Nowcasting with mixed frequency data using Gaussian processes

This paper develops flexible nowcasting and forecasting methods by combining elements from three strands of econometric literature. First, drawing from the mixed data sampling (MIDAS) framework introduced by Ghysels et al. (2007), see also Andreou et al. (2010), Ghysels (2016), or Ghysels et al. (2024) for a recent review, we leverage techniques that permit the efficient use of predictors sampled at a higher frequency than the target variable. Second, the Big Data literature, based on the idea that using a large set of predictors combined with penalized estimators or Bayesian shrinkage can improve predictive accuracy, see e.g., Babii et al. (2022) and Mogliani and Simoni (2021) in the context of mixed frequency models. Third, the machine learning literature, which postulates that proper algorithms combined with computing power can uncover complicated relationships among variables (economic and financial ones, in our case) and hence improve predictions, see e.g., Hastie et al. (2009). Our baseline framework uses Gaussian Processes (GPs) to estimate the unknown and perhaps nonlinear relationships between a target variable and a large set of mixed frequency predictors nonparametrically.