Is quantification synonymous to calibration in DSGE jagon?

I watched some recorded conferences on DSGEs and it seems one of DSGEs’ main purposes (maybe the dominant one) is quantification, i.e., quantifying a theoretical mechanism using the DSGE framework. For example, pick a theory on corruption and quantify its implications in a DSGE model.

It seems though there isn’t a formal definition for ‘quantification’. Normally it is meant to imply ‘putting some numbers’ as I have read and heard in some presentations. ‘Putting some numbers’ basically means properly calibrating the model, yeah?

Assuming my model is properly calibrated, can I say the model is properly quantified, conditioned on the mechanism in the model being true? As a non-theorist, your emphasis should be on justifying your calibration and not justifying the theory you are trying to incorporate into the DSGE model, right? Thanks!

The question covers a wide range of issues. Assigning parameters values (if you believe the mechanism) is complicated, with approaches ranging from estimation to formal calibration to just plugging in some numbers.

My understanding of quantification is to see how far your proposed mechanism brings you in terms of explaining the data. The mechanism’s performance will always depend on the parameters. That’s where outside evidence usually restricts you (e.g. risk aversion estimates from the lab). You are always defending both, your theory and the parameter values. Which part of the model is criticized most will depend on the referees.