  We propose and discuss Bayesian machine learning methods for mixed data
sampling (MIDAS) regressions. This involves handling frequency mismatches with
restricted and unrestricted MIDAS variants and specifying functional
relationships between many predictors and the dependent variable. We use
Gaussian processes (GP) and Bayesian additive regression trees (BART) as
flexible extensions to linear penalized estimation. In a nowcasting and
forecasting exercise we focus on quarterly US output growth and inflation in
the GDP deflator. The new models leverage macroeconomic Big Data in a
computationally efficient way and offer gains in predictive accuracy along
several dimensions.
