Factor Modelling for Time Series: A Dimension-Reduction Approach
发布者：系统管理员发布时间：2010-12-13浏览次数：2120
| 报告题目： | Factor Modelling for Time Series: A Dimension-Reduction Approach |
| 报 告 人： | 姚琦伟教授 |
| | 伦敦政治经济学院统计系教授、主任、我校客座教授、金融统计研究所所长 |
| 报告时间： | 2010年12月21日下午2:30-4:00 |
| 报告地点： | 九龙湖数学系第一报告厅 |
| 相关介绍： | Following a brief survey on the factor models for multiple time series in econometrics, we introduce a statistical approach from the viewpoint of dimension reduction. Our method can handle nonstationary factors. However under stationary settings, the inference is simple in the sense that the estimation for both the factor dimension and the loadings is resolved by an eigenanlysis for a non-negative definite matrix, and is therefore applicable when the dimension of time series is in the order of a few thousands. Asymptotic properties of the proposed method are investigated. In particular, our estimators for zero-eigenvalues enjoy the faster convergence rates even when the dimension goes to infinity together with the sample size. Numerical illustration with both simulated and real data will also be reported. |
