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Title: Estimating Macroeconomic Models: A Likelihood Approach

Author(s): Jesús Fernández-Villaverde and Juan Francisco Rubio-Ramírez

Publication Date: March 2006

Keyword(s): business cycle, dynamic macroeconomic models, nonlinear and/or non-normal models, particle filtering and stochastic volatility

Programme Area(s): International Macroeconomics

Abstract: This paper shows how particle filtering allows us to undertake likelihood-based inference in dynamic macroeconomic models. The models can be nonlinear and/or non-normal. We describe how to use the output from the particle filter to estimate the structural parameters of the model, those characterizing preferences and technology, and to compare different economies. Both tasks can be implemented from either a classical or a Bayesian perspective. We illustrate the technique by estimating a business cycle model with investment-specific technological change, preference shocks, and stochastic volatility.

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Bibliographic Reference

Fernández-Villaverde, J and Rubio-Ramírez, J. 2006. 'Estimating Macroeconomic Models: A Likelihood Approach'. London, Centre for Economic Policy Research. https://cepr.org/active/publications/discussion_papers/dp.php?dpno=5513