Julia:
I'm trying now to apply the package strucchange to see whether there is a structural change in linear regression. I have noted the following problem that arises in my case with recursive-based CUSUM: generic function recresid() in efp() generates an error, since (probably) it cannot compute the inverse matrix of (X^(i-1)^T)*(X^(i-1)) at each step (i-1), because the matrix (X^(i-1)^T)*(X^(i-1)) does not have full rank for all i (X consists of dummy variables). Does any solution of this problem exist (for example, to replace the ordinary inverse by the generalised inverse, ginv())?
The 1-step-ahead prediction error is well-defined even if there are rank deficiencies. For example, using lm.fit() will automatically alias coefficients that are not identified. The reason why recresid() doesn't use this is that it employs a more efficient updating algorithm.
If you need to investigate the recursive CUSUM test, you could hack recresid() and use the slower but more robust implementation based on lm.fit().
Personally, however, I would recommend to use a different test. In most situations (unless the break occurs very early in the sample), there are more powerful methods than the recursive CUSUM test.
hth, Z ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
