statsmodels.stats.diagnostic.acorr_lm¶

statsmodels.stats.diagnostic.acorr_lm(resid, nlags=None, autolag='AIC', store=False, *, period=None, ddof=0, cov_type='nonrobust', cov_kwargs=None)[source]¶

Lagrange Multiplier tests for autocorrelation.

This is a generic Lagrange Multiplier test for autocorrelation. Returns Engle’s ARCH test if resid is the squared residual array. Breusch-Godfrey is a variation on this test with additional exogenous variables.

Parameters

residarray_like: Time series to test.
nlagsint, default None: Highest lag to use. The behavior of this parameter will change after 0.12.
autolag{str, None}, default “AIC”: If None, then a fixed number of lags given by maxlag is used. This parameter is deprecated and will be removed after 0.12. Searching for model specification cannot control test size.
storebool, default False: If true then the intermediate results are also returned.
periodint, default none: The period of a Seasonal time series. Used to compute the max lag for seasonal data which uses min(2*period, nobs // 5) if set. If None, then the default rule is used to set the number of lags. When set, must be >= 2.
ddofint, default 0: The number of degrees of freedom consumed by the model used to produce resid. The default value is 0.
cov_typestr, default “nonrobust”: Covariance type. The default is “nonrobust` which uses the classic OLS covariance estimator. Specify one of “HC0”, “HC1”, “HC2”, “HC3” to use White’s covariance estimator. All covariance types supported by OLS.fit are accepted.
cov_kwargsdict, default None: Dictionary of covariance options passed to OLS.fit. See OLS.fit for more details.

Returns

lmfloat: Lagrange multiplier test statistic.
lmpvalfloat: The p-value for Lagrange multiplier test.
fvalfloat: The f statistic of the F test, alternative version of the same test based on F test for the parameter restriction.
fpvalfloat: The pvalue of the F test.
res_storeResultsStore, optional: Intermediate results. Only returned if store=True.