In this paper, we discuss the approaches to nowcasting Japan’s GDP quarterly growth rates, comparing a variety of mixed frequency approaches including a bridge equation approach, Mixed-Data Sampling (MIDAS) and factor-augmented version of these approaches. In doing so, we examine the usefulness of a novel sparse principal component analysis (SPCA) approach in extracting factors from the dataset. We also discuss the usefulness of forecast combination, considering various ways to combine forecasts from models and surveys. Our findings are summarized as follows. First, some of the mixed frequency models discussed in this paper record out-of-sample performance superior to a naïve constant growth model. Second, albeit small, the SPCA approach of extracting factors improves predictive power compared with traditional principal component approach. Furthermore, we find that there is a gain from combining model forecasts and professional survey forecasts.

在本文中，我们讨论了现在预测日本GDP季度增长率的方法，并比较了各种混合频率方法，包括桥梁方程法，混合数据采样（MIDAS）和这些方法的因子增强版。在此过程中，我们研究了一种新颖的稀疏主成分分析（SPCA）方法在从数据集中提取因子的有用性。我们还讨论了预测组合的有用性，并考虑了将模型和调查的预测进行组合的各种方法。我们的发现总结如下。首先，本文讨论的某些混合频率模型记录的样本外性能优于单纯的恒定增长模型。其次，尽管很小，但与传统的主成分方法相比，提取因子的SPCA方法提高了预测能力。