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RecursiveLSResults.cusum_squares()

statsmodels.regression.recursive_ls.RecursiveLSResults.cusum_squares

RecursiveLSResults.cusum_squares() [source]

Cumulative sum of squares of standardized recursive residuals statistics

Returns:

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

cusum_squares : array_like

An array of length nobs - k_exog holding the CUSUM of squares statistics.

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

[R44]

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.

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© 2006 Jonathan E. Taylor
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http://www.statsmodels.org/stable/generated/statsmodels.regression.recursive_ls.RecursiveLSResults.cusum_squares.html