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Confidence Sets for the Coefficients Vector of a Linear Regression Model with Nonspherical Disturbances

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  • Chaturvedi, Anoop
  • Hasegawa, Hikaru
  • Chaturvedi, Ajit
  • Shukla, Govind

Abstract

In this present paper, considering a linear regression model with nonspherical disturbances, improved confidence sets for the regression coefficients vector are developed using the Stein rule estimators. We derive the large-sample approximations for the coverage probabilities and the expected volumes of the confidence sets based on the feasible generalized least-squares estimator and the Stein rule estimator and discuss their ranking.

Suggested Citation

  • Chaturvedi, Anoop & Hasegawa, Hikaru & Chaturvedi, Ajit & Shukla, Govind, 1997. "Confidence Sets for the Coefficients Vector of a Linear Regression Model with Nonspherical Disturbances," Econometric Theory, Cambridge University Press, vol. 13(3), pages 406-429, June.
    • Handle: RePEc:cup:etheor:v:13:y:1997:i:03:p:406-429_00
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    Cited by:

    1. Chaturvedi, Anoop & Gupta, Suchita & Bhatti, M. Ishaq, 2012. "Confidence ellipsoids based on a general family of shrinkage estimators for a linear model with non-spherical disturbances," Journal of Multivariate Analysis, Elsevier, vol. 104(1), pages 140-158, February.
    2. Boot, Tom, 2023. "Joint inference based on Stein-type averaging estimators in the linear regression model," Journal of Econometrics, Elsevier, vol. 235(2), pages 1542-1563.

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