DSGE Models in a Data-Rich Environment / Jean Boivin, Marc Giannoni.

Format:

Book

Author/Creator:

Boivin, Jean.

Contributor:

National Bureau of Economic Research.

Giannoni, Marc.

Series:

Working Paper Series (National Bureau of Economic Research) no. w12772.

NBER working paper series no. w12772

Language:

English

Physical Description:

1 online resource: illustrations (black and white);

Place of Publication:

Cambridge, Mass. National Bureau of Economic Research 2006.

Summary:

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.

Notes:

Print version record

December 2006.