Hi Alessandro, please have a look at the paper by Soren Johansen that I mentioned in my reply to Pasquale. Shortly, Soren argues that it is fundamentally wrong to use ML methods when the statistical model is not a credible (and testable) representation of the (statistical) data generating process. If one takes this point, then the problem with DSGEs is even more serious than weak identification… Ciao, Gianluca Da: Alessandro Casini <
> Hi, I only had a glance at the Introduction. I think their results might be credible. But isn't this known in Econometrics as weak identification? Because weak identification is a long-study problem in Macroeconometrics and also in DSGE model. It might be that their results are just consequences of weak identification. The paper does not mention weak identification. I was expecting a discussion of this. For example, Fernéndez-Villaverde (2010) in his survey of DSGE estimation writes: "likelihoods of DSGE models are full of local maxima and minima and of nearly at surfaces... the standard errors of the estimates are notoriously di‑difficult to compute and their asymptotic distribution a poor approximation to the small sample one." So it is possible to get weird results if one does not have strong identification. Best, Alessandro On 1/5/2023 1:14 PM, Pasquale Scaramozzino wrote:
-- Alessandro Casini ( "> ) Department of Economics and Finance University of Rome Tor Vergata Via Columbia 2 00133 Rome, Italy http://alessandro-casini.com |
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