TY - JOUR AU - Boivin,Jean AU - Giannoni,Marc TI - DSGE Models in a Data-Rich Environment JF - National Bureau of Economic Research Working Paper Series VL - No. 12772 PY - 2006 Y2 - December 2006 UR - http://www.nber.org/papers/w12772 L1 - http://www.nber.org/papers/w12772.pdf N1 - Author contact info: Jean Boivin Bank of Canada 234 Wellington Street Ottawa Ontario K1A 0G9 Canada Tel: 613-782-8278 E-Mail: jboivin@bankofcanada.ca Marc Giannoni Federal Reserve Bank of New York Macroeconomic & Monetary Studies Function Research and Statistics Group 33 Liberty Street New York, NY 10045-0001 Tel: 212-720-6518 Fax: 212-720-1844 E-Mail: mg2190@columbia.edu AB - 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. ER -