Bootstrap Inference for Group Factor Models
针对分组因子模型中共同因子的检验问题，提出一种简单的Bootstrap检验方法，避免显式估计典型相关性的偏差和方差，模拟实验显示其零拒绝率更接近名义水平。
Abstract Andreou et al. (2019) have proposed a test for common factors based on canonical correlations between factors estimated separately from each group. We propose a simple bootstrap test that avoids the need to estimate the bias and variance of the canonical correlations explicitly and provide high-level conditions for its validity. We verify these conditions for a wild bootstrap scheme similar to the one proposed in Gonçalves and Perron (2014). Simulation experiments show that this bootstrap approach leads to null rejection rates closer to the nominal level in all of our designs compared to the asymptotic framework.