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I develop a feasible weighted projected principal component (FPPC) analysisfor factor models in which observable characteristics partially explain thelatent factors. This novel method provides more efficient and accurateestimators than existing methods. To increase estimation efficiency, I takeinto account both cross-sectional dependence and heteroskedasticity by using aconsistent estimator of the inverse error covariance matrix as the weightmatrix. To improve accuracy, I employ a projection approach usingcharacteristics because it removes noise components in high-dimensional factoranalysis. By using the FPPC method, estimators of the factors and loadings havefaster rates of convergence than those of the conventional factor analysis.Moreover, I propose an FPPC-based diffusion index forecasting model. Thelimiting distribution of the parameter estimates and the rate of convergencefor forecast errors are obtained. Using U.S. bond market and macroeconomicdata, I demonstrate that the proposed model outperforms models based onconventional principal component estimators. I also show that the proposedmodel performs well among a large group of machine learning techniques inforecasting excess bond returns.

Feasible Weighted Projected Principal Component Analysis for Factor Models with an Application to Bond Risk Premia