Macroeconomic Forecasting and Machine Learning

Forecasting has undergone a profound transformation in the 21st century, driven by advancements in methodology, computational power, and data availability. The origins of this transformation can be traced back to the late 1990s, when the field of economics began grappling with the challenges and opportunities presented by what is now termed "Big Data"--the availability of large datasets with numerous predictors. This period marked the emergence of systematic efforts to develop tools capable of addressing the high-dimensional nature of these datasets with seminal contributions by Frank Diebold, Mario Forni, Marc Hallin, Marco Lippi, Lucrezia Reichlin, Jim Stock, and Mark Watson (see Reichlin, 2003; Watson, 2003; Diebold, 2003). They laid the foundation for a new era of forecasting, as presented at the World Meeting of the Econometric Society in the summer of 2000 (for a survey see De Mol et al., 2017; Diebold, 2021). Since these early contributions, the field of macroeconomic forecasting has experienced significant progress.