Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/26671 
Year of Publication: 
2009
Series/Report no.: 
CESifo Working Paper No. 2626
Publisher: 
Center for Economic Studies and ifo Institute (CESifo), Munich
Abstract: 
This paper contributes to the on-going empirical debate regarding the role of the RBC model and in particular of technology shocks in explaining aggregate fluctuations. To this end we estimate the model's posterior density using Markov-Chain Monte-Carlo (MCMC) methods. Within this framework we extend Ireland's (2001, 2004) hybrid estimation approach to allow for a vector autoregressive moving average (VARMA) process to describe the movements and co-movements of the model's errors not explained by the basic RBC model. The results of marginal likelihood ratio tests reveal that the more general model of the errors significantly improves the model's fit relative to the VAR and AR alternatives. Moreover, despite setting the RBC model a more difficult task under the VARMA specification, our analysis, based on forecast error and spectral decompositions, suggests that the RBC model is still capable of explaining a significant fraction of the observed variation in macroeconomic aggregates in the post-war U.S. economy.
Subjects: 
Real Business Cycle
Bayesian estimation
VARMA errors
JEL: 
C11
C52
E32
Document Type: 
Working Paper
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