A time-varying diffusion index forecasting model

This paper introduces a novel forecasting method based on a time-varying diffusion index model, where both factor loadings and regression coefficients are allowed to be time-varying. We first obtain the local principal component analysis (PCA) estimators for the latent factors and then estimate the factor augmented forecasting regression with time-varying coefficients nonparametrically. A feasible forecast is proposed by combining the estimated factors and the nonparametric estimators of coefficients. A set of Monte Carlo simulations demonstrates better performance of our proposed method than the standard diffusion index forecasters based on rolling windows. An empirical application of forecasting US macroeconomic variables is provided.