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讲座简介：
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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.
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