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


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Abstract

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.

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Publication's profile

Status of publication Published
Affiliation WU
Type of publication Journal article
Journal Journal of Applied Econometrics
Citation Index SSCI
WU Journalrating 2009 A
WU-Journal-Rating new FIN-A, VW-B, WH-B
Language English
Title Forecasting with Global Vector Autoregressive Models: A Bayesian Approach
Volume 31
Number 7
Year 2016
Page from 1371
Page to 1391
Reviewed? Y
DOI http://dx.doi.org/10.1002/jae.2504

Associations

People
Crespo Cuaresma, Jesus (Details)
Feldkircher, Martin (Former researcher)
Huber, Florian (Details)
Organization
Institute for Macroeconomics IN (Details)
Research Institute for Human Capital and Development FI (Details)
Competence Center for Sustainability Transformation and Responsibility WE (Details)
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