Estimating the Deep Parameters of RBC Model with Learning
Stefano Eusepi and
No 404, Computing in Economics and Finance 2005 from Society for Computational Economics
We formulate and estimate a RBC model with structural changes and with bounded rationality, where the economic agents have to learn about the former. This paper investigates whether the agentsâ€™ learning process can generate business cycles fluctuations which are empirically plausible. This in turn implies the estimation of the structural parameters of the model. The estimation is carried out using indirect inference methods that allow to deal with the nonlinearity generated by the learning process and do not require the estimation of the agentsâ€™ initial beliefs. Furthermore, given that the asymptotic behavior of the agentsâ€™ beliefs depends only on the deep parameters of the model, our econometric approach does not require the estimation of extra free parameters, compared with the RBC model under rational expectations. We find that private agentsâ€™ expectations have a significant role in explaining business cycle fluctuations.
Keywords: RBC Model; Bounded Rationality; Simulated quasi-maximum likelihood (search for similar items in EconPapers)
JEL-codes: E32 E37 D83 C15 (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:sce:scecf5:404
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