ESTIMATION OF CHANGE-POINTS IN LINEAR AND NONLINEAR TIME SERIES MODELS

被引:12
作者
Ling, Shiqing [1 ]
机构
[1] Hong Kong Univ Sci & Technol, Hong Kong, Hong Kong, Peoples R China
关键词
STRUCTURAL-CHANGE; PARAMETER INSTABILITY; TESTS; SEQUENCE; REGRESSIONS; INFERENCE; BREAKS;
D O I
10.1017/S0266466614000863
中图分类号
F [经济];
学科分类号
02 ;
摘要
This paper develops an asymptotic theory for estimated change-points in linear and nonlinear time series models. Based on a measurable 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, Annals of Statistics 15, 1321-1328). This provides a channel to connect our results with those in Picard (1985, Advances in Applied Probability 17, 841-867) and Bai, Lumsdaine, and Stock (1998, Review of Economic Studies 65, 395-432), where the magnitude of changed parameters depends on the sample size n and tends to zero as n -> infinity. The theory is applied for the self-weighted QMLE and the local QMLE of change-points in ARMA-GARCH/IGARCH models. A simulation study is carried out to evaluate the performance of these estimators in the finite sample.
引用
收藏
页码:402 / 430
页数:29
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