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全球向量自回归模型的预测：贝叶斯方法AI 解读

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…

空间滤波、模型不确定性与欧洲收入收敛速度AI 解读

SUMMARY In this paper we put forward a Bayesian model averaging method aimed at performing inference under model uncertainty in the presence of potential spatial autocorrelation. The method uses spatial filtering in order to account for uncertainty in spatial linkages. Our procedure is applied to a dataset of income per capita growth and 50 potential determinants for 255 NUTS‐2 European regions. We show that ignoring uncertainty in the type of spatial weight matrix can have…