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.

RIS

@ARTICLE{Crespo Cuaresma2016,
title = {Forecasting with Global Vector Autoregressive Models: A Bayesian Approach},
author = {Jesus Crespo Cuaresma and Martin Feldkircher and Florian Huber},
year = {2016},
doi = {http://dx.doi.org/10.1002/jae.2504},
volume = {31},
number = {7},
language = {EN},
pages = {1371-1391},
journal = {Journal of Applied Econometrics},
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. },
}