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The Impact of COVID-19 on Airfares-A Machine Learning Counterfactual Analysis

Wozny, Florian (2022) The Impact of COVID-19 on Airfares-A Machine Learning Counterfactual Analysis. Econometrics, 10 (1), pp. 1-10. Multidisciplinary Digital Publishing Institute (MDPI). doi: 10.3390/econometrics10010008. ISSN 2225-1146.

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Official URL: https://www.mdpi.com/2225-1146/10/1/8

Abstract

This paper studies the performance of machine learning predictions for the counterfactual analysis of air transport. It is motivated by the dynamic and universally regulated international air transport market, where ex post policy evaluations usually lack counterfactual control scenarios. As an empirical example, this paper studies the impact of the COVID-19 pandemic on airfares in 2020 as the difference between predicted and actual airfares. Airfares are important from a policy makers’ perspective, as air transport is crucial for mobility. From a methodological point of view, airfares are also of particular interest given their dynamic character, which makes them challenging for prediction. This paper adopts a novel multi-step prediction technique with walk-forward validation to increase the transparency of the model’s predictive quality. For the analysis, the universe of worldwide airline bookings is combined with detailed airline information. The results show that machine learning with walk-forward validation is powerful for the counterfactual analysis of airfares.

Item URL in elib:https://elib.dlr.de/185310/
Document Type:Article
Title:The Impact of COVID-19 on Airfares-A Machine Learning Counterfactual Analysis
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Wozny, FlorianFlorian.Wozny (at) dlr.deUNSPECIFIED
Date:February 2022
Journal or Publication Title:Econometrics
Refereed publication:Yes
Open Access:Yes
Gold Open Access:Yes
In SCOPUS:Yes
In ISI Web of Science:Yes
Volume:10
DOI :10.3390/econometrics10010008
Page Range:pp. 1-10
Publisher:Multidisciplinary Digital Publishing Institute (MDPI)
ISSN:2225-1146
Status:Published
Keywords:machine learning; policy evaluation; aviation
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Transport
HGF - Program Themes:Transport System
DLR - Research area:Transport
DLR - Program:V VS - Verkehrssystem
DLR - Research theme (Project):V - Ökonver II
Location: Köln-Porz
Institutes and Institutions:Institute of Air Transport and Airport Research > Air Transport Economics
Deposited By: Wozny, Florian
Deposited On:03 Mar 2022 12:18
Last Modified:03 Mar 2022 12:18

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