Efficient estimation of factor models with time and cross-sectional dependence

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.