【题目】Maximum Likelihood Estimation and Inference for High Dimensional Nonlinear Factor Models with Application to Factor Augmented Regressions
【主讲人】Fa Wang, Assistant Professor, City University of London
【时间】2019年11月6日下午13:30-14:30
【地点】bat365在线官网登录明德主楼734

【内容简介】This paper reestablishes the main results in Bai (2003) and Bai and Ng (2006) for nonlinear 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 models, Logit, Probit, Tobit, Poisson and some other 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.

【主讲人简介】Fa Wang is an econometrician from Cass Business School at City University of London. He was awarded a PhD in Economics degree from Syracuse University in 2016. His main research interest includes panel data models, financial econometrics, and asset pricing. He has published papers in various top journals, such as Journal of Econometrics and Econometrics Reviews.

数量经济教研室
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2019年10月