This paper compares several forecasting methods using high-dimensional macroeconomic data from Japan. The diffusion index (DI) model has been widely used to incorporate the information contained in high-dimensional data for forecasting. We propose two selection methods of the number of latent factors in the DI model and compare the DI model with the vector autoregression (VAR) model whose parameters are estimated by lasso-type methods. We find that the DI model tends to be better for short-horizon forecasting, whereas the VAR model tends to be better for long-horizon forecasting. Moreover, we find that the information exploited for forecasting is similar between the DI and VAR models.

本文比较了几种使用日本高维宏观经济数据的预测方法。扩散指数（DI）模型已被广泛用于整合高维数据中包含的信息进行预测。我们提出了 DI 模型中潜在因子数量的两种选择方法，并将 DI 模型与向量自回归 (VAR) 模型进行了比较，后者的参数是通过套索类型的方法估计的。我们发现 DI 模型往往更适合短期预测，而 VAR 模型往往更适合长期预测。此外，我们发现用于预测的信息在 DI 和 VAR 模型之间是相似的。