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Abstract Details
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Activity Number:
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173
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Type:
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Contributed
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Date/Time:
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Monday, July 30, 2012 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Nonparametric Statistics
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| Abstract - #305097 |
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Title:
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A New Stationarity Test of Time Series in Time Domain
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Author(s):
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Lei Jin*+ and Suojin Wang and Haiyan Wang
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Companies:
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McNeese State University and Texas A&M University and Kansas State University
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Address:
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Box 92340, Lake Charles, LA, 70609, United States
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Keywords:
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auto-covariance ;
stationarity ;
time series ;
Walsh functions
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Abstract:
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In this paper, we proposed a new test to verify second-order stationarity of a time series. To develop the test, a sequence of systematic samples are defined based on Walsh functions. The test is to detect whether there is a non-constant covariance structure over time, by checking the deviation from the auto-covariances on a sequence of systematic samples to the auto-covariances of the whole time series. The asymptotic distribution of these deviations are obtained. Interestingly, these deviations of different systematic samples are asymptotically independent. A data driven method is used to combine the deviations for estimating auto-covariances with different lags in systematic samples. The null asymptotic distribution is given and critical values of the test are tabulated. An simulation study illustrates the validity of the asymptotic result and finite sample properties.
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