In this paper we first develop two statistical tests of the null hypothesis that early release data are rational. The tests are consistent against generic nonlinear alternatives, and are conditional moment type tests, in the spirit of Bierens (1982,1990), Chao, Corradi and Swanson (2001 and Corradi and Swanson (2002). We then use this test, in conjunction with standard regression analysis in order to individually and jointly analyze a real-time dataset for money, output, prices and interest rates. All of our empirical analysis is carried out using various variable/vintage combinations, allowing us to comment not only on rationality, but also on a number of other related issues. For example, we discuss and illustrate the importance of the choice between using first, later, or mixed vintages of data in prediction. Interestingly, it turns out that early release data are generally best predicted using first releases. The standard practice of using "mixed vintages" of data appears to always yield poorer predictions, regardless of what we term "definitional change problems" associated with using only first releases for prediction. Furthermore, we note that our tests of first release rationality based on ex ante prediction find no evidence that the data rationality null hypothesis is rejected for a variety of variables (i.e. we find strong evidence in favor of the "news" hypothesis). Thus, it appears that there is little benefit to using later releases of data for prediction and policy analysis, for example.Additionally, we argue that the notion of final data is misleading, and that definitional and other methodological changes that pepper real-time datasets are important. Finally, we carry out an empirical example, where little evidence that money has marginal predictive content for output is found, regardless of whether various revision error variables are added to standard vector autoregression models of money, output, prices and interest rates.