Forecasting with Global Vector Autoregressive Models: a Bayesian Approach

Crespo Cuaresma J, Feldkircher M, & Huber F (2016). Forecasting with Global Vector Autoregressive Models: a Bayesian Approach. Journal of Applied Econometrics 31 (7): 1371-1391. DOI:10.1002/jae.2504.

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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 predicive 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
Research Programs: World Population (POP)
Depositing User: Luke Kirwan
Date Deposited: 15 Feb 2016 09:14
Last Modified: 21 Dec 2016 09:51
URI: http://pure.iiasa.ac.at/11918

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