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.