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-ofsample
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.
