I am familiar with state space models, but I recently came across a problem. My model converges and gives sensible parameters, but it seems that one state estimate is only driven by another one. To explain, I aim at estimating a common trend from a few countries time-series ( lets say inflation). If you look at the smoothed state estimates below, you see that the global factor is (nearly) only explained by one country : Italy.
Italian idiosyncratic term has nearly no variance anymore, it feels like all variance in the Italy timeseries has been passed on to the common trend, which is odd since I used the same specification for the 3 countries, as you can see in the following code:
Code: Select all
@signal ca= c(3) +c(4)*sv1 + err_ca
@signal fr= c(7) + c(8)*sv1 +err_fr
@signal it= c(11) + c(12)*sv1 +err_it
@state sv1= log(c(19))*sv1(-1) + [var=1]
@state err_ca= log(c(22))*err_ca(-1) + [var = exp(c(23))]
@state err_fr= log(c(26))*err_fr(-1) + [var = exp(c(27))]
@state err_it= log(c(30))*err_it(-1) + [var = exp(c(31))]Does anyone had to deal with that kind of issue?
Thanks a lot in adavnce
