Calibrate Shock in Bayesian Estimation

Dear all,
I have to compare Bayesian IRFs with different models for a monetary policy shock.
Thus, to make it comparable, the shock size across all models should be the same.
I have removed rhoM from estimated_params, so that the MP shock will be calibrated since I have rhoM = 0.5 as a parameter calibrated.
However, I get the error message:

Error using chol
Matrix must be positive definite. (…)

Could someone give me a hint, where the mistake is and how I can set the MP shock to be equal across all models?

Thank you !

  1. Usually you should estimate the model without such restrictions if they are just there for comparability.
  2. You can use the relative_irf- option to generate IRFs to a 1percent shock in all models and then rescale these IRFs.

Hello Prof. Pfeifer,

so you would suggest not to equalize the shock size but to estimate the shock, so that the shock across all models is 1 stderr and compare models of a shock of 1 stderr?
I would like to do it that way, but I don’t understand, how the models are comparable since in the Bayesian Irfs one model is hit by a r=0.129006 shock and in another model the economy is hit by a r=0.1899 shock.
Then the irfs of the other variables differ since there is a discripancy in the shocks.

Could you clarify the misunderstanding?

Thanks !

What the relative_irf-option will do is provide the IRFs to a 1 percent shock instead of a one standard deviation shock. That solves the comparability issue.

Hello Prof. Pfeifer,

I have followed the steps suggested in a previous post:

in my case, given that I have a log linearized version and no stoch_simul command
I have added after the estimation command

if options_.relative_irf 

However, I was not successful with it. Could you give me an advice?

Many thanks !

Why don’t you set options_.relative_irf=1 before estimation?