Efficient estimation of factor models with time and cross‐sectional dependence
研究了在协方差矩阵可分离假设下，同时处理时间和截面相依性的大维因子模型的有效估计方法，推导了估计量的渐近分布，并通过模拟和实际数据（如Lee-Carter模型和预期寿命预测）展示了其优于主成分估计的有限样本表现。
Summary This paper studies the efficient estimation of large‐dimensional factor models with both time and cross‐sectional dependence assuming ( N , T ) separability of the covariance matrix. The asymptotic distribution of the estimator of the factor and factor‐loading space under factor stationarity is derived and compared to that of the principal component (PC) estimator. The paper also considers the case when factors exhibit a unit root. We provide feasible estimators and show in a simulation study that they are more efficient than the PC estimator in finite samples. In application, the estimation procedure is employed to estimate the Lee–Carter model and life expectancy is forecast. The Dutch gender gap is explored and the relationship between life expectancy and the level of economic development is examined in a cross‐country comparison.