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The Cusum Test for Parameter Change in Time Series Models

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  • Sangyeol Lee
  • Jeongcheol Ha
  • Okyoung Na
  • Seongryong Na
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    Abstract

    In this paper, we consider the problem of testing for parameter changes in time series models based on a cusum test. Although the test procedure is well established for the mean and variance in time series models, a general parameter case has not been discussed in the literature. Therefore, here we develop a cusum test for parameter change in a more general framework. As an example, we consider the change of the parameters in a random coeefficient autoregressive (1) model and that of the autocovariances of a linear process. Simulation results are reported for illustration. Copyright 2003 Board of the Foundation of the Scandinavian Journal of Statistics..

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    Bibliographic Info

    Article provided by Danish Society for Theoretical Statistics & Finnish Statistical Society & Norwegian Statistical Association & Swedish Statistical Association in its journal Scandinavian Journal of Statistics.

    Volume (Year): 30 (2003)
    Issue (Month): 4 ()
    Pages: 781-796

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    Handle: RePEc:bla:scjsta:v:30:y:2003:i:4:p:781-796

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    Cited by:
    1. Na, Okyoung & Lee, Sangyeol, 2007. "Moving estimates test with time varying bandwidth," Journal of Multivariate Analysis, Elsevier, vol. 98(7), pages 1356-1375, August.
    2. Rafajlowicz, Ewaryst & Pawlak, Mirosław & Steland, Ansgar, 2004. "Non-parametric vertical box control chart for monitoring the mean," Technical Reports 2004,52, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    3. Ai Deng & Pierre Perron, 2006. "The Limit Distribution of the CUSUM of Squares Test Under General Mixing Conditions," Boston University - Department of Economics - Working Papers Series wp2006-004, Boston University - Department of Economics.
    4. Lee, Sangyeol & Na, Okyoung, 2005. "Test for parameter change in stochastic processes based on conditional least-squares estimator," Journal of Multivariate Analysis, Elsevier, vol. 93(2), pages 375-393, April.
    5. Sangyeol Lee & Okyoung Na, 2005. "Test for parameter change based on the estimator minimizing density-based divergence measures," Annals of the Institute of Statistical Mathematics, Springer, vol. 57(3), pages 553-573, September.
    6. Okyoung Na & Youngmi Lee & Sangyeol Lee, 2011. "Monitoring parameter change in time series models," Statistical Methods and Applications, Springer, vol. 20(2), pages 171-199, June.
    7. Ai Deng & Pierre Perron, 2005. "The Limit Distribution of the CUSUM of Square Test Under Genreal MIxing Conditions," Boston University - Department of Economics - Working Papers Series WP2005-046, Boston University - Department of Economics.
    8. Junmo Song & Sangyeol Lee, 2009. "Test for parameter change in discretely observed diffusion processes," Statistical Inference for Stochastic Processes, Springer, vol. 12(2), pages 165-183, June.
    9. Ana Badagian & Regina Kaiser & Daniel Pena, 2009. "Time series segmentation by Cusum, AutoSLEX and AutoPARM methods," Statistics and Econometrics Working Papers ws098025, Universidad Carlos III, Departamento de Estadística y Econometría.
    10. Lee, Sangyeol & Park, Siyun, 2009. "The monitoring test for the stability of regression models with nonstationary regressors," Economics Letters, Elsevier, vol. 105(3), pages 250-252, December.
    11. Lu, Xinhong & Maekawa, Koichi & Lee, Sangyeol, 2008. "The CUSUM of squares test for the stability of regression models with non-stationary regressors," Economics Letters, Elsevier, vol. 100(2), pages 234-237, August.
    12. Sangyeol Lee & Yoichi Nishiyama & Nakahiro Yoshida, 2006. "Test for Parameter Change in Diffusion Processes by Cusum Statistics Based on One-step Estimators," Annals of the Institute of Statistical Mathematics, Springer, vol. 58(2), pages 211-222, June.
    13. Koichi Maekawa & Sangyeol & Lee, 2004. "The Cusum Test for Parameter Change in Regression with ARCH Errors," Econometric Society 2004 Far Eastern Meetings 606, Econometric Society.

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