Feasible Weighted Projected Principal Component Analysis for Factor Models with an Application to Bond Risk Premia
因子模型的可行加权投影主成分分析及债券风险溢价应用
I develop a feasible weighted projected principal component (FPPC) analysis for factor models in which observable characteristics partially explain the latent factors. This novel method provides more efficient and accurate estimators than existing methods. To increase estimation efficiency, I take into account both cross-sectional dependence and heteroskedasticity by using a consistent estimator of the inverse error covariance matrix as the weight matrix. To improve…