The Impact of COVID-19 on Airfares—A Machine Learning Counterfactual Analysis
Abstract
:1. Introduction
2. Institutional Background and Data
2.1. The Universal COVID-19 Policy Response
2.2. Airfares and Airline Information
2.3. Imputation and Feature Selection
2.4. Counterfactual Prediction and Walk-Forward Validation
3. Results
4. Conclusions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Appendix A
From | To | |
---|---|---|
Max depth | 1 | 25 |
Learning rate | 0.01 | 0.10 |
Min Child Weight | 1 | 10 |
Alpha | 5 | 70 |
Lambda | 0.04 | 0.40 |
Delta | 50 | 160 |
Gamma | 0 | 1 |
Number of Trees | 1500 |
2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 | 2020 | |
---|---|---|---|---|---|---|---|---|---|
Expected | 220.34 | 241.23 | 215.56 | 181.94 | 158.19 | 162.22 | 168.22 | 132.03 | 137.08 |
Baseline | 218.09 | 220.34 | 241.23 | 215.56 | 181.94 | 158.19 | 162.22 | 168.22 | 123.03 |
OLS | 192.11 | 243.38 | 242.00 | 183.44 | 170.01 | 132.09 | 163.11 | 150.87 | 155.55 |
XGBoost | 219.00 | 240.88 | 220.02 | 182.55 | 160.99 | 155.74 | 169.71 | 140.22 | 162.01 |
1 | XGBoost is an optimized distributed gradient-boosting framework and free of usage under https://github.com/dmlc/xgboost, (accessed on 7 July 2021). |
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Wozny, F. The Impact of COVID-19 on Airfares—A Machine Learning Counterfactual Analysis. Econometrics 2022, 10, 8. https://doi.org/10.3390/econometrics10010008
Wozny F. The Impact of COVID-19 on Airfares—A Machine Learning Counterfactual Analysis. Econometrics. 2022; 10(1):8. https://doi.org/10.3390/econometrics10010008
Chicago/Turabian StyleWozny, Florian. 2022. "The Impact of COVID-19 on Airfares—A Machine Learning Counterfactual Analysis" Econometrics 10, no. 1: 8. https://doi.org/10.3390/econometrics10010008
APA StyleWozny, F. (2022). The Impact of COVID-19 on Airfares—A Machine Learning Counterfactual Analysis. Econometrics, 10(1), 8. https://doi.org/10.3390/econometrics10010008