Title: Financial conditions and economic activity: Insights from machine learning
Authors: Michael Kiley - Federal Reserve Board (United States) [presenting]
Abstract: 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 the 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).