When estimating DSGE models, the number of observable economic variables is usually kept small, and it is conveniently assumed that DSGE model variables are perfectly measured by a single data series. Building upon Boivin and Giannoni (2006), we relax these two assumptions and estimate a fairly simple monetary DSGE model on a richer data set. Using post-1983 U.S.data on real output, inflation, nominal interest rates, measures of inverse money velocity, and a large panel of informational series, we compare the data-rich DSGE model with the regular - few observables, perfect measurement - DSGE model in terms of deep parameter estimates, propagation of monetary policy and technology shocks and sources of business cycle fluctuations. We document that the data-rich DSGE model generates a higher implied duration of Calvo price contracts and a lower slope of the New Keynesian Phillips curve. To reduce the computational costs of the likelihood-based estimation, we employed a novel speedup as in Jungbacker and Koopman (2008) and achieved the time savings of 60 percent.
-space representation of the model is constructed by augmenting (1) - (2) with a number of measurement equations that connect model concepts in S ¯ t
to data indicators in vector X t .
A. Regular vs. Data-Rich DSGE Models
Depending on the number of data indicators and on how we connect them to the model concepts, we will distinguish regularanddata-richDSGEmodels. In regular DSGE models, the number of observables contained in X t is usually kept small (most often equal to the number of structural shocks) and model concepts are often assumed to be perfectly measured