Alternative Approaches to Long-term Industrial Forecasting: An Empirical Case Study of Export Demand in the South Korean Materials Sector

The primary purpose of economic forecasting is to support rational decision-making under uncertainty and to provide information for the efficient allocation of limited resources. Mid-term industrial forecasts are essential for formulating national economic strategies in response to structural changes, and model-based approaches must therefore specify variables and functional forms in ways that reflect these dynamics. As demographic shifts, advances in artificial intelligence, and global fragmentation reshape long-run economic fundamentals, forecasting models need to flexibly accommodate evolving relationships among variables while maintaining parsimony to mitigate the growing uncertainty of exogenous inputs. This study proposes time-varying coefficient models and their extension, functional-coefficient panel models, as alternatives that satisfy these requirements. Applying these models to long-term export demand forecasts for South Korea’s materials industries shows that income elasticities have shifted significantly over time and that models that account for structural change achieve superior predictive performance compared to traditional fixed-coefficient or short-term time-series models.