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Erscheinungsjahr: 
2024
Quellenangabe: 
[Journal:] Review of Economic Analysis (REA) [ISSN:] 1973-3909 [Volume:] 16 [Issue:] 3 [Year:] 2024 [Pages:] 287-308
Verlag: 
International Centre for Economic Analysis (ICEA), Waterloo (Ontario)
Zusammenfassung: 
We study the out-of-sample forecasting performance of 32 exchange rates vis-a-vis the New Taiwan Dollar (NTD) in a 32-variable vector autoregression (VAR) model. The Bayesian approach is applied to the large-scale VAR model (LBVAR), and its (timevarying) forecasting performance is compared to the random-walk model in terms of both forecast accuracy and Giacomini-Rossi fluctuation tests. We find the random-walk model outperforms the LBVAR model in a short-run forecasting competition. Moreover, the dominance of a random-walk in the competition is stable over time. Accordingly, we do not find any benefit of incorporating a rich set of information in predicting the exchange rates vis-a-vis the NTD.
Schlagwörter: 
Bayesian Approach
Forecast Stability
Vector Autoregression
JEL: 
C53
E37
F37
Persistent Identifier der Erstveröffentlichung: 
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