A Least Squares Method for Detecting Multiple Change Points in a Univariate Time Series

  • Kyu S. Hahn
  • Won Son
  • Hyungwon Choi
  • Johan Lim
Conference paper
Part of the ICSA Book Series in Statistics book series (ICSABSS)


Detecting and interpreting influential turning points in time series data is a routine research question in many disciplines of applied social science research. Here we propose a method for identifying important turning points in a univariate time series. The most rudimentary methods are inadequate when the researcher lacks preexisting expectations or hypotheses concerning where such turning points ought to exist. Other alternatives are computationally intensive and dependent on strict model assumptions. Our method is fused LASSO regression, a variant of regularized least squares method, providing a convenient alternative for estimation and inference of multiple change points under mild assumptions. We provide two examples to illustrate the method in social science applications. First, we assessed the validity of our method by reanalyzing the Greenback prices data used in (Willard et al. in Am Econ Rev 86:1001–1017, 1996). We next used the method to identify major change points in President Clinton’s approval ratings.


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Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Kyu S. Hahn
    • 1
  • Won Son
    • 2
  • Hyungwon Choi
    • 3
  • Johan Lim
    • 4
  1. 1.Department of CommunicationSeoul National UniversitySeoulSouth Korea
  2. 2.Bank of KoreaSeoulSouth Korea
  3. 3.Saw Swee Hock School of Public HealthNational University of SingaporeSingaporeSingapore
  4. 4.Department of StatisticsSeoul National UniversitySeoulSouth Korea

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