Multivariate STAR Unemployment Rate Forecasts

Abstract

In this paper we use smooth transition vector error-correction models (STVECMs) in a simulated out-of-sample forecasting experiment for the unemployment rates of the four non-Euro G-7 countries, the U.S., U.K., Canada, and Japan. For the U.S., pooled forecasts constructed by taking the median value across the point forecasts generated by the STVECMs perform better than the linear VECM benchmark more so during business cycle expansions. Pooling across the linear and nonlinear forecasts tends to lead to statistically signißcant forecast improvement for business cycle expansions for Canada, while the opposite is the case for the U.K.