Course description

Bayesian Time Series Modelling & Forecasting

Introduction

Please note that this course will be revised before it is offered again.

This course covers principles an methodology of bayesian dynamic modelling in multivariate time series. Several key model developments and examples involve analysis, inference and forecasting in financial and econometric contexts, including Bayesian decision analysis overlaying modelling and computational methodology. Several examples are drawn from these areas, while others exemplify use of this range of models in other fields. The course includes recent modelling and methodological developments in multivariate time series and forecasting, and contacts current research frontiers

Course content

1. Multivariate Time Series and Multivariate Volatility
2. Multivariate Time Series: Time-Varying Vector AR and Related Models
3. Dynamic Latent Factor Models
4. Dynamic Graphical Models
5. Dynamic Dependence Network Models
6. Dynamic Simultaneous Graphical Models

Learning outcome knowledge

Course participants will gain :

  • Exposure to the basic ideas and approaches of Bayesian model-based time series analysis using key classes of dynamic models;
  • Exposure to the integration of Bayesian forecasting with decision analysis in financial applications;
  • An appreciation of the roles of analytic and simulation-based Bayesian computation in fitting and using multivariate time series models;
  • Awareness of texts, papers and software that will enable follow-on explorations and analysis; exposure to recent and current research topics, especially focused on scale-up of models and methods to higherdimensional series, and dynamic sparsity modelling in time series and forecasting.

Exam organisation

  • Written assignment: 100%