A question on this topic has been asked before: Combining a linear Kalman Filter with additional linear constraints.

And I checked out some of these references.

I will probably use estimate projection. However I have a couple of questions:

  1. For the log-likelihood calculation, (i.e. when fitting the model) should I use the original or projected estimates? I presume the former ?
  2. When forecasting should I use the original or projected estimates, in this case I think the latter ? Could someone please confirm which to use in each case and ideally why.
  3. Lastly for the prefect measurement method shown in the link above, why is the number of restrictions forced to be less than the number of variables ? I would have thought mathematically they could also be equal to the number of variables ?



  • $\begingroup$ Did you solve those issues? $\endgroup$ – Royi Mar 26 '16 at 13:18
  • $\begingroup$ Unfortunately not, I'd still be interested in knowing how to do it though. $\endgroup$ – Bazman Mar 27 '16 at 11:03
  • 1
    $\begingroup$ Cold you elaborate data from the articles into the question and we'll try to help? It's not a good practice to send people to read articles :-). $\endgroup$ – Royi Mar 27 '16 at 15:35

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