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主讲人 |
Shiqing Ling |
简介 |
<p><span style="font-size: small;"> <span style="font-family: 'Times New Roman', serif;">This paper first develops a general theory for estimating change-points in a general class of linear and nonlinear time series models. Based on a general objective function, it is shown that the estimated change-point converges weakly to the location of the maxima of a double-sided random walk and other estimated parameters are asymptotically normal. When the magnitude $d$ of changed parameters is small, it is shown that the limiting distribution can be approximated by the known distribution as in Yao (1987). This provides a channel to connect our results with those in Picard (1985) and Bai, Lumsdaine and Stock (1998), where the magnitude of changed parameters depends on the sample size $n$ and tends to zero as $n\to \infty$. We then focus on the self-weighted QMLE and the local QMLE of structure-change ARMA-GARCH/IGARCH models. The limiting distribution of the estimated change-point and its approximating distribution are obtained. Some simulation results are reported.</span></span></p>
<pre style="background-position: initial initial; background-repeat: initial initial;"><span lang="EN-US" style="font-family: 'Times New Roman', serif;"><o:p></o:p></span></pre> |
|
主讲人简介 |
<p><span style="font-size: small;"><span style="font-family: Arial;"> <span lang="EN-US">Department of Mathematics, Hong Kong University of Science and </span>Technology, Hong Kong</span></span></p>
<p><a href="/Upload/File/2016/3/20160311024110528.pdf">Upload/File/2016/3/20160311024110528.pdf</a></p> |
期数 |
厦门大学高级计量经济学与统计学系列讲座2016春季学期第四讲（总第78讲） |
