Arbeitspapier

Predicting recessions in Germany with boosted regression trees

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. We then use receiver operating characteristic (ROC) curves to study the accuracy of forecasts. Results show that the short-term interest rate and the term spread are important leading indicators, but also that the stock market has some predictive value. The recession probability is a nonlinear function of these leading indicators. The BRT approach also helps to recover how the recession probability depends on the interactions of 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. We also study how the shape of a forecaster's utility function affects the optimal choice of a cutoff value above which the estimated recession prob- ability should be interpreted as a signal of a recession. The BRT approach shows a competitive out-of-sample performance compared to popular Pro- bit approaches.

Sprache
Englisch

Erschienen in
Series: DEP (Socioeconomics) Discussion Papers - Macroeconomics and Finance Series ; No. 5/2015

Klassifikation
Wirtschaft
Model Evaluation, Validation, and Selection
Forecasting Models; Simulation Methods
Business Fluctuations; Cycles
Prices, Business Fluctuations, and Cycles: Forecasting and Simulation: Models and Applications
Thema
Recession forecasting
Boosting
Regression trees
ROC curves

Ereignis
Geistige Schöpfung
(wer)
Döpke, Jörg
Fritsche, Ulrich
Pierdzioch, Christian
Ereignis
Veröffentlichung
(wer)
Hamburg University, Department Socioeconomics
(wo)
Hamburg
(wann)
2015

Handle
Letzte Aktualisierung
20.09.2024, 08:25 MESZ

Datenpartner

Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.

Objekttyp

  • Arbeitspapier

Beteiligte

  • Döpke, Jörg
  • Fritsche, Ulrich
  • Pierdzioch, Christian
  • Hamburg University, Department Socioeconomics

Entstanden

  • 2015

Ähnliche Objekte (12)