Arbeitspapier
Recession probabilities falling from the STARs
We follow the idea of exploiting cross-sectional information to improve recession probability forecasts by aggregating indicator-specific turning point predictions to obtain economy-wide recession probabilities. This stands in contrast to most of the relevant literature, which relies on an aggregated economic indicator to identify business cycle turning points. Using smooth transition regressions we compare the forecast performance of both approaches to business cycle dating in a comprehensive real-time forecasting exercise for recessions in the US. Moreover, we propose a novel smooth transition modelling framework which makes use of the interrelation between business and growth cycles to forecast recession probabilities. Our real-time out-of-sample forecast evaluation reveals that (i) using cross-sectional information is benficial to predicting recession probabilities, (ii) aggregating indicator-specific turning point forecasts clearly outperforms turning point predictions based on a single indicator and (iii) the proposed smooth transition framework is able to provide informative recession probability forecasts for up to three months in the US.
- ISBN
-
978-3-95729-675-7
- Sprache
-
Englisch
- Erschienen in
-
Series: Deutsche Bundesbank Discussion Paper ; No. 08/2020
- Klassifikation
-
Wirtschaft
Single Equation Models; Single Variables: Truncated and Censored Models; Switching Regression Models; Threshold Regression Models
Forecasting Models; Simulation Methods
Prices, Business Fluctuations, and Cycles: Forecasting and Simulation: Models and Applications
- Thema
-
Business cycles
forecasting
recessions
STAR models
turning points
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Eraslan, Sercan
Nöller, Marvin
- Ereignis
-
Veröffentlichung
- (wer)
-
Deutsche Bundesbank
- (wo)
-
Frankfurt a. M.
- (wann)
-
2020
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:43 MEZ
Datenpartner
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Arbeitspapier
Beteiligte
- Eraslan, Sercan
- Nöller, Marvin
- Deutsche Bundesbank
Entstanden
- 2020
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