Forecasting With Nonspurious Factors in U.S. Macroeconomic Time Series
研究了因子载荷的结构变化会导致虚假因子并引发过拟合问题，提出一种估计非虚假因子的方法，发现美国132个宏观经济序列中存在大量结构变化，使用非虚假因子能显著提升样本外预测效果。
This study examines the practical implications of the fact that structural changes in factor loadings can produce spurious factors (or irrelevant factors) in forecasting exercises. These spurious factors can induce an overfitting problem in factor-augmented forecasting models. To address this concern, we propose a method to estimate nonspurious factors by identifying the set of response variables that have no structural changes in their factor loadings. Our theoretical results show that the obtained set may include a fraction of unstable response variables. However, the fraction is so small that the original factors are able to be identified and estimated consistently. Moreover, using this approach, we find that a significant portion of 132 U.S. macroeconomic time series have structural changes in their factor loadings. Although traditional principal components provide eight or more factors, there are significantly fewer nonspurious factors. The forecasts using the nonspurious factors can significantly improve out-of-sample performance.