Forecasting with DSGE models: The role of nonlinearities

This paper studies whether the out-of-sample forecasting performance of a dynamic stochastic general equilibrium (DSGE) model improves by taking its nonlinear rather than its linear approximation to the data. We address this question within a New Keynesian monetary economy, considering both environments of simulated and real data. Precisely, we estimate our model based on its linear respectively quadratic approximate solution, generate out-of-sample forecasts for three observables (output, inflation, and the nominal interest rate), and compare the quality of forecasts by several statistical measures of accuracy. We find that the value of nonlinearities in terms of predictive power depends crucially on whether the model is well specified. For simulated data, the nonlinear model indeed forecasts noticeably better as compared to its linearized counterpart, whereas for real data, we find no substantial differences in predictive abilities.