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/ [[alternative HTML version deleted]] _______________________________________________ R-SIG-Finance@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-sig-finance -- Subscriber-posting only. If you want to post, subscribe first. -- Also note that this is not the r-help list where general R questions should go.