statsmodels.tsa.ar_model.AR.information AR.information(params) [source] Not Implemented Yet

statsmodels.discrete.discrete_model.Logit.fit Logit.fit(start_params=None, method='newton', maxiter=35, full_output=1, disp=1, callback=None, **kwargs) [source] Fit the model using maximum likelihood. The rest of the docstring is from statsmodels.base.model.LikelihoodModel.fit Fit method for likelihood based models Parameters: start_params : array-like, optional Initial guess of the solution for the loglikelihood maximization. The default is an array of zeros. method : str, optional The

statsmodels.tsa.vector_ar.var_model.VARProcess.mean VARProcess.mean() [source] Mean of stable process Lutkepohl eq. 2.1.23

statsmodels.regression.quantile_regression.QuantRegResults class statsmodels.regression.quantile_regression.QuantRegResults(model, params, normalized_cov_params=None, scale=1.0, cov_type='nonrobust', cov_kwds=None, use_t=None) [source] Results instance for the QuantReg model Methods HC0_se() HC1_se() HC2_se() HC3_se() aic() bic() bse() centered_tss() compare_f_test(restricted) use F test to test whether restricted model is correct compare_lm_test(restricted[, demean, use_lr

statsmodels.regression.quantile_regression.QuantRegResults.compare_lm_test QuantRegResults.compare_lm_test(restricted, demean=True, use_lr=False) Use Lagrange Multiplier test to test whether restricted model is correct Parameters: restricted : Result instance The restricted model is assumed to be nested in the current model. The result instance of the restricted model is required to have two attributes, residual sum of squares, ssr, residual degrees of freedom, df_resid. demean : bool Fl

statsmodels.discrete.discrete_model.DiscreteResults.prsquared static DiscreteResults.prsquared() [source]

statsmodels.discrete.discrete_model.DiscreteResults.bse static DiscreteResults.bse()

statsmodels.stats.diagnostic.het_arch statsmodels.stats.diagnostic.het_arch(resid, maxlag=None, autolag=None, store=False, regresults=False, ddof=0) Engle?s Test for Autoregressive Conditional Heteroscedasticity (ARCH) Parameters: resid : ndarray, (nobs,) residuals from an estimation, or time series maxlag : int highest lag to use autolag : None or string If None, then a fixed number of lags given by maxlag is used. store : bool If true then the intermediate results are also returned

statsmodels.regression.quantile_regression.QuantRegResults.bic static QuantRegResults.bic() [source]

statsmodels.stats.sandwich_covariance.se_cov statsmodels.stats.sandwich_covariance.se_cov(cov) get standard deviation from covariance matrix just a shorthand function np.sqrt(np.diag(cov)) Parameters: cov : array_like, square covariance matrix Returns: std : ndarray standard deviation from diagonal of cov