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

该项目的重点是开发灵活的贝叶斯方法，用于模拟时间序列数据。具体来说，我们的工作是从贝叶斯的角度开发推理方法，然后预测技术的高维时间序列数据中出现的经济和金融。例如，投资组合成分的收益向量，或不同国家的信用违约掉期数据向量。关于方法问题，本项目的重点是开发针对低秩线性模型的贝叶斯方法，如事先指定和有效的参数估计。这些模型预计将非常适合经济和金融数据，因为这些数据通常具有比实际观察到的维度低得多的结构。低秩线性建模的贝叶斯方法将能够自适应地确定难以用经典方法估计的模型参数。

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.