Topic: Self-weighted LS estimation in nonlinear cointegrating regression

Lecturer: Professor,Qiying Wang, University of Sydney

Time: 15:00-16:30 p.m. October 27th, 2023

Venue: B321 Zhixin Building, Central Campus

Abstract: In this paper, we provide a new framework on self-weighted least squares estimation that is easy to apply for various nonlinear regression models with heteroscedasticity. In a nonlinear regression model where the regressors include nearly integrated arrays and stationary processes, it is shown that the WLS estimator has a mixed Gaussian limit and the corresponding studentized statistic converges to a standard

normal distribution. Such a WLS estimator is free of the memory parameter even when a fractional process is included in the regressors, and hence there is big advantage in applications.