Invitation to the Lee Kong Chian Professorship Inaugural Virtual Public Lecture by Professor Jun Yu

Machine learning in forecast combinations

Synopsis

Reliable forecasting based on observational evidence is useful in individual and firm decision-making, as well as governmental planning, regulation, and many other activities that rely on future economic projections. When structural instabilities and nonstationarities are present in data, a common approach in the econometrics literature is to combine forecasts from different econometric models. This technique is known as forecast combinations. In economics, structural instabilities and nonstationarities are widely expected as the economic environment changes constantly.

In recent years, machine learning (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.

In this lecture, we propose a novel framework that first allows machine learning methods to simultaneously consider multiple model specification, and then averages the outputs by the weights that are either pre-specified or estimated. In empirical applications to forecast key macroeconomic variables (such as GDP growth, the inflation rate, and the unemployment rate) and financial variables (such as the interest rate), we find that combining machine learning methods can produce more accurate forecasts than individual machine learning techniques and traditional econometric methods.

About the Speaker