I was listening to a macro presentation, and the presenter showed his calibration strategy.
Two parameters in a stochastic process were estimated (MLE) and the rest was parameterized to match some moments. Then there was a critic of mixing estimation and calibration (more specifically, the critic was about not simulating all the moments/using SMM).
Why should we not use estimation and calibration at the same time? Is it because it’s wrong or just not recommendable?
SMM is not calibration, but a form of estimation. The problem with what you are describing is that you are mixing two types of estimation strategies: full information and limited information. Thus, there is an inherent tension: