宏观经济数据因子模型中结构突变点的解耦
Disentangling Structural Breaks in Factor Models for Macroeconomic Data
AI总结：
本文提出基于投影的分解方法以区分因子模型中的方差突变与载荷突变，并应用于美国宏观数据，发现“大缓和”更应归因于因子方差下降超过70%而非载荷突变。
摘要：
我们提出了一种基于投影的分解方法，以解耦因子方差和因子载荷中的结构突变。该方法生成的检验统计量可与结构突变文献中常用的标准分布进行比较。由于宏观经济学中估计因子模型的标准方法对因子方差进行了归一化处理，因此无法区分因子方差的突变与因子载荷的突变。将我们的方法应用于美国宏观经济数据，我们发现“大缓和”更自然地体现为因子方差的突变，而非因子载荷的突变；这与现有方法形成对比，现有方法无法区分二者，因而将“大缓和”解释为因子载荷的结构突变。通过基于投影的分解，我们估计“大缓和”与总因子方差下降超过70%相关，这凸显了解耦因子结构中突变的重要性。
英文摘要：
We develop a projection-based decomposition to disentangle structural breaks in the factor variance and factor loadings. Our approach yields test statistics that can be compared against standard distributions commonly used in the structural break literature. Because standard methods for estimating factor models in macroeconomics normalize the factor variance, they do not distinguish between breaks of the factor variance and factor loadings. Applying our procedure to U.S. macroeconomic data, we find that the Great Moderation is more naturally accommodated as a break in the factor variance as opposed to a break in the factor loadings, in contrast to extant procedures which do not tell the two apart and thus interpret the Great Moderation as a structural break in the factor loadings. Through our projection-based decomposition, we estimate that the Great Moderation is associated with an over 70\% reduction in the total factor variance, highlighting the relevance of disentangling breaks in the factor structure.
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