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 revisions to GDP and its components. 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, models that include revisions to components of GDP improve forecast accuracy. The first revision to consumption is particularly relevant in that every model that includes the revision outperforms the baseline model. Measured by root mean squared forecasting error (RMSFE), improvements are quite sizable, with many models increasing forecasting performance by 5% or more, and with top-performing models forecasting 0.18 percentage points closer to the advance estimate of growth. We use Bayesian model averaging to underscore that our results are driven by the informational content of revisions. The posterior probability of models with the first revision to consumption is significantly higher than our baseline model, despite strong priors that the latter should be the preferred forecasting model. We thank Dean Croushore, Eric Gaus, C. Richard Higgins, and Jermy Piger for reading early drafts, and we thank anonymous referees for their helpful feedback on the completed draft. We also benefitted from comments at the Midwest Economics Association's Annual Meeting, the Liberal Arts Macro Conference, the Applied Probability and Statistics Workshop at St. Thomas, and a seminar at Drake University. We appreciate Mary Reichardt's editorial suggestions. All remaining errors are our own.