A Semiparametric Generalized Ridge Estimator and Link with Model Averaging

Aman Ullah, Alan T.K. Wan, Huansha Wang, Xinyu Zhang and Guohua Zou
Additional contact information
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

No 201412, Working Papers from University of California at Riverside, Department of Economics

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.

Date: 2014-09
New Economics Papers: this item is included in nep-ecm
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https://economics.ucr.edu/repec/ucr/wpaper/201412.pdf First version, 2014 (application/pdf)

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Journal Article: A semiparametric generalized ridge estimator and link with model averaging (2017)
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