Forecasting Singapore's economy statistical learning and factor models

We evaluate the performance of the penalized vector autoregression (VAR), diffusion index (DI), and regression tree-based ensemble learning models to forecast Singapore's macroeconomy using high-dimensional data. Our dataset consists of 220 monthly time series that capture the economy of Singapore and 20 monthly times series that capture the global economic environment. We find that the penalized VAR model and the ensemble learning model give an outstanding performance in both short and long horizons. On the other hand, the performance of the DI model depends crucially on the methods to select the number of factors. In particular, a conventional selection method may overestimate the true number of factors and thus deteriorate the forecasting performance of the DI model. Additionally, the VAR and DI models may utilize different information in forecasting.