CEMS PhD course on Bayesian Dynamic Modelling for Multivariate Time Series Analysis
By Prof. Mike West (Duke University)
May 29th-June 1st , 2017 at UCLouvain Louvain-La Neuve
This short-course covers principles and methodology of Bayesian dynamic modelling, with a main focus on methodology for multivariate time series analysis and forecasting. Following introductory conceptual and perspective development in univariate settings, the course works through a series of contexts of multivariate dynamic modelling for multiple time series. 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.
- Brief Overview of Bayesian Dynamic Modelling and Forecasting
- Multivariate Time Series: Common Components, Multivariate Volatility
- Dynamic Latent Factor Models
- Dynamic Graphical Models
- Simultaneous Dynamic Graphical Models
- Dynamic sparsity via latent thresholding– in economic and financial forecasting and decisions
More info on the content here.
- Members of the organizing institutions : free
- Academics & PhD students : 200 Euro
- Other : 500 Euro
- Please transfer the money to: Banque Belfius - 44, Boulevard Pachéco - B 1000 Bruxelles, Account number: 091-0015728-43,
Account holder: UCLouvain, IBAN : BE66.0910.0157.2843, BIC (SWIFT) : GKCCBEBB. With communication: 41.11000.031 + "Mike West"
Registration includes access to course, course material, coffee breaks and lunches