R help - Oct 2009 - R strucchange question: recursive-based CUSUM

Hello R users:

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())?

Thank you in advance for your help!

Julia

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