DSGE Estimation of Models with Learning∗
نویسنده
چکیده
I investigate how a model that assumes learning might interact with a rational expectations data generating process. Milani (2007b) asserts that if agents are learning and there is no conditional heteroscedasticity then an econometrician may be fooled into estimating ARCH/GARCH models. In addition, I evaluate the contribution of a new endogenous gain, which I have proposed in previous paper, may yield on the fit of the NK model. Initial indications suggest the an endogenous gain can significantly increase the value of the likelihood function, which could mean that model comparison, using Bayes factor analysis, would support a model with an endogenous gain.
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