Excerpt from 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

Disclaimer

This is an excerpt from the complete course description for the course. If you are an active student at BI, you can find the complete course descriptions with information on eg. learning goals, learning process, curriculum and exam at portal.bi.no. We reserve the right to make changes to this description.