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On Guaranteed Sequential Change Point Detection for TAR(1)/ARCH(1) Process

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Information Technologies and Mathematical Modelling (ITMM 2014)

Abstract

The problem of guaranteed parameter estimation and change point detection of threshold autoregressive processes with conditional heteroscedasticity (TAR/ARCH) is considered. The parameters of the process are assumed to be unknown. A sequential procedure with guaranteed quality is proposed. The results of simulation are presented.

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References

  1. Tong, H.: On a Threshold Model. In: Chen, C.H. (ed.) Pattern Recognition and Signal Processing, pp. 101–141. Sijhoff & Noordhoff, Amsterdam (1978)

    Google Scholar 

  2. Rabemanajara, R., Zakoian, J.M.: Threshold ARCH Models and Asymmetries in Volatility. Journal of Applied Econometrics 8, 31–49 (1993)

    Article  Google Scholar 

  3. Zakoian, J.M.: Threshold Heteroskedastic Models. Journal of Econometric Dynamics and Control 18, 931–955 (1994)

    Article  Google Scholar 

  4. Liu, J., Li, W.K., Li, C.W.: On a Threshold Autoregression with Conditional Heteroscedastic Variances. Journal of Statistical Planning and Inference 62, 279–300 (1997)

    Article  MATH  MathSciNet  Google Scholar 

  5. Petrucelli, J.D., Woolford, S.W.: A Threshold AR(1) Model. J. Appl. Prob. 21, 270–286 (1984)

    Article  Google Scholar 

  6. Cline, D.B.H., Pu, H.: Stability and the Lyapounov Exponent of Threshold AR-ARCH Models. The Annals of Applied Probability 14, 1920–1949 (2004)

    Article  MATH  MathSciNet  Google Scholar 

  7. Lange, T., Rahbek, A., Jensen, S.T.: Estimation and Asymptotic Inference in the AR-ARCH Model. Econometric Reviews 30(I.2), 129–153 (2011)

    Article  MATH  MathSciNet  Google Scholar 

  8. Cline, D.B.H.: Evaluating the Lyapounov Exponent and Existence of Moments for Threshold AR-ARCH Models. Journal of Time Series Analysis 28(2), 241–260 (2006)

    Article  MathSciNet  Google Scholar 

  9. Hwang, S.Y., Kim, S., Lee, S.D., Basawa, I.V.: Generalized Least Squares Estimation for Explosive AR(1) Processes with Conditionally Geteroscedastic Errors. Statist. Probab. Lett. 77(I.13), 1439–1448 (2007)

    Article  MATH  MathSciNet  Google Scholar 

  10. Vald, A.: Sequential Analisys. Phismatgis, Moscow (1960)

    Google Scholar 

  11. Sergeeva, E.E., Vorobejchikov, S.E.: An Efficient Algorithm for Detecting a Change Point of Autoregressive Parameters of AR(p)/ARCH(q) Process. In: Proceedings of the 11th International Conference of Pattern Recognition and Information Processing, pp. 156–159 (2011)

    Google Scholar 

  12. Burkatovskaya, Y.B., Vorobeychikov, S.E.: Guaranteed Estimation of Parameters of Threshold Autoregressive Process with Conditional Heteroskedasticity. Tomsk State University Journal of Control and Computer Science 2(23), 32–41 (2013) (in Russian)

    Google Scholar 

  13. Page, E.S.: Continuous Inspection Schemes. Biometrica 42(1), 100–115 (1956)

    MathSciNet  Google Scholar 

  14. Hinkley, D.V.: Inference About Change-Point From Cumulative Sum-Tests. Biometrica 58(3), 509–523 (1971)

    Article  MATH  MathSciNet  Google Scholar 

  15. Lorden, G.: Procedures for Reacting to a Change in Distribution. Annals. Math. Statist. (42), 1897–(1971)

    Google Scholar 

  16. Kokoszka, P., Leipus, R.: Change-Point Estimation in ARCH. Bernoulli 6(3), 513–539 (2000)

    Article  MATH  MathSciNet  Google Scholar 

  17. Horvath, L., Liese, F.: Lp Estimators in ARCH Models. Journal of Statistical Planning and Inference 119, 277–309 (2004)

    Article  MATH  MathSciNet  Google Scholar 

  18. Aue, A., Horvath, L., Huskova, M., Kokoszka, P.: Change-Point Monitoring in Linear Models with Conditionally Heteroskedastic Errors. Econometrics J. 79, 373–403 (2006)

    Article  MathSciNet  Google Scholar 

  19. Cheng, T.-L.: An Efficient Algorithm for Estimating a Change-Point. Statistics and Probability Letters 79, 559–565 (2009)

    Article  MATH  MathSciNet  Google Scholar 

  20. Dupuy, J.F.: Detecting Change in a Hazard Regression Model with Rightcensoring. Journal of Statistical Planning and Inference 139, 1578–1586 (2009)

    Article  MATH  MathSciNet  Google Scholar 

  21. Mein, S., Tweedie, R.: Markov Chains and Stochastic Stability. Springer (1993)

    Google Scholar 

  22. Dmitrienko, A.A., Konev, V.V.: On Sequential Classification of Autoregressive Processes with Unknown Noise Variance. Problems of Information Transmission 31(4), 51–62 (1995) (in Russian)

    Google Scholar 

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Burkatovskaya, Y., Sergeeva, E., Vorobeychikov, S. (2014). On Guaranteed Sequential Change Point Detection for TAR(1)/ARCH(1) Process. In: Dudin, A., Nazarov, A., Yakupov, R., Gortsev, A. (eds) Information Technologies and Mathematical Modelling. ITMM 2014. Communications in Computer and Information Science, vol 487. Springer, Cham. https://doi.org/10.1007/978-3-319-13671-4_8

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  • DOI: https://doi.org/10.1007/978-3-319-13671-4_8

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-13670-7

  • Online ISBN: 978-3-319-13671-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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