statsmodels.sandbox.regression.gmm.GMMResults.calc_cov_params GMMResults.calc_cov_params(moms, gradmoms, weights=None, use_weights=False, has_optimal_weights=True, weights_method='cov', wargs=()) [source] calculate covariance of parameter estimates not all options tried out yet If weights matrix is given, then the formula use to calculate cov_params depends on whether has_optimal_weights is true. If no weights are given, then the weight matrix is calculated with the given method, and has_opt

statsmodels.tsa.vector_ar.var_model.VARProcess.get_eq_index VARProcess.get_eq_index(name) [source] Return integer position of requested equation name

statsmodels.sandbox.distributions.transformed.Transf_gen.cdf Transf_gen.cdf(x, *args, **kwds) Cumulative distribution function of the given RV. Parameters: x : array_like quantiles arg1, arg2, arg3,... : array_like The shape parameter(s) for the distribution (see docstring of the instance object for more information) loc : array_like, optional location parameter (default=0) scale : array_like, optional scale parameter (default=1) Returns: cdf : ndarray Cumulative distribution fun

statsmodels.sandbox.distributions.transformed.Transf_gen.median Transf_gen.median(*args, **kwds) Median of the distribution. Parameters: arg1, arg2, arg3,... : array_like The shape parameter(s) for the distribution (see docstring of the instance object for more information) loc : array_like, optional Location parameter, Default is 0. scale : array_like, optional Scale parameter, Default is 1. Returns: median : float The median of the distribution. See also stats.distributions.r

statsmodels.stats.outliers_influence.OLSInfluence.cooks_distance static OLSInfluence.cooks_distance() [source] (cached attribute) Cooks distance uses original results, no nobs loop

statsmodels.duration.hazard_regression.PHRegResults.remove_data PHRegResults.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 an attrib

statsmodels.tsa.arima_model.ARIMAResults.forecast ARIMAResults.forecast(steps=1, exog=None, alpha=0.05) [source] Out-of-sample forecasts Parameters: steps : int The number of out of sample forecasts from the end of the sample. exog : array If the model is an ARIMAX, you must provide out of sample values for the exogenous variables. This should not include the constant. alpha : float The confidence intervals for the forecasts are (1 - alpha) % Returns: forecast : array Array of out

statsmodels.tsa.arima_model.ARIMAResults.f_test ARIMAResults.f_test(r_matrix, cov_p=None, scale=1.0, invcov=None) Compute the F-test for a joint linear hypothesis. This is a special case of wald_test that always uses the F distribution. 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 giv

statsmodels.nonparametric.kernel_density.KDEMultivariate class statsmodels.nonparametric.kernel_density.KDEMultivariate(data, var_type, bw=None, defaults=) [source] Multivariate kernel density estimator. This density estimator can handle univariate as well as multivariate data, including mixed continuous / ordered discrete / unordered discrete data. It also provides cross-validated bandwidth selection methods (least squares, maximum likelihood). Parameters: data: list of ndarrays or 2-D nda

statsmodels.robust.norms.HuberT.psi HuberT.psi(z) [source] The psi function for Huber?s t estimator The analytic derivative of rho Parameters: z : array-like 1d array Returns: psi : array psi(z) = z for |z| <= t psi(z) = sign(z)*t for |z| > t