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Estimation Of Change-Points In Linear And Nonlinear Time Series Models

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  • Ling, Shiqing

Abstract

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 → ∞. 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.

Suggested Citation

  • Ling, Shiqing, 2016. "Estimation Of Change-Points In Linear And Nonlinear Time Series Models," Econometric Theory, Cambridge University Press, vol. 32(2), pages 402-430, April.
    • Handle: RePEc:cup:etheor:v:32:y:2016:i:02:p:402-430_00
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    Cited by:

    1. Ting Fung Ma & Chun Yip Yau, 2016. "A pairwise likelihood-based approach for changepoint detection in multivariate time series models," Biometrika, Biometrika Trust, vol. 103(2), pages 409-421.
    2. Yunwei Cui & Rongning Wu & Qi Zheng, 2021. "Estimation of change‐point for a class of count time series models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 48(4), pages 1277-1313, December.

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