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August 13, 2026Journal of Time Series AnalysisOpen Access

Detecting Multiple Change Points in Linear Models With Heteroscedasticity

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Authors

LHLajos HorváthGRGregory RiceYZYuqian Zhao

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Overview

Randomized trial uncovers effective methods for detecting change points in regression models, indicating practical applications for asset pricing.

Key Points

  • The aim is to detect change points in linear regression model parameters under heteroscedasticity.
  • Analyzed asymptotic results for weighted functionals of CUSUM processes of model residuals.
  • Conducted simulation experiments to assess adapted change-point tests.
  • Proposed finite sample adjustments to testing procedures.
  • Methods effectively detected multiple change points in linear model parameters.
  • Controlled Type I error rate despite heteroscedasticity.
  • Illustrated practical application in asset pricing model testing.

Cite This Study

Horváth et al. (2026) studied this question.

synapsesocial.com/papers/6a7d769b2b0e0cff3f6401dehttps://doi.org/10.1111/jtsa.70076
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