In this article, we introduce lassopack, a suite of programs for regularized regression in Stata. lassopack implements lasso, square-root lasso, elastic net, ridge regression, adaptive lasso, and postestimation ordinary least squares. The methods are suitable for the high-dimensional setting, where the number of predictors p may be large and possibly greater than the number of observations, n. We offer three approaches for selecting the penalization (“tuning”) parameters: information criteria (implemented in lasso2), K-fold cross-validation and h-step-ahead rolling cross-validation for cross-section, panel, and time-series data (cvlasso), and theory-driven (“rigorous” or plugin) penalization for the lasso and square-root lasso for cross-section and panel data (rlasso). We discuss the theoretical framework and practical considerations for each approach. We also present Monte Carlo results to compare the performances of the penalization approaches.

在本文中，我们介绍了lassopack，这是Stata中用于正规化回归的一组程序。lassopack实现套索，平方根套索，弹性网，岭回归，自适应套索和后估计普通最小二乘。该方法适用于高维设置，其中预测变量的数量p可能很大，并且可能大于观测值n。我们提供三种选择惩罚（“调整”）参数的方法：信息标准（在lasso2中实现），K折交叉验证和h-提前滚动交叉验证的横截面，面板和时间序列数据（cvlasso），并对套索和平方根套索的横截面和面板数据（rlasso）进行理论驱动（“严格”或插件）惩罚。我们讨论每种方法的理论框架和实际考虑因素。我们还提供了蒙特卡洛结果，以比较惩罚方法的性能。