labels=None, depth=None, method='MBD', wfactor=1.5, ax=None, plot_opts={}) [source] Plot functional boxplot. A functional boxplot is the analog of a boxplot for functional data. Functional data is any type of data that varies over a continuum, i.e. curves, probabillity distributions, seasonal data, etc. The data is first ordered, the order statistic used here is banddepth. Plotted are then the

statsmodels.sandbox.regression.gmm.NonlinearIVGMM.start_weights NonlinearIVGMM.start_weights(inv=True)

statsmodels.tsa.arima_model.ARIMAResults.plot_predict ARIMAResults.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 f

statsmodels.sandbox.regression.gmm.IVRegressionResults.cov_HC0 static IVRegressionResults.cov_HC0() See statsmodels.RegressionResults

statsmodels.tsa.varma_process.VarmaPoly.hstackarma_minus1 VarmaPoly.hstackarma_minus1() [source] stack ar and lagpolynomial vertically in 2d array this is the Kalman Filter representation, I think

statsmodels.discrete.discrete_model.BinaryResults class statsmodels.discrete.discrete_model.BinaryResults(model, mlefit, cov_type='nonrobust', cov_kwds=None, use_t=None) [source] A results class for binary data 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 criterio

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

statsmodels.miscmodels.count.PoissonZiGMLE.initialize PoissonZiGMLE.initialize()