Financial Conditions and Economic Activity: Insights from Machine Learning

Michael Kiley

No 2020-095, Finance and Economics Discussion Series from Board of Governors of the Federal Reserve System (U.S.)

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 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).

Keywords: Big Data; Recession Prediction; Variable Selection (search for similar items in EconPapers)
JEL-codes: C55 E17 E44 E50 (search for similar items in EconPapers)
Pages: 40 p.
Date: 2020-11-16
New Economics Papers: this item is included in nep-big, nep-cmp and nep-mac
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:fip:fedgfe:2020-95

DOI: 10.17016/FEDS.2020.095

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