Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
This paper develops and illustrates a simple method of generating a DSGE model-based 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 medium-scale 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.