Forecasting with Bayesian Global Vector Autoregressions

Florian Huber (), Jesus Crespo-Cuaresma and Martin Feldkircher
Authors registered in the RePEc Author Service: Jesus Crespo Cuaresma

ERSA conference papers from European Regional Science Association

Abstract: 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 vec- tor 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.

Keywords: Global vector autoregressions; forecasting; prior sensitivity analysis (search for similar items in EconPapers)
JEL-codes: C32 E32 F44 O54 (search for similar items in EconPapers)
Date: 2014-11
New Economics Papers: this item is included in nep-ecm, nep-for, nep-mac and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (24)

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Persistent link: https://EconPapers.repec.org/RePEc:wiw:wiwrsa:ersa14p25

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