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The benefits of estimating autoregressive models on lightly revised data are related to the nature of the data revision process and the underlying process for the true values. Empirically, we find improvements in root mean square forecasting error of 2\u20134% when forecasting output growth and inflation with univariate models, and of 8% with multivariate models. We show that multiple\u2010vintage models, which explicitly model data revisions, require large estimation samples to deliver competitive forecasts. 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