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.

翻译：我们提出并讨论了适用于混合数据采样回归的贝叶斯机器学习方法。这涉及使用受限和非受限混合数据采样变体处理频率不匹配问题，并指定多个预测变量与因变量之间的函数关系。我们采用高斯过程及贝叶斯加性回归树作为线性惩罚估计的灵活扩展。在即时预测与预测实践中，我们聚焦于美国季度产出增长及GDP平减指数中通胀的预测。这些新模型以计算高效的方式利用宏观经济大数据，并在多个维度上提升了预测精度。