# statsmodels.regression.recursive_ls.RecursiveLSResults.cusum_squares¶

RecursiveLSResults.cusum_squares()[source]

Cumulative sum of squares of standardized recursive residuals statistics

Returns: cusum_squares – An array of length nobs - k_exog holding the CUSUM of squares statistics. array_like

Notes

The CUSUM of squares statistic takes the form:

$s_t = \left ( \sum_{j=k+1}^t w_j^2 \right ) \Bigg / \left ( \sum_{j=k+1}^T w_j^2 \right )$

where $$w_j$$ is the recursive residual at time $$j$$.

Excludes the first k_exog datapoints.

References

 [*] Brown, R. L., J. Durbin, and J. M. Evans. 1975. “Techniques for Testing the Constancy of Regression Relationships over Time.” Journal of the Royal Statistical Society. Series B (Methodological) 37 (2): 149-92.