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Board of Governors of the Federal Reserve System (US)
Finance and Economics Discussion Series
Bottom-up Leading Macroeconomic Indicators: An Application to Non-Financial Corporate Defaults using Machine Learning
This paper constructs a leading macroeconomic indicator from microeconomic data using recent machine learning techniques. Using tree-based methods, we estimate probabilities of default for publicly traded non-financial firms in the United States. We then use the cross-section of out-of-sample predicted default probabilities to construct a leading indicator of non-financial corporate health. The index predicts real economic outcomes such as GDP growth and employment up to eight quarters ahead. Impulse responses validate the interpretation of the index as a measure of financial stress.
Cite this item
Tyler Pike & Horacio Sapriza & Thomas Zimmermann, Bottom-up Leading Macroeconomic Indicators: An Application to Non-Financial Corporate Defaults using Machine Learning, Board of Governors of the Federal Reserve System (US), Finance and Economics Discussion Series 2019-070, 20 Sep 2019.
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
- E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
- G33 - Financial Economics - - Corporate Finance and Governance - - - Bankruptcy; Liquidation
Keywords: Corporate Default ; Early Warning Indicators ; Economic Activity ; Machine Learning
This item with handle RePEc:fip:fedgfe:2019-70
is also listed on EconPapers
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