论文标题
确定大维广义因子模型中的因素数量
Determining the number of factors in a large-dimensional generalised factor model
论文作者
论文摘要
本文提出了与严格因子模型相比，具有更轻松的假设的通用因子模型的因素数量的新估计值。在大型横截面$ n $和大时间维度$ t $的框架下，我们首先通过随机矩阵理论得出噪声方差的偏差校正估计器$ \ hatσ^2 _*$。然后，我们基于$ \ hatσ^2 _*$构建三个信息标准，进一步提出了对因子数量的一致估计器。最后，模拟和实际数据分析表明，我们提出的估计更准确，并避免在某些现有作品中高估。
This paper proposes new estimators of the number of factors for a generalised factor model with more relaxed assumptions than the strict factor model. Under the framework of large cross-sections $N$ and large time dimensions $T$, we first derive the bias-corrected estimator $\hat σ^2_*$ of the noise variance in a generalised factor model by random matrix theory. Then we construct three information criteria based on $\hat σ^2_*$, further propose the consistent estimators of the number of factors. Finally, simulations and real data analysis illustrate that our proposed estimations are more accurate and avoid the overestimation in some existing works.
