Recession Prediction with Optimal Use of Leading Indicators

Arbeitspapier

We use the gradient boosting estimation technique and the ROC curveto non-parametrically measure and exploit the maximal predictive powerof leading indicators for the future state of the business cycle. We de-velop novel procedures for finding the best performing transformationsof individual indicators, for combining them to form an optimal reces-sion prediction model and for assessing which predictors are contribut-ing in the model. Among our empirical findings with US data are thatthe predictive impact of various indicators is non-monotone and thatrecession predictions based on our nonparametric procedures clearlyoutperform the ones based on a conventional probit model.

Angaben zum Objekt

• Sprache: Englisch

Verweise und Beziehungen

• Erschienen in: Series: Discussion paper ; No. 125

Klassifikation und Themen

• Klassifikation: Wirtschaft ; Single Equation Models; Single Variables: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes ; Single Equation Models; Single Variables: Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities ; Forecasting Models; Simulation Methods ; Prices, Business Fluctuations, and Cycles: Forecasting and Simulation: Models and Applications
• Thema: gradient boosting ; leading indicators ; non-parametric esti-mation ; optimal binary prediction ; recession prediction

Beteiligte, Orts- und Zeitangaben

• Ereignis: Geistige Schöpfung
• (wer): Kauppi, Heikki
• Ereignis: Veröffentlichung
• (wer): Aboa Centre for Economics (ACE)
• (wo): Turku
• (wann): 2019

Weitere Informationen

• Handle: http://hdl.handle.net/10419/233340
• Letzte Aktualisierung: 10.03.2025, 11:43 MEZ