I have two estimated models I want to compare, which I can do using, say, the log data density. Everything is the same in both models except for different monetary policy rules. I have the following results: Model 1: larger log data density, but data moments using the estimated parameters are off b…

The point is which moments you match. SMM will match the targeted moments, MDD will target all available moments, not just the ones you care about most.

We had a similar discussion in [image] How to see whether the model fit the real data well? ML/Bayesian estimation You are confusing fit in the context of full information and limited information estimation techniques. They imply a different weighting of moments. See above…

Many thanks, Prof Pfeifer!! I understood but I still have a little confusion. So more like, use MDD to first select features and compare models. But there is no guarantee that moments would be matched under the estimated parameters. So secondly, I can use, say, SMM to estimate the model again and ma…

Thanks, Prof. Pfeifer, for the reply. I understand. But I guess I do not know which moments I want to match. :). I am doing something related to optimal monetary policy. Is there is rule of thumb or some guide on which moments you should care about? When I was in graduate school, we kind of never ta…

But if you already estimated your model using full information techniques, then you are done. There is nothing else you need to do. Theoretically, that’s the best you can do (unless the model is misspecified)

Comparing models using log data density vs using moments

General DSGE Modeling

jpfeifer 11 April 2023 12:13 2

We had a similar discussion in

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