摘要翻译：
本文介绍了STATA中用于正则化回归的一套程序lassopack。lassopack实现了套索、平方根套索、弹性网、岭回归、自适应套索和后估计OLS。这些方法适用于高维环境，其中预测器的数目$P$可能很大，并且可能大于观测的数目$N$。我们提供了三种不同的方法来选择惩罚(`调谐')参数：信息标准（在lasso2中实现），横截面、面板和时间序列数据的$K$-折叠交叉验证和$H$-步前滚动交叉验证(cvlasso)，以及横截面和面板数据的套索和平方根套索的理论驱动(`严格')惩罚(rlasso)。我们讨论了每种方法的理论框架和实际考虑。我们也给出蒙特卡罗结果来比较惩罚方法的性能。
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英文标题：
《lassopack: Model selection and prediction with regularized regression in
  Stata》
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作者：
Achim Ahrens, Christian B. Hansen, Mark E. Schaffer
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最新提交年份：
2019
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分类信息：

一级分类：Economics        经济学
二级分类：Econometrics        计量经济学
分类描述：Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论，微观计量经济学，宏观计量经济学，通过新方法发现的经济关系的实证内容，统计推论应用于经济数据的方法论方面。
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英文摘要：
  This article introduces lassopack, a suite of programs for regularized regression in Stata. lassopack implements lasso, square-root lasso, elastic net, ridge regression, adaptive lasso and post-estimation OLS. 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 different 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') 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 performance of the penalization approaches.
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PDF链接：
https://arxiv.org/pdf/1901.05397