Bayesian High-Dimensional Time series models with applications to Macroeconomic and Financial data

This project focuses on the development of flexible Bayesian methods for modelling time series data. Specifically, we work on developing inference methodology from a Bayesian perspective, and then forecasting techniques for high dimensional time series data arising in economics and finance. For example, the vector of returns of portfolio constituents, or a vector of credit default swaps data for different nations.

Regarding the methodological issue, the focus of the project is on developing Bayesian methods specific to low-rank linear models, such as prior specification and efficient parameter estimation. Such models are expected to be well-suited to economics and finance data, as such data often possesses structure with dimension much lower than that what is actually observed. A Bayesian approach to low-rank linear modelling will enable adaptive determination of model parameters that are hard to estimate with classical methods.