Forecasting with Global Vector Autoregressive Models: a Bayesian Approach
提出贝叶斯全局向量自回归模型，通过分层先验对系数进行收缩，用于预测国际宏观经济和金融变量，发现该模型在点预测和密度预测上优于朴素单变量模型和无收缩的全局模型。
Summary 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. Copyright © 2016 John Wiley & Sons, Ltd.