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A Semiparametric Generalized Ridge Estimator and Link with Model Averaging

Author

Listed:
  • Aman Ullah

    (Department of Economics, University of California Riverside)

  • Alan T.K. Wan

    (City University of Hong Kong)

  • Huansha Wang

    (University of California, Riverside)

  • Xinyu Zhang

    (Chinese Academy of Sciences)

  • Guohua Zou

    (Chinese Academy of Sciences)

Abstract

In recent years, the suggestion of combining models as an alternative to selecting a single model from a frequentist prospective has been advanced in a number of studies. In this paper, we propose a new semi-parametric estimator of regression coe¢ cients, which is in the form of a feasible generalized ridge estimator by Hoerl and Kennard (1970b) but with di¤erent biasing factors. We prove that the generalized ridge estimator is algebraically identical to the model average estimator. Further, the biasing factors that determine the properties of both the generalized ridge and semi-parametric estimators are directly linked to the weights used in model averaging. These are interesting results for the interpretations and applications of both semi-parametric and ridge estimators. Furthermore, we demonstrate that these estimators based on model averaging weights can have properties superior to the well-known feasible generalized ridge estimator in a large region of the parameter space. Two empirical examples are presented.

Suggested Citation

  • Aman Ullah & Alan T.K. Wan & Huansha Wang & Xinyu Zhang & Guohua Zou, 2014. "A Semiparametric Generalized Ridge Estimator and Link with Model Averaging," Working Papers 201412, University of California at Riverside, Department of Economics.
    • Handle: RePEc:ucr:wpaper:201412
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