Improved Construction of diffusion indexes for macroeconomic forecasting

This article proposes a modified method for the construction of diffusion indexes in macroeconomic forecasting using principal component regres- sion. The method aims to maximize the amount of variance of the origi- nal predictor variables retained by the diffusion indexes, by matching the data windows used for constructing the principal components and for es- timating the diffusion index models. The method is applied to construct forecasts of eight monthly US macroeconomic time series, using the data set of Stock and Watson (2002a). The results show that the proposed method leads, on average, to simpler models with smaller forecast errors than previously used methods.