Search Results

You are looking at 1 - 2 of 2 items for :

  • "Regular and data-rich DSGE models" x
Clear All
Mr. Maxym Kryshko
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.
Mr. Maxym Kryshko

-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 regular and data-rich DSGE models. 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