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Abstract Details
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Activity Number:
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574
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Type:
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Contributed
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Date/Time:
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Wednesday, August 1, 2012 : 2:00 PM to 3:50 PM
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Sponsor:
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Business and Economic Statistics Section
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| Abstract - #306138 |
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Title:
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A New Algorithm for Multiple Change-points estimation in Time Series
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Author(s):
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Chun-Yip Yau*+ and Ngai Hang Chan and Rongmao Zhang
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Companies:
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Chinese University of Hong Kong and Chinese University of Hong Kong and Zhejiang University
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Address:
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LSB 110, Shatin, N.T., , Hong Kong
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Keywords:
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change-point ;
non-stationary time series ;
LASSO ;
LARS
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Abstract:
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We consider the structural break autoregressive process where a time series has an unknown number of break-points, and the time series follows a stationary AR model in between any two break-points. It is well-known that the estimation of the locations of the break-points involves huge computational challenges. By reformulating the problem in a regression variable selection context, we propose in this paper a group least absolute shrinkage and selection operator (LASSO) procedure to estimate the number and the locations of the break-points, where the computation can be efficiently performed. Simululation studies are conducted to assess the finite sample performance.
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