Macroeconomic Forecasting Using Diffusion Indexes
研究如何用大量预测变量通过主成分分析提取少数扩散指数来预测宏观经济时间序列，基于近似动态因子模型，用215个预测变量对8个美国月度宏观序列进行模拟实时预测，发现该方法优于单变量自回归、小向量自回归和领先指标模型。
This article studies forecasting a macroeconomic time series variable using a large number of predictors. The predictors are summarized using a small number of indexes constructed by principal component analysis. An approximate dynamic factor model serves as the statistical framework for the estimation of the indexes and construction of the forecasts. The method is used to construct 6-, 12-, and 24-monthahead forecasts for eight monthly U.S. macroeconomic time series using 215 predictors in simulated real time from 1970 through 1998. During this sample period these new forecasts outperformed univariate autoregressions, small vector autoregressions, and leading indicator models.