Does Joint Modelling of the World Economy Pay Off? Evaluating Global Forecasts from a Bayesian GVAR

Arbeitspapier

We analyze how modeling international dependencies improves forecasts for the global economy based on a Bayesian GVAR with SSVS prior and stochastic volatility. To analyze the source of performance gains, we decompose the predictive joint density into its marginals and a copula term capturing the dependence structure across countries. The GVAR outperforms forecasts based on country-specific models. This performance is solely driven by superior predictions for the dependence structure across countries, whereas the GVAR does not yield better predictive marginal densities. The relative performance gains of the GVAR model are particularly pronounced during volatile periods and for emerging economies.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: Working Paper ; No. 200

Classification and Topics

• Classification: Wirtschaft ; Forecasting Models; Simulation Methods ; Prices, Business Fluctuations, and Cycles: Forecasting and Simulation: Models and Applications ; Macroeconomic Aspects of International Trade and Finance: Forecasting and Simulation: Models and Applications
• Subject: GVAR ; global economy ; forecast evaluation ; log score ; copula

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Dovern, Jonas ; Feldkircher, Martin ; Huber, Florian
• Event: Veröffentlichung
• (who): Oesterreichische Nationalbank (OeNB)
• (where): Vienna
• (when): 2015

Further information

• Handle: http://hdl.handle.net/10419/264792
• Last update: 10.03.2025, 11:45 AM CET