What is the main strength of Smet and Wouters paper (2003,2007)

The following statement typically appears in papers in different forms

“Smets and Wouters [2003 or 2007] account relatively well for euro area business cycles.”

What is the model’s main strength in accounting well for the euro business cycle? Is it because of the frictions in the model? It seems so. Or is it because of the Bayesian estimation technique they use? Or both? If both, how much weight should we place on frictions versus estimation method? People do not really do sensitivity analysis for different estimation methods, so I guess the model’s strength is in the frictions.

Are there other models with different frictions that also match the data really well? Like if we drop two frictions from Smets and Wouters model, perhaps it will still match the data? Smets and Wouters perform sensitivity analysis, but I suppose that is different from completely dropping frictions from the model or adding more frictions. And would using a different estimation method matter for the estimated parameters.

Ideally, I should check this myself…:slight_smile: But maybe, the answer is common knowledge in the field…and some paper already does that.

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There are some papers like Christiano/Motto/Rostagno (2014) that compare marginal data densities of models with different frictions to judge which specification matches the data best. That approach corrects for degrees of freedom.
Also, it’s about allowing for the frictions per se. The estimation will only tell you about the strength of the frictions.