期刊名称：Departmental Discussion Papers / University of Glasgow, Department of Economics
出版年度：2009
卷号：1
出版社：University of Glasgow, Department of Economics
摘要：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.
关键词：Real Business Cycle, Bayesian estimation, VARMA errors
