Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
I generate priors for a vector autoregression (VAR) from a standard real business cycle (RBC) model, an RBC model with capital-adjustment costs and habit formation, and a sticky-price model with an unaccommodating monetary authority. The response of hours worked to a TFP shock differs sharply across these models. I compare the accuracy of forecasts made from each of the resulting dynamic stochastic general equilibrium vector autoregression (DSGE-VAR) models. Despite having different structural characteristics, the DSGE-VARs are comparable in terms of forecasting performance. As in previous work, DSGE-VARs compare favorably with atheoretical VARs.