Forecasting with Bayesian Global Vector Autoregressive Models: A Comparison of Priors

Arbeitspapier

This paper puts forward a Bayesian version of the global vector autoregressive model (B-GVAR) that accommodates international linkages across countries in a system of vector autoregressions. We compare the predictive performance of B-GVAR models for the one- and four-quarter ahead forecast horizon for standard macroeconomic variables (real GDP, inflation, the real exchange rate and interest rates). Our results show that taking international linkages into account improves forecasts of inflation, real GDP and the real exchange rate, while for interest rates forecasts of univariate benchmark models remain difficult to beat. Our Bayesian version of the GVAR model outperforms forecasts of the standard cointegrated VAR for practically all variables and at both forecast horizons. The comparison of prior elicitation strategies indicates that the use of the stochastic search variable selection (SSVS) prior tends to improve out-of-sample predictions systematically. This finding is confirmed by density forecast measures, for which the predictive ability of the SSVS prior is the best among all priors entertained for all variables at all forecasting horizons.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: Working Paper ; No. 189

Classification and Topics

• Classification: Wirtschaft ; Multiple or Simultaneous Equation Models: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models ; International Business Cycles ; Business Fluctuations; Cycles ; Economywide Country Studies: Latin America; Caribbean
• Subject: Forecasting with Bayesian Global Vector Autoregressive Models: A Comparison of Priors

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Cuaresma, Jesús Crespo ; Feldkircher, Martin ; Huber, Florian
• Event: Veröffentlichung
• (who): Oesterreichische Nationalbank (OeNB)
• (where): Vienna
• (when): 2014

Further information

• Handle: http://hdl.handle.net/10419/264781
• Last update: 10.03.2025, 11:45 AM CET