报告题目：On Structurally Grouped Approximate Factor Models

报告人： 涂云东教授（北京大学）

时间：2023年10月7日（周六）下午3：00开始

地点：太阳成集团tyc411(中国)有限公司-百度百科，海纳苑2幢206

摘要：This paper explores the group structure in large dimensional approximate factor models, which portrays homogeneous effects of the common factors on the individuals that fall into the same group. With the initial principle component estimates, we identify the unknown group structure by a combination of the agglomerative hierarchical clustering algorithm and an information criterion. The loadings and factors are then re-estimated conditional on the identified groups. Under some regularity conditions, we establish the consistency of the membership estimator as well as that of the group number estimator obtained from the information criterion. The new estimators under the group structure are shown to achieve efficiency gain compared to those obtained without this information. Numerical simulations and empirical applications demonstrate the nice finite sample performance of our proposed approach when group structure presents.

联系人：张荣茂（rmzhang@zju.edu.cn）

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