AI 中文总结
研究动态因子模型中因子载荷的推断问题，提出用HAR推断和子抽样程序校正载荷均方误差，考虑协方差矩阵和因子估计的不确定性，通过美国各州经济收敛实证分析说明结果相关性。
AI 中文摘要
动态因子模型（DFMs）常用于宏观经济和/或金融变量大系统的实证分析中以降维。在此背景下，常用主成分（PC）提取共同潜在因子及其载荷，其在一般条件下是一致且渐近正态的。因此，对因子载荷的推断常基于其渐近分布，用HAC估计量一致估计载荷的极限协方差矩阵。本文分析了构建置信区间和检验估计的PC载荷时有限样本渐近近似的性能。结果表明，当横截面维度不够大时，这种近似会受到严重影响。我们提出使用HAR推断和子抽样程序来校正载荷的均方误差，分别考虑与协方差矩阵估计和因子估计相关的不确定性。美国各州经济收敛的实证分析说明了结果的相关性。
英文摘要
Dynamic Factor Models (DFMs) are popular to reduce dimensionality being customary in the empirical analysis of large systems of macroeconomic and/or financial variables. In this context, the common underlying factors and their loadings are often extracted using Principal Components (PC), which are consistent and asymptotically normal under very general conditions. Consequently, inference on the factor loadings, which is crucial for the correct interpretation of the underlying factors, is often based on their asymptotic distribution with the limit covariance matrix of the loadings consistently estimated using HAC estimators. In this paper, we analyse the performance of the finite sample asymptotic approximation when constructing confidence intervals and testing about estimated PC loadings. We show that this approximation is seriously affected when the cross-sectional dimension is not large enough. We propose using HAR inference and a subsampling procedure to correct the MSE of the loadings to take into account the uncertainty associated with the estimation of the covariance matrix and of the factors, respectively. The relevance of the results is illustrated in an empirical analysis of economic convergence among the US states.
