Estimating Macroeconomic Models: A Likelihood Approach

Jesus Fernandez-Villaverde () and Juan F Rubio-Ramirez ()

No 5513, CEPR Discussion Papers from C.E.P.R. Discussion Papers

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.

Keywords: business cycle; dynamic macroeconomic models; nonlinear and/or non-normal models; particle filtering; stochastic volatility (search for similar items in EconPapers)
JEL-codes: C11 C5 E10 E32 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-dge and nep-mac
Date: 2006-03
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Related works:
Working Paper: Estimating Macroeconomic Models: A Likelihood Approach (2006)
Working Paper: Estimating Macroeconomic Models: A Likelihood Approach (2006)
Journal Article: Estimating Macroeconomic Models: A Likelihood Approach (2007)
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