Nonlinear Forecasting Analysis Using Diffusion Indexes: An Application to Japan
扩展了Stock和Watson的扩散指数预测方法，利用主成分分析从大量日本数据中提取因子，发现非线性结构证据，并比较了线性和非线性扩散指数预测与传统时间序列预测的表现。
This paper extends the diffusion index (DI) forecast approach of Stock and When the number of series is large, a two-step procedure based on the principal components method is useful since it allows the wide variety of the nonlinearity in the factors. The factors extracted from a large Japanese data suggest some evidence of nonlinear structure. Furthermore, both the linear and nonlinear DI forecasts in Japan outperform traditional time series forecasts, while the linear DI forecast, in most cases, performs as well as the nonlinear DI forecast.