DP5513 Estimating Macroeconomic Models: A Likelihood Approach

Author(s): Jesús Fernández-Villaverde, Juan Francisco Rubio-Ramírez
Publication Date: March 2006
Keyword(s): business cycle, dynamic macroeconomic models, nonlinear and/or non-normal models, particle filtering, stochastic volatility
JEL(s): C11, C5, E10, E32
Programme Areas: International Macroeconomics
Link to this Page: cepr.org/active/publications/discussion_papers/dp.php?dpno=5513

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.