Predicting recessions with boosted regression trees

Link:
Autor/in:
Erscheinungsjahr:
2017
Medientyp:
Text
Schlagworte:
  • Recession
  • Yield curve
  • Term spread
  • Monetary Policy
  • Economic Growth
  • Exports
  • Recession
  • Yield curve
  • Term spread
  • Monetary Policy
  • Economic Growth
  • Exports
Beschreibung:
  • We use a machine-learning approach known as boosted regression trees (BRT) to reexamine the usefulness of selected leading indicators for predicting recessions. We estimate the BRT approach on German data and study the relative importance of the indicators and their marginal effects on the probability of a recession. Our results show that measures of the short-term interest rate and the term spread are important leading indicators. The recession probability is a nonlinear function of these leading indicators. The BRT approach also helps to uncover the way in which the recession probability depends on the interactions between the leading indicators. While the predictive power of the short-term interest rates has declined over time, the term spread and the stock market have gained in importance. The BRT approach shows a better out-of-sample performance than popular probit approaches. (C) 2017 International Institute of Forecasters.
Lizenz:
  • info:eu-repo/semantics/closedAccess
Quellsystem:
Forschungsinformationssystem der UHH

Interne Metadaten
Quelldatensatz
oai:www.edit.fis.uni-hamburg.de:publications/d17c1924-0a71-497c-bf62-3bc890af5f6b