statsmodels.stats.diagnostic.HetGoldfeldQuandt class statsmodels.stats.diagnostic.HetGoldfeldQuandt test whether variance is the same in 2 subsamples Parameters: y : array_like endogenous variable x : array_like exogenous variable, regressors idx : integer column index of variable according to which observations are sorted for the split split : None or integer or float in intervall (0,1) index at which sample is split. If 0<split<1 then split is interpreted as fraction of the o

statsmodels.stats.diagnostic.compare_j statsmodels.stats.diagnostic.compare_j = J-Test for comparing non-nested models Parameters: results_x : Result instance result instance of first model results_z : Result instance result instance of second model attach : bool From description in Greene, section 8.3.3 : produces correct results for Example 8.3, Greene - not checked yet : #currently an exception, but I don?t have clean reload in python session : check what results should be attached

statsmodels.stats.diagnostic.compare_cox statsmodels.stats.diagnostic.compare_cox = Cox Test for non-nested models Parameters: results_x : Result instance result instance of first model results_z : Result instance result instance of second model attach : bool Formulas from Greene, section 8.3.4 translated to code : produces correct results for Example 8.3, Greene :

statsmodels.stats.diagnostic.CompareJ class statsmodels.stats.diagnostic.CompareJ J-Test for comparing non-nested models Parameters: results_x : Result instance result instance of first model results_z : Result instance result instance of second model attach : bool From description in Greene, section 8.3.3 : produces correct results for Example 8.3, Greene - not checked yet : #currently an exception, but I don?t have clean reload in python session : check what results should be attached

statsmodels.stats.diagnostic.CompareCox class statsmodels.stats.diagnostic.CompareCox Cox Test for non-nested models Parameters: results_x : Result instance result instance of first model results_z : Result instance result instance of second model attach : bool Formulas from Greene, section 8.3.4 translated to code : produces correct results for Example 8.3, Greene : Methods run(results_x, results_z[, attach]) run Cox test for non-nested models

statsmodels.stats.diagnostic.breaks_hansen statsmodels.stats.diagnostic.breaks_hansen(olsresults) test for model stability, breaks in parameters for ols, Hansen 1992 Parameters: olsresults : instance of RegressionResults uses only endog and exog Returns: teststat : float Hansen?s test statistic crit : structured array critical values at alpha=0.95 for different nvars pvalue Not yet : ft, s : arrays temporary return for debugging, will be removed Notes looks good in example, maybe

statsmodels.stats.diagnostic.breaks_cusumolsresid statsmodels.stats.diagnostic.breaks_cusumolsresid(olsresidual, ddof=0) cusum test for parameter stability based on ols residuals Parameters: olsresiduals : ndarray array of residuals from an OLS estimation ddof : int number of parameters in the OLS estimation, used as degrees of freedom correction for error variance. Returns: sup_b : float test statistic, maximum of absolute value of scaled cumulative OLS residuals pval : float Prob

statsmodels.stats.diagnostic.acorr_ljungbox statsmodels.stats.diagnostic.acorr_ljungbox(x, lags=None, boxpierce=False) Ljung-Box test for no autocorrelation Parameters: x : array_like, 1d data series, regression residuals when used as diagnostic test lags : None, int or array_like If lags is an integer then this is taken to be the largest lag that is included, the test result is reported for all smaller lag length. If lags is a list or array, then all lags are included up to the largest

statsmodels.stats.diagnostic.acorr_breush_godfrey statsmodels.stats.diagnostic.acorr_breush_godfrey(results, nlags=None, store=False) Breush Godfrey Lagrange Multiplier tests for residual autocorrelation Parameters: results : Result instance Estimation results for which the residuals are tested for serial correlation nlags : int Number of lags to include in the auxiliary regression. (nlags is highest lag) store : bool If store is true, then an additional class instance that contains in

statsmodels.stats.descriptivestats.sign_test statsmodels.stats.descriptivestats.sign_test(samp, mu0=0) [source] Signs test. Parameters: samp : array-like 1d array. The sample for which you want to perform the signs test. mu0 : float See Notes for the definition of the sign test. mu0 is 0 by default, but it is common to set it to the median. Returns: M, p-value : See also scipy.stats.wilcoxon Notes The signs test returns M = (N(+) - N(-))/2 where N(+) is the number of values above m