【題目】Maximum?Likelihood?Estimation?and?Inference?for?High?Dimensional?Nonlinear?Factor?Models?with?Application?to?Factor?Augmented?Regressions
【主講人】Fa?Wang,?Assistant?Professor,?City?University?of?London
【時間】2019年11月6日下午13:30-14:30
【地點】中國人民大學明德主樓734

【內容簡介】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.

【主講人簡介】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.

數量經濟教研室
中國人民大學經濟學院
2019年10月