Publication Type
Working Paper
Version
publishedVersion
Publication Date
1-2016
Abstract
Based on the Girsanov theorem, this paper first 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 seriously biased. These two properties are also found in the finite sample distribution of the least squares estimator of structural break point in the discrete time model. The paper then builds a continuous time approximation to the discrete time model and develops an in-fill asymptotic theory for the least squares estimator. The obtained in-fill asymptotic distribution is asymmetric and tri-modal and delivers good approximations to the finite sample distribution. In order to reduce the bias in the estimation of both the continuous time model and the discrete time model, a simulation-based method based on the indirect estimation approach is proposed. Monte Carlo studies show that the indirect estimation method achieves substantial bias reductions. However, since the binding function has a slope less than one, the variance of the indirect estimator is larger than that of the original estimator.
Keywords
Structural break, Bias reduction, Indirect estimation, Exact distribution, In-fill asymptotics
Discipline
Econometrics
Research Areas
Econometrics
Volume
01-2016
First Page
1
Last Page
47
Publisher
SMU Economics and Statistics Working Paper Series, No. 01-2016
City or Country
Singapore
Embargo Period
2-22-2016
Citation
JIANG, Liang; WANG, Xiaohu; and YU, Jun.
New Distribution Theory for the Estimation of Structural Break Point in Mean. (2016). 01-2016, 1-47.
Available at: https://ink.library.smu.edu.sg/soe_research/1782
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Comments
Published in Journal of Econometrics, 2018, 205 (1), 156-176. https://doi.org/10.1016/j.jeconom.2018.03.009