This paper analyzes the estimation of econometric models by penalizing the sum of squares of the residuals with a factor that makes the model estimates approximate those that would be obtained when considering the possible simple regressions between the dependent variable of the econometric model and each of its independent variables. It is shown that the ridge estimator is a particular case of the penalized estimator obtained, which, upon analysis of its main characteristics, presents better properties than the ridge especially in reference to the individual boostrap inference of the coefficients of the model and the numerical stability of the estimates obtained. This improvement is due to the fact that instead of shrinking the estimator towards zero, the estimator shrinks towards the estimates of the coefficients of the simple regressions discussed above.

翻译：本文通过引入一个惩罚因子对残差平方和进行惩罚，使得模型估计量近似于在考虑计量经济模型中因变量与每个自变量之间可能的简单回归时所获得的估计量，从而分析了计量经济模型的估计问题。研究表明，岭估计量是所得惩罚估计量的一个特例，而该惩罚估计量在分析其主要特征后，表现出比岭估计量更优的性质，尤其体现在模型系数的个体自助法推断以及所得估计量的数值稳定性方面。这种改进源于该估计量并非将估计量向零收缩，而是将其向上述简单回归的系数估计值收缩。