statsmodels.miscmodels.count.PoissonGMLE.nloglike PoissonGMLE.nloglike(params)

statsmodels.tsa.filters.hp_filter.hpfilter statsmodels.tsa.filters.hp_filter.hpfilter(X, lamb=1600) [source] Hodrick-Prescott filter Parameters: X : array-like The 1d ndarray timeseries to filter of length (nobs,) or (nobs,1) lamb : float The Hodrick-Prescott smoothing parameter. A value of 1600 is suggested for quarterly data. Ravn and Uhlig suggest using a value of 6.25 (1600/4**4) for annual data and 129600 (1600*3**4) for monthly data. Returns: cycle : array The estimated cycle i

statsmodels.discrete.discrete_model.Logit.fit_regularized Logit.fit_regularized(start_params=None, method='l1', maxiter='defined_by_method', full_output=1, disp=1, callback=None, alpha=0, trim_mode='auto', auto_trim_tol=0.01, size_trim_tol=0.0001, qc_tol=0.03, **kwargs) Fit the model using a regularized maximum likelihood. The regularization method AND the solver used is determined by the argument method. Parameters: start_params : array-like, optional Initial guess of the solution for the

Robust Linear Models Link to Notebook GitHub In [1]: from __future__ import print_function import numpy as np import statsmodels.api as sm import matplotlib.pyplot as plt from statsmodels.sandbox.regression.predstd import wls_prediction_std Estimation Load data: In [2]: data = sm.datasets.stackloss.load() data.exog = sm.add_constant(data.exog) Huber's T norm with the (default) median absolute deviation scaling In [3]: huber_t = sm.RLM(data.endog, data.e

statsmodels.tsa.vector_ar.dynamic.DynamicVAR.resid static DynamicVAR.resid() [source]

statsmodels.sandbox.distributions.extras.NormExpan_gen.est_loc_scale NormExpan_gen.est_loc_scale(*args, **kwds) est_loc_scale is deprecated! This function is deprecated, use self.fit_loc_scale(data) instead.

statsmodels.regression.linear_model.RegressionResults.bic static RegressionResults.bic() [source]

statsmodels.discrete.discrete_model.DiscreteResults.t_test DiscreteResults.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 : A tup

statsmodels.discrete.discrete_model.MNLogit.fit_regularized MNLogit.fit_regularized(start_params=None, method='l1', maxiter='defined_by_method', full_output=1, disp=1, callback=None, alpha=0, trim_mode='auto', auto_trim_tol=0.01, size_trim_tol=0.0001, qc_tol=0.03, **kwargs) Fit the model using a regularized maximum likelihood. The regularization method AND the solver used is determined by the argument method. Parameters: start_params : array-like, optional Initial guess of the solution for

statsmodels.sandbox.stats.multicomp.catstack statsmodels.sandbox.stats.multicomp.catstack(args) [source]