On-Line Monitoring the Stationarity for Time Seires

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When analyzing time series an important issue is to decide whether the time series is stationary or nonstationary. Fixed sample statistical tests for that problem are well studies in the literature. In this paper we propose a moving variance ratio statistic to monitor the stationarity for normal sequence. Our Monte Carlo studies show that the proposed monitoring procedure has satisfactory test power and that the decision can often be made very early.

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687-691

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October 2013

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© 2014 Trans Tech Publications Ltd. All Rights Reserved

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[1] Maozu Lu, Advanced time series econometrics, People's publication of Shanghai, Shanghai, (1999).

Google Scholar

[2] D. Kwiatkowski, P.C.B. Phillips, P. Schmidt, Y. Shin, Testing the null hypothesis of stationarity against the alternative of a unit root: How sure are we that economic time series have a unit root? Journal of Econometrics, 54 (1992) 159-178.

DOI: 10.1016/0304-4076(92)90104-y

Google Scholar

[3] Z. Chen, Z. Tian, Modified procedures for change point monitoring in linear models, Mathematics and Computers in Simulations, 81 (2010) 62-75.

DOI: 10.1016/j.matcom.2010.06.021

Google Scholar

[4] Steland, A. Monitoring procedures to detect unit roots and stationarity, Econometric Theory, 23(2007): 1108-1135.

DOI: 10.1017/s0266466607070442

Google Scholar

[5] Z. Chen, Z. Tian, Online bootstrap monitoring of the stationarity for a class of heavy tailed random signals, Control Theory & Applications, 27(2010) 933-938.

Google Scholar

[6] Z. Chen, Z. Tian, C. Zhao, Monitoring persistence change in infinite variance observations, Journal of the Korean Statistical Society, 41 (2012) 61-73.

DOI: 10.1016/j.jkss.2011.06.001

Google Scholar

[7] Z. Chen, Z. Tian, Y. Wei, Monitoring change in persistence in linear time series, Statistics and Probability Letters, 80 (2010) 1520-1527.

DOI: 10.1016/j.spl.2010.06.004

Google Scholar

[8] Z. Chen, et al. Bootstrap testing multiple changes in persistence for a heavy-tailed sequence, Computational Statistics & Data Analysis, 56 (2012) 2303-2316.

DOI: 10.1016/j.csda.2012.01.011

Google Scholar