Search Results
Showing results 1 to 2 of approximately 2.
(refine search)
Working Paper
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.
Discussion Paper
Out-of-Sample Performance of Recession Probability Models
This note discusses the out-of-sample (OOS) performance of several probit models used to assess the likelihood that the U.S. economy will be in a recession within the following year.