Nowcasting from cross‐sectionally dependent panels
构建了一个混合频率面板数据模型，用于多国经济变量的即时预测，通过允许异质性系数和多因子误差结构处理截面相依性，在GDP增长和通胀预测中优于标准基准模型。
Summary This paper builds a mixed‐frequency panel data model for nowcasting economic variables across many countries. The model extends the mixed‐frequency panel vector autoregression (MF‐PVAR) to allow for heterogeneous coefficients and a multifactor error structure to model cross‐sectional dependence. We propose a modified common correlated effects (CCE) estimation technique which performs well in simulations. The model is applied in two distinct settings: nowcasting gross domestic product (GDP) growth for a pool of advanced and emerging economies and nowcasting inflation across many European countries. Our method is capable of beating standard benchmark models and can produce updated nowcasts whenever data releases occur in any country in the panel.