EMPIRICAL DECOMPOSITION AND ECONOMIC GROWTH FORECASTING

We use the empirical mode decomposition (EMD), specifically designed for decomposing nonstationary and nonlinear series by Huang et al. (1998), to disentangle and forecast Taiwan's real GDP growth. We find that the real GDP could be decomposed into six stationary and near-orthogonal intrinsic mode functions (IMFs), along with a nonlinear trend. Specifically, some IMFs have cyclical patterns similar to the Hodrick-Prescott filtered real GDP series under certain smoothing parameters. Based on the empirical stationarity and near-orthogonality of the IMFs, we can estimate and forecast these component series easily through simple time series models. In particular, our approach differs from the typical difference-first-then-fit recipes in that it retains all information and dynamic content of the original series instead of discarding partial information, which can be relevant for anticipating futures, due to differencing. By comparing with the other popular methodologies, linear or nonlinear, in predicting Taiwan's real GDP quarterly growth, our empirical results confirm the superior yet simple forecasting performance of the new approach. [PUBLICATION ABSTRACT]