Shrinkage Estimators for the Nonlinear Regression Model

S. E. Ahmed and Christopher Nicol
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S. E. Ahmed: University of Regina
Christopher Nicol: University of Regina

No 1256, Econometric Society World Congress 2000 Contributed Papers from Econometric Society

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.

Date: 2000-08-01
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