Revised 9 August 2012
This is a replication of Lanne and Lutkepohl(2008), "Identifying Monetary Policy Shocks via Changes in Volatility", JMCB, vol 40, no 6, 1131-1149 which identifies an SVAR using variance regimes. The authors use three variance regimes, only two of which use the structural model for the covariance matrix. The third (actually the first in order) is left to be freely estimated. It should be relatively easy to adapt to different numbers of variance regimes.
This has the actual data used. However, there was an error in the coding used in the paper. This is one of those errors which is a good example of why you shouldn't use Gauss (or Matlab) for time series; the data matrix shifted as a result of lags, but the break points didn't adapt. With RATS, the data stay put and the lags are extracted from them.
The second program replicates the "Chow test" results for breaks in the (non-structural) VAR. The published results were done with incorrect break points (off by 13 relative to the description).