statsmodels.tsa.interp.denton.dentonm statsmodels.tsa.interp.denton.dentonm(indicator, benchmark, freq='aq', **kwargs) [source] Modified Denton?s method to convert low-frequency to high-frequency data. Uses proportionate first-differences as the penalty function. See notes. Parameters: indicator : A low-frequency indicator series. It is assumed that there are no pre-sample indicators. Ie., the first indicators line up with the first benchmark. benchmark : array-like The higher frequency

statsmodels.tsa.vector_ar.var_model.VARProcess.long_run_effects VARProcess.long_run_effects() [source] Compute long-run effect of unit impulse

statsmodels.regression.linear_model.GLSAR.iterative_fit GLSAR.iterative_fit(maxiter=3) [source] Perform an iterative two-stage procedure to estimate a GLS model. The model is assumed to have AR(p) errors, AR(p) parameters and regression coefficients are estimated iteratively. Parameters: maxiter : integer, optional the number of iterations

statsmodels.duration.hazard_regression.PHReg.robust_covariance PHReg.robust_covariance(params) [source] Returns a covariance matrix for the proportional hazards model regresion coefficient estimates that is robust to certain forms of model misspecification. Parameters: params : ndarray The parameter vector at which the covariance matrix is calculated. Returns: The robust covariance matrix as a square ndarray. : Notes This function uses the groups argument to determine groups within whi

statsmodels.robust.norms.HuberT.weights HuberT.weights(z) [source] Huber?s t weighting function for the IRLS algorithm The psi function scaled by z Parameters: z : array-like 1d array Returns: weights : array weights(z) = 1 for |z| <= t weights(z) = t/|z| for |z| > t

statsmodels.sandbox.regression.gmm.IVRegressionResults.normalized_cov_params IVRegressionResults.normalized_cov_params()

statsmodels.nonparametric.kernel_regression.KernelCensoredReg.fit KernelCensoredReg.fit(data_predict=None) [source] Returns the marginal effects at the data_predict points.

statsmodels.tsa.vector_ar.var_model.VAR.from_formula classmethod VAR.from_formula(formula, data, subset=None, *args, **kwargs) Create a Model from a formula and dataframe. Parameters: formula : str or generic Formula object The formula specifying the model data : array-like The data for the model. See Notes. subset : array-like An array-like object of booleans, integers, or index values that indicate the subset of df to use in the model. Assumes df is a pandas.DataFrame args : extra a

statsmodels.discrete.discrete_model.MultinomialResults.llr_pvalue static MultinomialResults.llr_pvalue()

statsmodels.sandbox.regression.gmm.IVRegressionResults.scale static IVRegressionResults.scale()