Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/251037 
Erscheinungsjahr: 
2021
Schriftenreihe/Nr.: 
KOF Working Papers No. 497
Verlag: 
ETH Zurich, KOF Swiss Economic Institute, Zurich
Zusammenfassung: 
This paper proposes a multi-level dynamic factor model to identify common components in output gap estimates. We pool multiple output gap estimates for 157 countries and decompose them into one global, eight regional, and 157 country-specific cycles. Our approach easily deals with mixed frequencies, ragged edges, and discontinuities in the underlying output gap estimates. To restrict the parameter space in the Bayesian state space model, we apply a stochastic search variable selection approach and base the prior inclusion probabilities on spatial information. Our results suggest that the global and the regional cycles explain a substantial proportion of the output gaps. On average, 18% of a country's output gap is attributable to the global cycle, 24% to the regional cycle, and 58% to the local cycle.
Schlagwörter: 
Multi-Level DFM
Bayesian State Space Model
Output Gap Decomposition
Model Combination
Business Cycles
Variable Selection
Spatial Prior
JEL: 
C11
C32
C52
F44
R11
Persistent Identifier der Erstveröffentlichung: 
Dokumentart: 
Working Paper

Datei(en):
Datei
Größe
1.85 MB





Publikationen in EconStor sind urheberrechtlich geschützt.