AbstractThis article reviews and illustrates the methodology of forecasting with dynamic stochastic general equilibrium (DSGE) models using Bayesian methods. It discusses an algorithm for estimating the predictive distribution of the observed variables based on draws from the posterior distribution of the DSGE model parameters and simulation of future paths for the variables with the model. The article is organized as follows. Section 2 sketches the new area-wide model (NAWM) and briefly reports on its empirical implementation. Section 3 discusses how the predictive distribution of a DSGE model can be estimated and then presents the alternative forecasting models that are used in the empirical analysis. Section 4 covers the forecast evaluation of the NAWM, focusing first on point forecasts and then on density forecasts. Section 5 summarizes the main findings and concludes.