ABSTRACT

This paper tackles the mixed-frequency modelling problem from a new perspective. Instead of drawing upon the common distributed lag polynomial model, we use a transfer function representation to develop a new type of models, named TF-MIDAS. We derive the theoretical TF-MIDAS implied by the high-frequency VARMA family models for two common aggregation schemes, flow and stock. This exact correspondence leads to potential gains in terms of nowcasting and forecasting performance against the current alternatives. The estimation of the model proposed is also addressed via its state space equivalent form. A Monte Carlo simulation exercise confirms that TF-MIDAS beats U-MIDAS models (its natural competitor) in terms of out-of-sample nowcasting performance for several data generating high-frequency processes.

摘要

本文从一个新的角度解决了混合频率建模问题。我们没有利用常见的分布式滞后多项式模型，而是使用传递函数表示来开发一种新型模型，称为 TF-MIDAS。我们推导出由高频 VARMA 系列模型隐含的理论 TF-MIDAS，用于两种常见的聚合方案，流量和存量。这种精确的对应关系在针对当前替代方案的临近预报和预测性能方面带来了潜在收益。所提出模型的估计也通过其状态空间等效形式来解决。蒙特卡罗模拟练习证实，TF-MIDAS 在几个生成高频过程的数据的样本外临近预报性能方面击败了 U-MIDAS 模型（其自然竞争对手）。