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Estimating the Deep Parameters of RBC Model with Learning

Stefano Eusepi and Stefania D'Amico

No 404, Computing in Economics and Finance 2005 from Society for Computational Economics

Abstract: 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 agents’ 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 agents’ initial beliefs. Furthermore, given that the asymptotic behavior of the agents’ 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 agents’ 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: C15 D83 E32 E37 (search for similar items in EconPapers)
Date: 2005-11-11
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