statsmodels.stats.diagnostic.acorr_lm#

statsmodels.stats.diagnostic.acorr_lm(resid, nlags=None, store=False, *, period=None, ddof=0, cov_type='nonrobust', cov_kwds=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.
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_kwdsdict, 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.