Financial Conditions and Economic Activity: Insights from Machine Learning

Machine learning (ML) techniques are used to construct a financial conditions index (FCI). The components of the ML-FCI are selected based on their ability to predict the unemployment rate one-year ahead. Three lessons for macroeconomics and variable selection/dimension reduction with large datasets emerge. First, variable transformations can drive results, emphasizing the need for transparency in selection of transformations and robustness to a range of reasonable choices. Second, there is strong evidence of nonlinearity in the relationship between financial variables and economic activity—tight financial conditions are associated with sharp deteriorations in economic activity and accommodative conditions are associated with only modest improvements in activity. Finally, the ML-FCI places sizable weight on equity prices and term spreads, in contrast to other measures. These lessons yield an ML-FCI showing tightening in financial conditions before the early 1990s and early 2000s recessions, in contrast to the National Financial Conditions Index (NFCI).