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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 the authors estimate the model's posterior density using Markov-Chain Monte-Carlo (MCMC) methods. Within this framework they 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.
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