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Shrinkage Estimators for the Nonlinear Regression Model

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  • S. E. Ahmed

    (University of Regina)

  • Christopher Nicol

    (University of Regina)

Abstract

In this paper, we discuss various large sample estimation techniques in a nonlinear regression model. We propose estimators on the basis of preliminary tests of significance and James-Stein rule. The properties of these estimators are studied in the problem of estimating regression coefficients in the multiple regression model when it is a priori suspected that the coefficients may be restricted to a subspace. A simulation based on a demand for money model shows the superiority of the positive-part shrinkage estimator over a range of economically meaningful parameter values. This indicates that this estimator can be usefully employed in important practical situations.

Suggested Citation

  • S. E. Ahmed & Christopher Nicol, 2000. "Shrinkage Estimators for the Nonlinear Regression Model," Econometric Society World Congress 2000 Contributed Papers 1256, Econometric Society.
  • Handle: RePEc:ecm:wc2000:1256
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