DSGE Models in a Data-Rich Environment

Jean Boivin () and Marc Giannoni

No 332, NBER Technical Working Papers from National Bureau of Economic Research, Inc

Abstract: Standard practice for the estimation of dynamic stochastic general equilibrium (DSGE) models maintains the assumption that economic variables are properly measured by a single indicator, and that all relevant information for the estimation is summarized by a small number of data series. However, recent empirical research on factor models has shown that information contained in large data sets is relevant for the evolution of important macroeconomic series. This suggests that conventional model estimates and inference based on estimated DSGE models might be distorted. In this paper, we propose an empirical framework for the estimation of DSGE models that exploits the relevant information from a data-rich environment. This framework provides an interpretation of all information contained in a large data set, and in particular of the latent factors, through the lenses of a DSGE model. The estimation involves Markov-Chain Monte-Carlo (MCMC) methods. We apply this estimation approach to a state-of-the-art DSGE monetary model. We find evidence of imperfect measurement of the model's theoretical concepts, in particular for inflation. We show that exploiting more information is important for accurate estimation of the model's concepts and shocks, and that it implies different conclusions about key structural parameters and the sources of economic fluctuations.

JEL-codes: C10 C32 C53 E01 E32 E37 (search for similar items in EconPapers)
Date: 2006-12
New Economics Papers: this item is included in nep-cba, nep-dge, nep-ecm and nep-mac
Note: EFG ME TWP
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (199)

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Related works:
Working Paper: DSGE Models in a Data-Rich Environment (2007)
Working Paper: DSGE Models in a Data-Rich Environment (2006)
Working Paper: DSGE Models in a Data-Rich Environment (2005)
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