Transmuting unequally spaced data: a MIDAS regression touch to forecast real GDP growth in Brazil
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Unequally spaced data poses a dilemma on how to aggregate high-frequency variables to model a low-frequency variable. To tackle this quandary, this work proposes to apply MI(xed) DA(ta) S(ampling) (MIDAS), which allows the independent and dependent variables to be sampled at various and different frequencies, to forecast the real GDP growth in Brazil using macroeconomic data. The results show that the restricted polynomial MIDAS specification can outperform the AR(1) for out of the sample recursively estimated nowcasts. Moreover, IBC-BR restricted lag polynomial based MIDAS showcase the best performance under all the computed metrics for evaluation. Not only did the restricted IBC-Br MIDAS outperform the benchmark, but it also beat the U-MIDAS. Fortuitously, the cumulative MSE ratio revealed that between 2014Q3 until the end of 2015, the quotient for the monetary base MIDAS model continuously declined. While this behavior might not be related to the "fiscal pedaling", its trend contributes to the economic policy narrative during those years.