| Summary: | ThispaperaimstocomparetheforecastingperformanceofthewidelyusedVARandBayesian VAR model to the unrestricted MIDAS regression. The models are tested on a real-time macroeconomic data set ranging from 2000 to 2015. The variables are mixed frequency data, specifically, predictions are made for GDP, using economic tendency indicator, unemployment and inflation as predicting variables. The baseline model of this analysis is a simple VAR, while it has great flexibility, this model risks to overfit the data and as a consequence makes unreliablepredictions. TheVillaniBayesianVARismeanttosolvethisproblembyintroducing long run beliefs about the data structure and the steady state unconditional means of each series. Whenfacingmixedfrequencydata,boththeseapproachedaggregateatthelowerlevelby discarding useful information. In this scenario, the unrestricted MIDAS model addresses this problem without losing high frequency information. The results show how both BVAR and U-MIDAS outperform VAR at every horizon, while there is no absolute winner among BVAR and U-MIDAS. Evidence suggests that U-MIDAS is superior for short horizons, specifically up to the 5th step ahead, which corresponds to one year and a quarter.
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