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.

Additional Metadata
Keywords factor construction, forecasting, principal components
JEL Time-Series Models; Dynamic Quantile Regressions (jel C32), Forecasting and Other Model Applications (jel C53), Forecasting and Simulation (jel E17)
Persistent URL
Series Econometric Institute Research Papers
Journal Report / Econometric Institute, Erasmus University Rotterdam
Heij, C, van Dijk, D.J.C, & Groenen, P.J.F. (2006). Improved Construction of diffusion indexes for macroeconomic forecasting (No. EI 2006-03-REV). Report / Econometric Institute, Erasmus University Rotterdam. Retrieved from