We show how to improve the accuracy of real-time forecasts from models that include autoregressive terms by estimating the models on ëlightly-revisedídata instead of using data from the latest-available vintage. Forecast accuracy is improved by reorganizing the data vintages employed in the estimation of the model in such a way that the vintages used in estimation are of a similar maturity to the data in the forecast loss function. The size of the expected reductions in mean squared error depend on the characteristics of the data revision process. Empirically, we Önd RMSFE gains of 2-4% when forecasting output growth and ináation with AR models, and gains of the order of 8% with ADL models.