Forecasting With Dynamic Factor Models Estimated by Partial Least Squares

Dynamic factor models (DFMs) have found great success in nowcasting and short-term macroeconomic forecasting when incorporating large sets of predictive information. The factor loadings are typically estimated cross-sectionally with principal component analysis (PCA) or maximum likelihood (ML), which ignore whether the factors have predictive power. We suggest two novel alternative approaches using partial least squares to estimate large vector autoregressions (VARs) and DFMs, which take the dynamic dependencies better into account. Our Monte Carlo simulations and forecasting results for the Finnish GDP growth show that these methods generally perform on par with and under certain conditions better than the existing approaches.