Title（标题）：Factor modeling for high-dimensional time series

Speaker（汇报人）：Professor Qiwei Yao,' '&' ' GSM of Peking University ' '&' 'amp;The London School of Economics and Political Science, United Kingdom

Time（功夫）：2011年4月7日（周四）下午4:00－5:00' '&' '

Place（地址）：拉斯维加斯9888新楼217教室

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Abstract（提要）：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 number of factors 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 under two settings: (i) the sample size goes to infinity while the dimension of time series is fixed; and (ii) both the sample size and the dimension of time series go to infinity together. In particular, our estimators for zero-eigenvalues enjoy the faster convergence rates. Furthermore the estimation for the number of factors shows the so-called ' '&' 'quot;blessing of dimensionality' '&' 'quot; property at its clearest. Numerical illustration with both simulated and real data is also reported.

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