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Abstract Details

Activity Number: 574
Type: Contributed
Date/Time: Wednesday, August 1, 2012 : 2:00 PM to 3:50 PM
Sponsor: Business and Economic Statistics Section
Abstract - #306138
Title: A New Algorithm for Multiple Change-points estimation in Time Series
Author(s): Chun-Yip Yau*+ and Ngai Hang Chan and Rongmao Zhang
Companies: Chinese University of Hong Kong and Chinese University of Hong Kong and Zhejiang University
Address: LSB 110, Shatin, N.T., , Hong Kong
Keywords: change-point ; non-stationary time series ; LASSO ; LARS

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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