DSGE Models in a Data-Rich Environment

Marc P. Giannoni () and Jean Boivin ()

No 431, Computing in Economics and Finance 2005 from Society for Computational Economics

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 adequately summarized by a small number of data series, whether or not measurement error is allowed for. 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 are likely to 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 through the lenses of a DSGE model. The estimation involves Bayesian Markov-Chain Monte-Carlo (MCMC) methods extended so that the estimates can, in some cases, inherit the properties of classical maximum likelihood estimation. We apply this estimation approach to a state-of-the-art DSGE monetary model. Treating theoretical concepts of the model --- such as output, inflation and employment --- as partially observed, we show that the information from a large set of macroeconomic indicators is important for accurate estimation of the model. It also allows us to improve the forecasts of important economic variables

Keywords: DSGE models; model estimation; measurement error; large data sets; factor models; MCMC techniques; Bayesian estimation (search for similar items in EconPapers)
JEL-codes: E52 E3 C32 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-dge, nep-ecm, nep-for and nep-mac
Date: Written 2005-11-11
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Related works:
Working Paper: DSGE Models in a Data-Rich Environment (2006)
Working Paper: DSGE Models in a Data-Rich Environment (2006)
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