The asymptotic distribution of the linear instrumental variables (IV) estimator with empirically selected ridge regression penalty is characterized. The regularization tuning parameter is selected by splitting the observed data into training and test samples and becomes an estimated parameter that jointly converges with the parameters of interest. The asymptotic distribution is a nonstandard mixture distribution. Monte Carlo simulations show the asymptotic distribution captures the characteristics of the sampling distributions and when this ridge estimator performs better than two-stage least squares. An empirical application on returns to education data is presented.

中文翻译：

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使用训练和测试样本的岭回归估计量的渐近分布

表征了具有经验选择的岭回归惩罚的线性工具变量（IV）估计量的渐近分布。通过将观察到的数据分为训练样本和测试样本来选择正则化调整参数，并成为与目标参数共同收敛的估计参数。渐近分布是非标准的混合物分布。蒙特卡洛模拟显示渐近分布捕获了采样分布的特征，并且当该岭估计器的性能优于两阶段最小二乘法时。提出了对教育数据收益的实证应用。