3.11.8. statsmodels.stats.diagnostic

3.11.8.1. Functions

acorr_breush_godfrey(results[, nlags, store]) Breush Godfrey Lagrange Multiplier tests for residual autocorrelation
acorr_ljungbox(x[, lags, boxpierce]) Ljung-Box test for no autocorrelation
breaks_cusumolsresid(olsresidual[, ddof]) cusum test for parameter stability based on ols residuals
breaks_hansen(olsresults) test for model stability, breaks in parameters for ols, Hansen 1992
het_arch(resid[, maxlag, autolag, store, ...]) Engle’s Test for Autoregressive Conditional Heteroscedasticity (ARCH)
het_breushpagan(resid, exog_het) Breush-Pagan Lagrange Multiplier test for heteroscedasticity
het_white(resid, exog[, retres]) White’s Lagrange Multiplier Test for Heteroscedasticity
kstest_normal(x[, pvalmethod]) Lillifors test for normality,
lillifors(x[, pvalmethod]) Lillifors test for normality,
linear_harvey_collier(res) Harvey Collier test for linearity
linear_lm(resid, exog[, func]) Lagrange multiplier test for linearity against functional alternative
linear_rainbow(res[, frac]) Rainbow test for linearity
normal_ad(x[, axis]) Anderson-Darling test for normal distribution unknown mean and variance
recursive_olsresiduals(olsresults[, skip, ...]) calculate recursive ols with residuals and cusum test statistic
unitroot_adf(x[, maxlag, trendorder, ...])

3.11.8.2. Classes

CompareCox Cox Test for non-nested models
CompareJ J-Test for comparing non-nested models
HetGoldfeldQuandt test whether variance is the same in 2 subsamples