  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 accuracy, I employ a projection approach using
characteristics because it removes noise components in high-dimensional factor
analysis. By using the FPPC method, estimators of the factors and loadings have
faster rates of convergence than those of the conventional factor analysis.
Moreover, I propose an FPPC-based diffusion index forecasting model. The
limiting distribution of the parameter estimates and the rate of convergence
for forecast errors are obtained. Using U.S. bond market and macroeconomic
data, I demonstrate that the proposed model outperforms models based on
conventional principal component estimators. I also show that the proposed
model performs well among a large group of machine learning techniques in
forecasting excess bond returns.
