Publication Type
Working Paper
Version
publishedVersion
Publication Date
9-2024
Abstract
We develop a framework for quantifying delay propagation in airline networks by integrating structural modeling and machine learning methods to estimate causal effects. Using a comprehensive dataset on actual delays and a model-selection algorithm (elastic net), we estimate a weighted directed graph of delay propagation for each major airline in the United States and establish conditions under which the propagation coefficients are causal. These estimates enable a decomposition of airline performance into "luck" and "ability." Our findings indicate that luck accounts for approximately 38% of the performance difference between Delta and American Airlines in our data. Additionally, we leverage these estimates to analyze how network topology and other airline characteristics, such as aircraft fleet heterogeneity, influence expected delays.
Keywords
Airline Networks, Shock Propagation, Elastic Net
Discipline
Econometrics
Research Areas
Econometrics
First Page
1
Last Page
46
Publisher
Singapore Management University
Citation
DOU, Liyu; KASTL, Jakub; and LAZAREV, John.
Quantifying delay propagation in airline networks. (2024). 1-46.
Available at: https://ink.library.smu.edu.sg/soe_research/2795
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
