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Application of Stein-Rule Estimation to Linear Regression Models with Some Missing Observations

Author

Listed:
  • H. Toutenburg

    (Universität München)

  • V. K. Srivastava

    (University of Lucknow)

  • C. Heumann

    (Universität München)

Abstract

The problem of estimating the coefficients in a linear regression model is considered when some of the response values are missing. The conventional Yates procedure employing least squares predictions for missing values does not lead to any improvement over the least squares estimator using complete observations only. However, if we use Stein-rule predictions, it is demonstrated that some improvement can be achieved. An unbiased estimator of the mean squared error matrix of the proposed estimator of coefficient vector is also presented. Some work on the application of the proposed estimation procedure to real-world data sets involving some discrete variables in the set of explanatory variables is under way and will be reported in future.

Suggested Citation

  • H. Toutenburg & V. K. Srivastava & C. Heumann, 2006. "Application of Stein-Rule Estimation to Linear Regression Models with Some Missing Observations," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 4(2), pages 14-24, July.
    • Handle: RePEc:spr:jqecon:v:4:y:2006:i:2:d:10.1007_bf03546445
    DOI: 10.1007/BF03546445
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    References listed on IDEAS

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    1. Carter, R.A.L. & Srivastava, M.S. & Srivastava, V.K. & Ullah, A., 1990. "Unbiased Estimation of the MSE Matrix of Stein-Rule Estimators, Confidence Ellipsoids, and Hypothesis Testing," Econometric Theory, Cambridge University Press, vol. 6(1), pages 63-74, March.
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