Modelos Empiricos de Pos-Dupla Selecao por LASSO: Discussoes para Estudos do Transporte Aereo

TL;DR

This paper discusses regularized regression and model selection using LASSO, focusing on high-dimensional econometrics and applications to air transport efficiency and fuel consumption.

Contribution

It examines post-double selection and post-regularization models, including variations for instrumental variables, with practical examples and a software package implementation.

Findings

LASSO effectively handles high-dimensional data in econometrics.

Post-selection models improve estimation accuracy in complex settings.

Application to air transport demonstrates practical utility.

Abstract

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.