DP5513 Estimating Macroeconomic Models: A Likelihood Approach

Author(s): Jesús Fernández-Villaverde , Juan Francisco Rubio-Ramirez

Publication Date: March 2006

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

JEL(s): C11 , C5 , E10 , E32

Programme Areas: International Macroeconomics

Link to this Page: www.cepr.org/pubs/dps/DP5513.asp

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.