This thesis addresses different issues related to the use of mixed-frequency data. In the first chapter, I review, discuss and compare the main approaches proposed so far in the literature to deal with mixed-frequency data, with ragged edges due to publication delays: aggregation, bridge-equations, mixed-data sampling (MIDAS) approach, mixed-frequency VAR and factor models. The second chapter, a joint work with Massimiliano Marcellino, compares the different approaches analyzed in the first chapter, in a detailed empirical application. We focus on now- and forecasting the quarterly growth rate of Euro Area GDP and its components, using a very large set of monthly indicators, with a wide number of forecasting methods, in a pseudo real-time framework. The results highlight the importance of monthly information, especially during the crisis periods. The third chapter, a joint work with Massimiliano Marcellino and Christian Schumacher, studies the performance of a variant of the MIDAS model, which does not resort to functional distributed lag polynomials. We call this approach unrestricted MIDAS (U-MIDAS). We discuss the pros and cons of unrestricted lag polynomials in MIDAS regressions. In Monte Carlo experiments and empirical applications, we compare U-MIDAS to MIDAS and show that U-MIDAS performs better than MIDAS for small differences in sampling frequencies. The fourth chapter, a joint work with Massimiliano Marcellino, focuses on the issues related to mixed-frequency data in structural models. We show analytically, with simulation experiments and with actual data that a mismatch between the time scale of a DSGE or structural VAR model and that of the time series data used for its estimation generally creates identification problems, introduces estimation bias and distorts the results of policy analysis. On the constructive side, we prove that the use of mixed-frequency data can alleviate the temporal aggregation bias, mitigate the identification issues, and yield more reliable policy conclusions.