Was the Recent Downturn in US GDP Predictable?

Abstract

This paper uses small set of variables-- real GDP, the inflation rate, and the short-term interest rate -- and a rich set of models -- athoeretical (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 by root mean squared errors of the models to the benchmark random-walk model, the two structural (theoretical) models, especially the nonlinear model, perform well on the average across all forecast horizons in our ex-post out-of-sample forecasts, although at specific forecast horizons certain nonlinear athoeretical 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.

Keywords: Forecasting, Linear and non-linear models, Great Recession

JEL Classification: C32, E37