
Greetings!
I was wondering what algorithm lies behind the exact nonlinear Kalman filtering employed in IRIS. Is it based on some kind of linearisation of nonlinear equations invlolved?
Best,
AndreyO


Coordinator
Jul 28, 2015 at 6:39 PM

As far as I know it is able to take a nonlinear prediction step but the MSE matrices etc. are still based on assumptions about linearity and Gaussian shocks as in the standard Kalman filter. In this sense it may not be optimal if the nonlinearities are
strong.
People like Robert Kollmann have developed analytic filtering solutions to which are secondorder accurate:
http://www.robertkollmann.com/KOLLMANN_PUBL_COMPUT_ECON_2015.pdf. Otherwise you need to use a particle filter (unless I have missed some recent paper in the literature...).
Jaromir wrote the code behind this. Perhaps he can comment with more detail.


Coordinator
Jul 28, 2015 at 6:45 PM

Yes, as Michael said... The nonlinear Kalman filter in IRIS only runs a nonlinear prediction step, while the updating(filtering) and smoothing steps are the usual first order algorithms. This a quickanddirty approach that can capture a substantial portion
of nonlinear behavior in many model, but may fail when the nonlinearities are more nontrivial...
Best,
Jaromir



Thanks for the replies!
Could you give a hint on how the nonlinear prediction step is computed?
Best,
AndreyO


Coordinator
Jul 29, 2015 at 9:27 AM

Hi Andrey
It is computed using the equationselective nonlinear algorithm, the same as nonlinear simulations in the
simulate(...) command. I'll post a note describing the algorithm on the website.



Thank you, Jaromir!

