用LASSO方法解决高维航空运输研究中的变量选择问题
Modelos Empiricos de Pos-Dupla Selecao por LASSO: Discussoes para Estudos do Transporte Aereo
- 采用LASSO进行正则化回归与变量筛选，处理高维数据
- 提出后双选择和后正则化模型，提升估计精度
- 适用于航空运营效率与燃油消耗的实证研究
本文探讨了基于LASSO（最小绝对收缩与选择算子）的正则化回归与模型选择方法。LASSO是高维计量经济学中主流的监督学习方法，可处理大规模数据及多重相关控制变量。文章分析了高维性对现代计量经济学的影响，以及稀疏性原则在正则化中的作用。重点考察了后双选择与后正则化模型，包括工具变量模型的变体。简要介绍了lassopack程序包的语法与使用示例，涵盖高维稀疏（HD）、高维稀疏工具变量（HDS）及含固定效应的模型组合。最后讨论该方法在航空运输研究中的应用潜力，以航空公司运营效率与飞机燃油消耗的实证分析为例。
原文摘要 · Abstract (English)
This paper presents and discusses forms of estimation by regularized regression and model selection using the LASSO method - Least Absolute Shrinkage and Selection Operator. LASSO is recognized as one of the main supervised learning methods applied to high-dimensional econometrics, allowing work with large volumes of data and multiple correlated controls. Conceptual issues related to the consequences of high dimensionality in modern econometrics and the principle of sparsity, which underpins regularization procedures, are addressed. The study examines the main post-double selection and post-regularization models, including variations applied to instrumental variable models. A brief description of the lassopack routine package, its syntaxes, and examples of HD, HDS (High-Dimension Sparse), and IV-HDS models, with combinations involving fixed effects estimators, is also presented. Finally, the potential application of the approach in research focused on air transport is discussed, with emphasis on an empirical study on the operational efficiency of airlines and aircraft fuel consumption.
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