This paper develops and illustrates a simple method to generate a DSGE modelbased forecast for variables that do not explicitly appear in the model (non-core variables). We use auxiliary regressions that resemble measurement equations in a dynamic factor model to link the non-core variables to the state variables of the DSGE model.Predictions for the non-core variables are obtained by applying their measurement equations to DSGE model-generated forecasts of the state variables. Using a mediumscale New Keynesian DSGE model, we apply our approach to generate and evaluate recursive forecasts for PCE inflation, core PCE inflation, the unemployment rate, and housing starts along with predictions for the seven variables that have been used to estimate the DSGE model.
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 regularfew 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.
This paper develops and illustrates a simple method to generate a DSGE modelbased forecast for variables that do not explicitly appear in the model (non-core variables). We use auxiliary regressions that resemble measurement equations in a dynamic factor model to link the non-core variables to the state variables of the DSGE model.Predictions for the non-core variables are obtained by applying their measurement equations to DSGE model-generated forecasts of the state variables. Using a mediumscale New Keynesian DSGE model, we apply our approach to generate and evaluate recursive forecasts for PCE inflation, core PCE inflation, the unemployment rate, and housing starts along with predictions for the seven variables that have been used to estimate the DSGE model.
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