Shocks and Frictions in US Business Cycles: A Bayesian DSGE Approach
Frank Smets and
Raf Wouters ()
American Economic Review, 2007, vol. 97, issue 3, 586-606
Using a Bayesian likelihood approach, we estimate a dynamic stochastic general equilibrium model for the US economy using seven macroeconomic time series. The model incorporates many types of real and nominal frictions and seven types of structural shocks. We show that this model is able to compete with Bayesian Vector Autoregression models in out-of-sample prediction. We investigate the relative empirical importance of the various frictions. Finally, using the estimated model, we address a number of key issues in business cycle analysis: What are the sources of business cycle fluctuations? Can the model explain the cross correlation between output and inflation? What are the effects of productivity on hours worked? What are the sources of the "Great Moderation"? (JEL D58, E23, E31, E32)
Note: DOI: 10.1257/aer.97.3.586
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2103) Track citations by RSS feed
Downloads: (external link)
Access to full text is restricted to AEA members and institutional subscribers.
Working Paper: Shocks and Frictions in US Business Cycles: A Bayesian DSGE Approach (2007)
Working Paper: Shocks and frictions in US business cycles: a Bayesian DSGE approach (2007)
Working Paper: Shocks and Frictions in US Business Cycles: a Bayesian DSGE Approach (2007)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:aea:aecrev:v:97:y:2007:i:3:p:586-606
Ordering information: This journal article can be ordered from
Access Statistics for this article
American Economic Review is currently edited by Esther Duflo
More articles in American Economic Review from American Economic Association Contact information at EDIRC.
Bibliographic data for series maintained by Michael P. Albert ().