New Distribution Theory for the Estimation of Structural Break Point in Mean
报告人： Xiaohu Wang, The Chinese University of Hong Kong
时间：2017-02-23 14:00 ~ 15:30
地点：Room 217, Guanghua Building 2
Based on the Girsanov theorem, this paper obtains the exact distribution of the maximum likelihood estimator of structural break point in a continuous time model. The exact distribution is asymmetric and tri-modal, indicating that the estimator is biased. These two properties are also found in the finite sample distribution of the least squares (LS) estimator of structural break point in the discrete time model, suggesting that the classical long-span asymptotic theory is inadequate. The paper then builds a continuous time approximation to the discrete time model and develops an in-fill asymptotic theory for the LS estimator. The in-fill asymptotic distribution is asymmetric and tri-modal and delivers good approximations to the finite sample distribution. To reduce the bias in the estimation of both the continuous time and the discrete time models, a simulation-based method based on the indirect estimation (IE) approach is proposed. Monte Carlo studies show that IE achieves substantial bias reductions.
About the Speaker:
Dr. Wang Xiaohu is an assistant professor in economics at the Chinese University of Hong Kong. His primary research interests are in econometric theory and financial econometrics. In particular, his research focuses on the estimation and inference of continuous diffusions with discrete-time observations, and of non-stationary discrete time series.