I have a question on measurement equations. I consider non-linear model but I use log linear transformation for the data (some of ts) and hence I write:

I got quite standard results for my estimation. But I couldn’t match my smoothed variables back to the data. I tried the transformation : y_model = exp( y_smoothedvariable + log( y_model)) and it worked only for prices… Even though it is weird since dynare does linearization and not log-linearization by default.

Do you know what is the matter, or I just shall use non-linear measurement equations?
Thanks a lot!

Could you please provide step by step information on what you are doing. By construction the smoothed variables will coincide with the data you provide during estimation - unless there is measurement error or stochastic singularity.

For example inflation: if I take oo_.SmoothedVariables.pi then I fit the data that corresponds to oo_.SmoothedVariables.piobs. No problem here. So the smoothed variables are already presented in log deviations.

And the series are very close but no the same (see attached picture) meaning that this is a little missmatch between oo_.SmoothedVariables.yobs(ii, and yobs (ii,:). for all ii of the sample.

Do you have an idea what’s happening?

I don’t consider gammazss since oo_.SmoothedVariables.gammaz = log(gammaz) + log (gammazss).

Sorry, but the attachement is missing. Please clarify what the observation equation is and which smoothed object(s) you use to construct a series corresponding to the data.