statsmodels.stats.stattools.jarque_bera statsmodels.stats.stattools.jarque_bera(resids, axis=0) [source] Calculate residual skewness, kurtosis, and do the JB test for normality Parameters: resids : array-like axis : int, optional Default is 0 Returns: JB, JBpv, skew, kurtosis : JB = n/6*(S^2 + (K-3)^2/4) : JBpv is the Chi^2 two-tail probability value : skew is the measure of skewness : kurtosis is the measure of kurtosis :

statsmodels.stats.stattools.durbin_watson statsmodels.stats.stattools.durbin_watson(resids, axis=0) [source] Calculates the Durbin-Watson statistic Parameters: resids : array-like Returns: dw : float, array-like The Durbin-Watson statistic. : Notes The null hypothesis of the test is that there is no serial correlation. The Durbin-Watson test statistics is defined as: The test statistic is approximately equal to 2*(1-r) where r is the sample autocorrelation of the residuals. Thus, for

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

statsmodels.stats.sandwich_covariance.cov_white_simple statsmodels.stats.sandwich_covariance.cov_white_simple(results, use_correction=True) [source] heteroscedasticity robust covariance matrix (White) Parameters: results : result instance result of a regression, uses results.model.exog and results.resid TODO: this should use wexog instead Returns: cov : ndarray, (k_vars, k_vars) heteroscedasticity robust covariance matrix for parameter estimates See also cov_hc1, cov_hc2, cov_hc3 N

statsmodels.stats.sandwich_covariance.cov_nw_panel statsmodels.stats.sandwich_covariance.cov_nw_panel(results, nlags, groupidx, weights_func=, use_correction='hac') [source] Panel HAC robust covariance matrix Assumes we have a panel of time series with consecutive, equal spaced time periods. Data is assumed to be in long format with time series of each individual stacked into one array. Panel can be unbalanced. Parameters: results : result instance result of a regression, uses results.mode

statsmodels.stats.sandwich_covariance.cov_nw_groupsum statsmodels.stats.sandwich_covariance.cov_nw_groupsum(results, nlags, time, weights_func=, use_correction=0) [source] Driscoll and Kraay Panel robust covariance matrix Robust covariance matrix for panel data of Driscoll and Kraay. Assumes we have a panel of time series where the time index is available. The time index is assumed to represent equal spaced periods. At least one observation per period is required. Parameters: results : resu

statsmodels.stats.sandwich_covariance.cov_hc3 statsmodels.stats.sandwich_covariance.cov_hc3(results) [source] See statsmodels.RegressionResults

statsmodels.stats.sandwich_covariance.cov_hc2 statsmodels.stats.sandwich_covariance.cov_hc2(results) [source] See statsmodels.RegressionResults

statsmodels.stats.sandwich_covariance.cov_hc1 statsmodels.stats.sandwich_covariance.cov_hc1(results) [source] See statsmodels.RegressionResults

statsmodels.stats.sandwich_covariance.cov_hc0 statsmodels.stats.sandwich_covariance.cov_hc0(results) [source] See statsmodels.RegressionResults