statsmodels.stats.diagnostic.recursive_olsresiduals

statsmodels.stats.diagnostic.recursive_olsresiduals(olsresults, skip=None, lamda=0.0, alpha=0.95)

calculate recursive ols with residuals and cusum test statistic

Parameters:

olsresults : instance of RegressionResults

uses only endog and exog

skip : int or None

number of observations to use for initial OLS, if None then skip is set equal to the number of regressors (columns in exog)

lamda : float

weight for Ridge correction to initial (X’X)^{-1}

alpha : {0.95, 0.99}

confidence level of test, currently only two values supported, used for confidence interval in cusum graph

Returns:

rresid : array

recursive ols residuals

rparams : array

recursive ols parameter estimates

rypred : array

recursive prediction of endogenous variable

rresid_standardized : array

recursive residuals standardized so that N(0,sigma2) distributed, where sigma2 is the error variance

rresid_scaled : array

recursive residuals normalize so that N(0,1) distributed

rcusum : array

cumulative residuals for cusum test

rcusumci : array

confidence interval for cusum test, currently hard coded for alpha=0.95

Notes

It produces same recursive residuals as other version. This version updates the inverse of the X’X matrix and does not require matrix inversion during updating. looks efficient but no timing

Confidence interval in Greene and Brown, Durbin and Evans is the same as in Ploberger after a little bit of algebra.

References

jplv to check formulas, follows Harvey BigJudge 5.5.2b for formula for inverse(X’X) updating Greene section 7.5.2

Brown, R. L., J. Durbin, and J. M. Evans. “Techniques for Testing the Constancy of Regression Relationships over Time.” Journal of the Royal Statistical Society. Series B (Methodological) 37, no. 2 (1975): 149-192.