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MSE superiority of the unrestricted Stein-rule estimator in a regression model with a possible structural break

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  • Haifeng Xu
  • Akio Namba

Abstract

This paper investigates the estimation of a linear regression model with a possible structural break at a known point. We analytically derive the exact formulae of the MSE for the restricted SR and PSR estimators, which shrinks the OLS estimator toward the restriction of no structural break. We compare the MSE performance of restricted/unrestricted SR, PSR, and least squared estimators. We analytically show that the unrestricted SR estimator can have a smaller MSE than the restricted SR estimator even when the restriction is correct. Further, our numerical results show that the unrestricted PSR estimator has the best MSE performance over a wide region of parameter space. These results indicate that the use of the unrestricted PSR estimator is recommended even when a structural break may not exist, although the unrestricted PSR estimator does not take the possibility of no structural break into consideration.

Suggested Citation

  • Haifeng Xu & Akio Namba, 2024. "MSE superiority of the unrestricted Stein-rule estimator in a regression model with a possible structural break," Journal of Applied Statistics, Taylor & Francis Journals, vol. 51(15), pages 3233-3247, November.
    • Handle: RePEc:taf:japsta:v:51:y:2024:i:15:p:3233-3247
    DOI: 10.1080/02664763.2024.2346346
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