Macroeconomic forecasting in a multi‐country context
提出一种用于多国VAR模型的分层收缩方法，采用三种尺度混合正态先验，实证发现分层收缩（尤其是Horseshoe先验）对G7国家的通胀预测效果显著，且多国模型整体优于单国模型。
Abstract In this paper, we propose a hierarchical shrinkage approach for multi‐country VAR models. In implementation, we consider three different scale mixtures Normals priors and provide new theoretical results. Empirically, we examine how model specifications and prior choices affect the forecasting performance for GDP growth, inflation, and a short‐term interest rate for the G7 economies. We find that hierarchical shrinkage, particularly as implemented with the Horseshoe prior, is very useful in forecasting inflation. It also has the best density forecast performance for output growth and the interest rate. Multi‐country models generally improve on the forecast accuracy of single‐country models.