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Estimating the Deep Parameters of RBC Model with Learning

Author

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  • Stefano Eusepi
  • Stefania D'Amico

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.

Suggested Citation

  • Stefano Eusepi & Stefania D'Amico, 2005. "Estimating the Deep Parameters of RBC Model with Learning," Computing in Economics and Finance 2005 404, Society for Computational Economics.
    • Handle: RePEc:sce:scecf5:404
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    More about this item

    Keywords

    RBC Model; Bounded Rationality; Simulated quasi-maximum likelihood;
    All these keywords.

    JEL classification:

    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General

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