Was the recent downturn in US real GDP predictable?

Abstract

This article uses a small set of variables - real GDP, the inflation rate and the short-term interest rate - and a rich set of models - atheoretical (time series) and theoretical (structural), linear and nonlinear, as well as classical and Bayesian models - to consider whether we could have predicted the recent downturn of the US real GDP. Comparing the performance of the models to the benchmark random-walk model by root mean-square errors, the two structural (theoretical) models, especially the nonlinear model, perform well on average across all forecast horizons in our ex post, out-of-sample forecasts, although at specific forecast horizons certain nonlinear atheoretical models perform the best. The nonlinear theoretical model also dominates in our ex ante, out-of-sample forecast of the Great Recession, suggesting that developing forward-looking, microfounded, nonlinear, dynamic stochastic general equilibrium models of the economy may prove crucial in forecasting turning points.