This paper studies the comparative predictive accuracy of forecasting methods using mixed-frequency data, as applied to nowcasting Philippine inflation, real GDP growth, and other related macroeconomic variables. It focuses on variations of mixed-frequency dynamic latent factor models (DFM for short) and Mixed Data Sampling (MIDAS) Regression. DFM is parsimonious and dependent on a much smaller data set that needs to be updated regularly but technically and computationally more complicated, especially when there are mixed-frequency data. On the other hand, MIDAS is data-intensive but computationally more tractable. The analysis is done through comparison of forecast performance measures (such as mean squared prediction error) and application of statistical tests of comparative predictive accuracy and tests of forecast encompassing. Results obtained so far indicate that just about every method in the pool of forecasting methods studied performs best in some cases and worst in other cases. Thus, there is no clear winner. Under the circumstances, one viable approach in applications is to combine the forecasts from these powerful techniques to improve predictive accuracy. In most cases, least squares weights perform better for purposes of forecast averaging.