statsmodels.stats.diagnostic.recursive_olsresiduals¶
-
statsmodels.stats.diagnostic.
recursive_olsresiduals
(olsresults, skip=None, lamda=0.0, alpha=0.95)[source]¶ calculate recursive ols with residuals and cusum test statistic
- Parameters
- olsresults
instance
of
RegressionResults
uses only endog and exog
- skip
int
orNone
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
- olsresults
- 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
- rresid
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.