In this paper, we assess the relevance of real-time datasets for forecasting. We construct a variety of real-time prediction models and evaluate their performance in a series of ex-ante prediction experiments that are designed to mimic forecasting approaches used when constructing forecasts in real-time for output, prices and money. We assess the models within univariate and multivariate frameworks by including revision errors as regressors, allowing us to examine the marginal predictive content of the revision process. In another multivariate application for output we add money, thus examining the real-time predictive content of money for income. The most important result we obtain is that the choice of which release of data to predict seems not to have an impact on which releases of data should be used in estimation and prediction construction but that differences in how to utilize realtime datasets do arise when the variable being modelled and predicted changes. Overall our findings point to the importance of making real-time datasets available to forecasters, as the revision process has marginal predictive content, and because predictive accuracy increases when multiple releases of data are used when specifying and estimating prediction models. This underscores the importance of collecting and maintaining such real-time datasets.