Dear list,
I've implemented the time-varying cointegration framework by Bierens and
Martins (2010) in R [1], based on the gauss implementation of Luis
Martins [2]. I do get the same results as in gauss, when using lower
chebyshev dimensions, but when the number of dimensions is increasing, I
run into issues with complex eigenvalues and eigenvectors.
Additionally the eigenvalues and eigenvectors differ quite a bit between
gauss and R and I do not really know how to find out why that is. I also
experimented with different implementations of eigenvalues
determination, based on the C++ routine eigen, but was not able to
replicate the gauss results exactly.
One notable difference is that gauss does not normalize the
eigenvectors, but even after considering this a discrepancy remains.
Perhaps someone with a better knowledge of gauss may shed some light on
possible sources for these differences.
[1] https://github.com/hannes101/TimeVaryingCointegration
[2] http://home.iscte-iul.pt/~lfsm/