Am 02.07.2014 10:06, schrieb Deborah Sy:
Yes, I'll be reading onto that. Another question I have is that I've
been using a 9x9 matrix with a lot of constraints (here, it is 36) which
is not random (i.e. they were backed up by theoretical assumptions). In
such case, given any possible combinations of the 0's, the determinant
will most likely be zero and therefore, no solution will exist (unless
it is a Cholesky-decomposition or if there really exists a combination
that provides a solution in this relatively large matrix). Does it
follow that I will not be able to estimate the SVAR because no such
solution exists? This has been bugging me for days already. Any ideas?
Ok, so 36 constraints in a 9x9 matrix sounds like it's just-identified
(at best). I guess you could check a rank deficiency of the template
matrix numerically by first substituting out the non-restricted elements
with ones, then multiply it (elementwise) with a random matrix (and then
use the rank() function).
Of course, using Jack's identification check algorithm which he will
publish soon apparently will be much better...