Machine learning: Optimising forecasting for better decision making
About the course
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 complement and differ from AI?
ML applications across central bank functions
- Overview of key ML approach es: 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 A I to enhance inflation nowcasting?
Conclusion
Course summary and closing remarks
Induction to Machine learning
