A Testing Procedure for Determining the Number of Factors in Approximate Factor Models With Large Datasets
提出一种新的检验方法，用于估计大数据因子模型中的因子数量，该方法对截面和时间依赖具有稳健性，并通过蒙特卡洛模拟验证其有效性。
Abstract The paradigm of a factor model is very appealing and has been used extensively in economic analyses. Underlying the factor model is the idea that a large number of economic variables can be adequately modeled by a small number of indicator variables. Throughout this extensive research activity on large dimensional factor models a major preoccupation has been the development of tools for determining the number of factors needed for modeling. This article provides an alternative method to information criteria as a tool for estimating the number of factors in large dimensional factor models. The new method is robust to considerable cross-sectional and temporal dependence. The theoretical properties of the method are explored and an extensive Monte Carlo study is undertaken. Results are favorable for the new method and suggest that it is a reasonable alternative to existing methods. Keywords: : Factor modelsLarge sample covariance matrixMaximum eigenvalue