Estimating Macroeconomic Models: A Likelihood Approach

Jesus Fernandez-Villaverde and Juan F Rubio-Ramirez

No 321, NBER Technical Working Papers from National Bureau of Economic Research, Inc

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.

JEL-codes: C11 C15 E10 E32 (search for similar items in EconPapers)
Date: 2006-02
New Economics Papers: this item is included in nep-ecm and nep-mac
Note: TWP
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (46)

Downloads: (external link)
http://www.nber.org/papers/t0321.pdf (application/pdf)

Related works:
Journal Article: Estimating Macroeconomic Models: A Likelihood Approach (2007)
Working Paper: Estimating Macroeconomic Models: A Likelihood Approach (2006)
Working Paper: Estimating Macroeconomic Models: A Likelihood Approach (2006)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nbr:nberte:0321

Ordering information: This working paper can be ordered from
http://www.nber.org/papers/t0321

Access Statistics for this paper

More papers in NBER Technical Working Papers from National Bureau of Economic Research, Inc National Bureau of Economic Research, 1050 Massachusetts Avenue Cambridge, MA 02138, U.S.A.. Contact information at EDIRC.
Bibliographic data for series maintained by ().