Forecasting GDP: Do Revisions Matter?

This paper investigates the informational content of regular revisions to real GDP growth and its components. We perform a real-time forecasting exercise for the advance estimate of real GDP growth using dynamic regression models that include GDP and GDP component revisions. Echoing other work in the literature, we find little evidence that including aggregate GDP growth revisions improves forecast accuracy relative to an AR(1) baseline model; however, when we include revisions to components of GDP (i.e. C, I, G, X, and M) we find improvements in forecast accuracy. Overall, nearly 68\% of all models that contain subsets of component revisions outperform our baseline model. The "best" component-augmented model forecasts roughly 0.2 percentage points better, and a large subset of models improve RMSFE by more than 5%. Finally, we use Bayesian model comparison to demonstrate that differences in forecast performance are unlikely to be the result of statistical noise. Our results imply that component revisions, in particular to consumption, contain important information for forecasting GDP growth.