Machine learning: Optimising forecasting for better decision making
About the course
Participants on this course will explore machine learning (ML) applications for central banks, focusing on the technology’s role in supporting decision-making with timely, data-driven insights. The training will address ML as a subset of AI and provide an overview of key methods before delving into its role within central bank forecasting. Key topics include how ML can be leveraged for monetary policymaking, a comparison of efficiency and effectiveness between ML models and traditional econometric methods, and a study of ML applications for nowcasting. This engaging course will equip participants with the essential knowledge to understand the complexities of ML.
Agenda
Welcome and context-setting
- Introduction, programme overview, and learning objectives
Introduction to machine learning for central banks
- What is machine learning (ML) and how does it differ from and complement AI?
- ML applications across central bank functions
- Overview of key ML approaches: supervised, unsupervised, and reinforcement learning
Leveraging ML for monetary policy
- Role of ML in supporting monetary policy decision-making with timely, data-driven insights
- Key applications of ML in monetary policy: forecasting and nowcasting, and economic, sentiment, and policy analysis
- Balancing predictive accuracy of ML-produced models with interpretability
Enhancing economic forecasting with ML
- Overview of ML algorithms for macroeconomic forecasting
- Efficiency and effectiveness: ML models as compared to traditional econometric methods
- Increasing model accuracy with ML and generative AI: considering large and complex data, alternative data sources, continuous analysis, and sudden and unexpected macroeconomic shocks
Nowcasting: Real-time inflation intelligence
- Brief introduction to inflation nowcasting: definition, purpose, and role in supporting monetary policy decision making
- Benefits to and limitations of inflation nowcasting with ML
- How are central banks are leveraging ML and generative AI to enhance inflation nowcasting?
Conclusion
- Course summary and closing remarks
Learning objectives
- Understand how ML can be applied across central bank functions
- Learn the role of ML in supporting decision-making
- Discuss the benefits and challenges of applying ML to forecasting
- Identify the differences between ML and traditional models
- Examine methods of inflation nowcasting with ML
Who should attend
Relevant titles and departments may include, but are not limited to:
- Principal/senior economists
- Heads of forecasting
- Research analysts
- Heads of monetary policy
- Policy advisors/analysts
- Heads of statistics
- Chief data/AI officers
- Data analysts
- Heads of information technology
- IT analysts
- Heads of innovation
- Digital transformation leads
- ML engineers
- Economics department
- Forecasting and policy modelling division
- Business cycle division
- Monetary policy department
- Stress-test modelling division
- Statistics department
- Data office
- Information technology department
- Innovation/digital transformation division
