讲座主题：Inference for Large Dimensional Factor Models under General Missing Data Patterns

主讲嘉宾：王法（北京大学经济学院金融学系副教授）

学科方向：计量经济学

讲座时间：2025年5月9日（周五）下午16:00

讲座地点：经济学院407会议室

Abstract：Abstract: This paper establishes the inferential theory for the least squares estimation of large factor models with missing data. We propose a unified framework for asymptotic analysis of factor models that covers a wide range of missing patterns, including heterogenous random missing, selection on covariates/factors/loadings, block/staggered missing, mixed frequency and ragged edge. We establish the average convergence rates of the estimated factor space and loading space, the limit distributions of the estimated factors and loadings, as well as the limit distributions of the estimated average treatment effects and the parameter estimates in the factor-augmented regressions. These results allow us to impute the unbalanced panel appropriately or make inference for the heterogenous treatment effects. For computation, we can use the nuclear norm regularized estimator as the initial value for the EM algorithm and iterate until convergence. Empirically, we apply our method to test the average treatment effects of partisan alignment on grant allocation in UK.

嘉宾介绍：

王法，美国雪城大学经济学博士，现任北京大学经济学院金融学系副教授，博士生导师。研究领域为金融计量，因子模型和高维计量经济学，主讲时间序列、固定收益证券等课程。研究成果多次发表于Journal of Econometrics (4篇)和Econometric Reviews等期刊，还有多篇文章在二审，并多次担任计量经济学国际顶级期刊的审稿人，主持国家自然科学青年基金一项。