We perform a thorough comparative analysis of factor models and machine learning to forecast Japanese macroeconomic time series. Our main results can be summarized as follows. First, in many instances, factor models and machine learning perform better than the conventional AR model. Second, predictions made by machine learning methods perform particularly well for medium to long forecast horizons. Third, the success of machine learning mainly comes from the nonlinearity and interaction of variables, which suggests the importance of nonlinear structure in predicting the Japanese macroeconomic series. Fourth, the composite forecast of factor models and machine learning performs better than factor models or machine learning alone; and machine learning methods applied to common factors are found to be useful in the composite forecast.

我们对因子模型和机器学习进行了全面的比较分析，以预测日本的宏观经济时间序列。我们的主要结果可以总结如下。首先，在许多情况下，因素模型和机器学习的性能要优于传统的AR模型。其次，通过机器学习方法进行的预测在中长期预测范围内的表现尤其出色。第三，机器学习的成功主要来自变量的非线性和相互作用，这表明非线性结构在预测日本宏观经济序列中的重要性。第四，因子模型和机器学习的综合预测比单独的因子模型或机器学习表现更好；并发现应用于共同因素的机器学习方法在综合预测中很有用。