We study the real-time Granger-causal relationship between crude oil prices and US GDP growth through a simulated out-of-sample (OOS) forecasting exercise; we also provide strong evidence of in-sample predictability from oil prices to GDP. Comparing our benchmark "model\without oil against...

We study the real-time Granger-causal relationship between crude oil prices and US GDP growth through a simulated out-of-sample (OOS) forecasting exercise; we also provide strong evidence of in-sample predictability from oil prices to GDP. Comparing our benchmark model "without oil" against...

Asymmetric behavior has been documented in post-war quarterly U.S. unemployment rates. This suggests that improvement over conventional linear forecasts may be possible through use of nonlinear time series models. In this paper an out-of-sample forecasting competition is carried out for a set of...