【主讲人】 王法 副教授
【主持人】 高明 副教授
【报告题目】 Maximum Likelihood Estimation and Inference for High Dimensional Generalized Factor Models with Application to Factor-augmented Regressions

【报告摘要】This paper reestablishes the main results in Bai (2003) and Bai and Ng (2006) for generalized factor models, with slightly stronger conditions on the relative magnitude of N(number of subjects) and T(number of time periods). Convergence rates of the estimated factor space and loading space and asymptotic normality of the estimated factors and loadings are established under mild conditions that allow for linear, Logit, Probit, Tobit, Poisson and some other single-index nonlinear models. The probability density/mass function is allowed to vary across subjects and time, thus mixed models are also allowed for. For factor-augmented regressions, this paper establishes the limit distributions of the parameter estimates, the conditional mean, and the forecast when factors estimated from nonlinear/mixed data are used as proxies for the true factors.

【主讲人介绍】王法博士于2020年9月加入北京大学经济学院，担任金融学副教授。他于2016年获得美国锡拉丘兹大学经济学博士学位，曾任教于上海财经大学和伦敦卡斯商学院。他的研究领域是金融计量经济学，近期主要方向是非线性因子模型。