There is a continuum of causal/counterfactual techniques in economics.
This continuum ranges between "calibrated" simulation models and "econometric" models. The latter are statistical approaches, with the extreme capped by distribution free or semi-parametric "causal" estimations.
Structural dynamic econometric models, for example the estimation of parameters of auctions, hiring behavior or impacts of cost shifts, would fall in the middle of that range, featuring approaches from both areas (for example, computational fixed-point search for equilibria, re-estimation of parameters and then looping back to a new equilibrium).
The paper you cite is on the former: it is a calibrated DSGE model.
It is therefore not even an econometric model.
I don't want to be mean, but it really, really seems you are on the first Dunning Kruger peak of "knowing what one is talking about" when it comes to econometrics.
There is a continuum of causal/counterfactual techniques in economics.
This continuum ranges between "calibrated" simulation models and "econometric" models. The latter are statistical approaches, with the extreme capped by distribution free or semi-parametric "causal" estimations.
Structural dynamic econometric models, for example the estimation of parameters of auctions, hiring behavior or impacts of cost shifts, would fall in the middle of that range, featuring approaches from both areas (for example, computational fixed-point search for equilibria, re-estimation of parameters and then looping back to a new equilibrium).
The paper you cite is on the former: it is a calibrated DSGE model. It is therefore not even an econometric model.
I don't want to be mean, but it really, really seems you are on the first Dunning Kruger peak of "knowing what one is talking about" when it comes to econometrics.