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Multiple Change-Point Detection in Linear Regression Models via U-Statistic Type Processes

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  • Burcu Kapar
  • William Pouliot

Abstract

Many procedures have been developed that are suited to testing for multiple changes in parameters of regression models which occur at unknown times. Most notably, Brown, Durbin and Evans [11] and Dufour [15], have developed or extended existing techniques, but said extensions lack power for detecting changes (cf. Kramer, Ploberger, Alt [24] and Pouliot [32] in the intercept parameter of linear regression models. Orasch [26] has developed a stochastic process that easily accommodates testing for many change-points that occur at unknown times. A slight modification of his process is suggested here which improves the power of statistics fashioned from it. These statistics are then used to construct tests to detect multiple changes in intercept in linear regression models. It is also shown here that this slightly altered process, when weighted by appropriately chosen functions, is sensitive to detection of multiple changes in intercept that occur both early and later on in the sample, while maintaining sensitivity to changes that occur in the middle of the sample.

Suggested Citation

  • Burcu Kapar & William Pouliot, 2013. "Multiple Change-Point Detection in Linear Regression Models via U-Statistic Type Processes," Discussion Papers 13-13, Department of Economics, University of Birmingham.
    • Handle: RePEc:bir:birmec:13-13
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    References listed on IDEAS

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    Cited by:

    1. Jose Olmo & William Pouliot, 2014. "Tests to Disentangle Breaks in Intercept from Slope in Linear Regression Models with Application to Management Performance in the Mutual Fund Industry," Discussion Papers 14-02, Department of Economics, University of Birmingham.

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    More about this item

    Keywords

    Structural Breaks; U-Statistics; Brownian Bridge; Linear Regression Model;
    All these keywords.

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C2 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables

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