Decomposition into observables

Hello, community.

I would like to know if Dynare can decompose the smoothed variables (from the Kalman Filter) into contributions from the different observable variables. I am interested in better gauging what variables helps in identifying the latent variables in my model.

Some papers that do this composition are the following:

Let me give you some context about my question. I have a simple 3 equation NK model, which I use to get an estimate of the output gap. The model is augmented with a block of observed variables strictly related to the output gap (such as HP-filtered-GDP gap and capacity utilisation gap). This block is important to discipline the estimation of the latent output gap. Therefore, my smoothed output gap will be recovered from observed variables like inflation and interest rates, but also from other variables related to the output gap.

After running calib_smoother, I successfully get an estimate of the slack of my economy. Yet, I would like to gauge what is behind this estimate, i.e., what is the relative importance of each observable. My intuition says it can be obtained through a back of the envelope calculation using the observables and the smoothed states. But Iā€™m not sure.

If the answer to my question happens to be a straightforward NO, do you have any alternative in mind?

Thank you all for the support.

No, the technique by Andrle has not yet been implemented in Dynare. Dynare should provide all the outputs required, but it still needs to be coded.

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Thank you for the prompt reply.