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Estimation of a Structural Break Point in Linear Regression Models

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  • Yaein Baek

Abstract

This study proposes a point estimator of the break location for a one-time structural break in linear regression models. If the break magnitude is small, the least-squares estimator of the break date has two modes at the ends of the finite sample period, regardless of the true break location. To solve this problem, I suggest an alternative estimator based on a modification of the least-squares objective function. The modified objective function incorporates estimation uncertainty that varies across potential break dates. The new break point estimator is consistent and has a unimodal finite sample distribution under small break magnitudes. A limit distribution is provided under an in-fill asymptotic framework. Monte Carlo simulation results suggest that the new estimator outperforms the least-squares estimator. I apply the method to estimate the break date in U.S. and U.K. stock return prediction models.

Suggested Citation

  • Yaein Baek, 2024. "Estimation of a Structural Break Point in Linear Regression Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 42(1), pages 95-108, January.
  • Handle: RePEc:taf:jnlbes:v:42:y:2024:i:1:p:95-108
    DOI: 10.1080/07350015.2022.2154777
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