Estimation of DSGE models: Maximum Likelihood vs. Bayesian methods
2015 (English)Report (Other academic)
DSGE models are typically estimated using Bayesian methods, but a researcher may want to estimate a DSGE model with full information maximum likelihood (FIML) so as to avoid the use of prior distributions. A very robust algorithm is needed to ﬁnd the global maximum within the relevant parameter space. I suggest such an algorithm and show that it is possible to estimate the model of Smets and Wouters (2007) using FIML. Inference is carried out using stochastic bootstrapping techniques. Several FIML estimates turn out to be signiﬁcantly diﬀerent from the Bayesian estimates and the reasons behind those diﬀerences are analyzed.
Place, publisher, year, edition, pages
Uppsala: Uppsala University, Department of Economics , 2015. , 51 p.
Working paper / Department of Economics, Uppsala University (Online), ISSN 1653-6975 ; 2015:6
Bayesian methods, Maximum likelihood, Business Cycles, Estimate DSGE models
Research subject Economics
IdentifiersURN: urn:nbn:se:uu:diva-270200OAI: oai:DiVA.org:uu-270200DiVA: diva2:887132