Flexible regression models and relative forecast performance

In this paper, four alternative flexible nonlinear regression model approaches are reviewed and their performance evaluated based on various measures of out-of-sample forecast accuracy. The class of flexible regression model considered includes Neural Networks, Projection Pursuit models and the Random Field regression model approach recently suggested by Hamilton [Econometrica 69 (2001) 537-573]. An empirical illustration is provided, showing that linear models for the US unemployment rate and the growth rate in US industrial production cannot outperform the ''best'' flexible nonlinear regression models in terms of out-of-sample forecast accuracy. The results indicate a possible presence of a nonlinear component in the conditional mean function of both time series.