提出新方法Post-Double-Autometrics，更好估计高维线性回归中的关键参数。
Estimation in high-dimensional linear regression: Post-Double-Autometrics as an alternative to Post-Double-Lasso
- 基于Autometrics思想改进变量选择流程
- 在有限样本下显著降低遗漏变量偏差
- 适合关注因果效应的经济学研究者
当线性回归模型包含大量协变量且目标是准确估计某一参数（如平均处理效应）时，Post-Double-Lasso已成为主流方法。然而，该方法在有限样本下可能面临严重的遗漏变量偏差。本文提出一种新方法——Post-Double-Autometrics，其基于Autometrics框架，实证结果显示该方法优于Post-Double-Lasso。在标准经济增长应用中，该方法为“贫困向富裕经济体收敛”这一假说提供了新的证据。
原文摘要 · Abstract (English)
Post-Double-Lasso is becoming the most popular method for estimating linear regression models with many covariates when the purpose is to obtain an accurate estimate of a parameter of interest, such as an average treatment effect. However, this method can suffer from substantial omitted variable bias in finite sample. We propose a new method called Post-Double-Autometrics, which is based on Autometrics, and show that this method outperforms Post-Double-Lasso. Its use in a standard application of economic growth sheds new light on the hypothesis of convergence from poor to rich economies.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。