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Forecasting with Global Vector Autoregressive Models: A Bayesian Approach

Crespo Cuaresma, Jesus and Feldkircher, Martin and Huber, Florian (2016) Forecasting with Global Vector Autoregressive Models: A Bayesian Approach. Journal of Applied Econometrics, 31 (7). pp. 1371-1391. ISSN 1099-1255

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This paper develops a Bayesian variant of global vector autoregressive (B-GVAR) models to forecast an international set of macroeconomic and financial variables. We propose a set of hierarchical priors and compare the predictive performance of B-GVAR models in terms of point and density forecasts for one-quarter-ahead and four-quarter-ahead forecast horizons. We find that forecasts can be improved by employing a global framework and hierarchical priors which induce country-specific degrees of shrinkage on the coefficients of the GVAR model. Forecasts from various B-GVAR specifications tend to outperform forecasts from a naive univariate model, a global model without shrinkage on the parameters and country-specific vector autoregressions

Item Type: Article
Additional Information: This is the peer reviewed version of the following article, which has been published in final form at http://dx.doi.org/10.1002/jae.2504. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.
Divisions: Departments > Volkswirtschaft > Makroökonomie
Forschungsinstitute > Human Capital and Development
Kompetenzzentren > Nachhaltigkeit
Version of the Document: Accepted for Publication
Depositing User: Jesus Crespo Cuaresma
Date Deposited: 07 Aug 2017 10:37
Last Modified: 13 Feb 2018 07:06
Related URLs:
FIDES Link: https://bach.wu.ac.at/d/research/results/73593/
URI: http://epub.wu.ac.at/id/eprint/4701


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