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Prof Jun Yu delivers the School of Economics’ inaugural virtual public lecture, titled “Machine learning in forecast combinations”
Professor Jun Yu from SMU School of Economics spoke at an inaugural
virtual public lecture titled “Machine learning in forecast
combinations” to an audience of more than 70 via Zoom on 8 December
2020.
Why is economic forecasting important? Reliable economic forecasts, or accurate predictions about the direction of the economy, serve to help individuals, households, policymakers and firms make sound decisions that could lead to growth, employment and inflation. However, as Niels Bohr, a Nobel laureate in Physics once said, “Making predictions is very difficult, especially about the future.” Today, reliable economic forecasts can be made with a combination of techniques including econometric methods and machine learning.
Tackling this topic at an inaugural virtual public lecture titled “Machine learning in forecast combinations”, Professor Jun Yu from SMU School of Economics spoke to an audience of more than 70 via Zoom on Tuesday, 8 December 2020.
Prof Yu, who is Lee Kong Chian (LKC) Professor of Economics and Finance, asked: “Why do nonlinear econometric and nonparametric models largely fail in generating reliable economic forecasts?” Quoting statistician George Box, who said “all models are wrong, but some are useful”, Prof Yu elaborated that economic activities typically involve many economic agents, making it both an art and a science to build a good econometric model. By testing the validity of economic theories, such models could generate more reliable economic forecasts...
Prof Yu elaborated on machine learning (ML) as the scientific study of algorithms and statistical models used by computer systems to effectively perform a specific task without using explicit instructions, relying on patterns and inference instead. In recent years, ML methods have found successful applications in predicting economic activities, especially when the underlying relationship linking response and explanatory variables is complicated. However, ML methods typically assume stability in the underlying relationship. Hence, existing ML methods may not be suitable for making economic forecasting when data involve structural instabilities and nonstationarities.
Source: Singapore Management University
