Time-varying parameter MIDAS models: Application to nowcasting US Real GDP
提出一种时变参数混合频率数据采样（TVP-MIDAS）框架，允许MIDAS权重和高频变量影响系数随时间变化，用于实时预测美国GDP，相比传统模型能更好捕捉下行风险。
We introduce a novel time-varying parameter mixed-data sampling (TVP-MIDAS) framework. Specifically, we allow both the MIDAS weights and the coefficients representing the overall impacts of the high-frequency variables to vary over time. This is done by introducing a class of linear parameterizations, which facilitate estimation in settings with a large number of high-frequency predictors. We demonstrate the usefulness of this framework via an application of nowcasting US GDP in real-time using monthly, weekly and daily predictors. The results show that the TVP-MIDAS models produce superior nowcasts, and are particularly effective in capturing the downside risk compared to their time-invariant counterparts.