Ok I see. I have read multiple papers (in good quality journals) where they simulate the model at the mean of the posterior parameters. So what they are doing is technically incorrect? Also, when simulating a simple model where the parameters are calibrated, these calibrated parameters implicitly reference some previous research that has estimated these parameters. Thus, isn’t calibration simply setting a model at the mean of some estimated parameter that some paper has estimated?
Would you say that the only way to properly simulate an estimated model is to simulate the model at the entire distribution of the parameter space? I.e. plot an IRF for each accepted parameter draw combination during the estimation process. Thus, the IRFs would be described by a region