New Distribution Theory for the Estimation of Structural Break Point in Mean
| 主讲人 |
Jun Yu |
简介 |
<div>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 ?nite sample distribution of the least squares (LS) estimator of structural break point in the discrete time model, suggesting 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-?ll asymptotic theory for the LS estimator. The in-?ll asymptotic distribution is asymmetric and tri-modal and delivers good approximations to the ?nite 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.</div> |
| 时间 |
2016-10-24(Monday)10:30-12:00 |
地点 |
N118, Econ Building |
| 讲座语言 |
English |
主办单位 |
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| 承办单位 |
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类型 |
独立讲座 |
| 联系人信息 |
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主持人 |
Qingliang Fan |
| 专题网站 |
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专题 |
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| 主讲人简介 |
<p><span style="font-size: small;">Professor of Finance and Economics, Singapore Management University</span></p>
<p><span style="color: rgb(0, 0, 255);"><span style="font-size: small;"><a href="/Upload/File/2016/10/20161013051749914.pdf"><span style="color: rgb(0, 0, 255);"><strong><u>Prof. Jun Yu's CV</u></strong></span></a></span></span></p> |
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