Machine Learning in the Aviation Industry and the Potential of Using Air Traﬃc As a Real-Time Indicator of GDP

Norwegian School of Economics Bergen, Fall 2020 Machine learning in the aviation industry and the potential of using air traffic as a real-time indicator of GDP A study of how useful machine learning is to predict Norwegian air traffic and investigating the causal relationship between air traffic and GDP Andrea Madeleine Lohne and Nejira Skrbo Supervisor: Håkon Otneim Master thesis, Economics and Business Administration Major: Business Analytics and Business Analysis and Performance Management NORWEGIAN SCHOOL OF ECONOMICS This thesis was written as a part of the Master of Science in Economics and Business Administration at NHH. Please note that neither the institution nor the examiners are responsible – through the approval of this thesis – for the theories and methods used, or results and conclusions drawn in this work. i Acknowledgements This master thesis was written as a part of the MSc in Economics and Business Administration program at the Norwegian School of Economics (NHH), with majors in Business Analytics and Business Analysis and Performance Management. The process of writing this thesis has been rewarding and educational, although at times challenging. Firstly, we would like to thank our supervisor Håkon Otneim for providing us with constructive guidance and quick feedback throughout the process. Although we have never met in person due to the ongoing pandemic, he has always been available on short notice. Secondly, we would like to thank Helge Eidsnes at Avinor for providing us with valuable insight into the Norwegian aviation market. Also, a great thanks to Torolf Holte at Avinor for providing us with data. Lastly, we will thank each other for a great semester. Norwegian School of Economics Bergen, December 2020 Your name here Your name here ii Abstract Travel by air is an essential part of both the Norwegian society and its infrastructure, where Norway has one of the highest number of flights per capita in Europe. Nonetheless, the aviation industry is characterized by high uncertainty, with the Covid-19 pandemic being the most recent one. This thesis has sought to investigate the use of machine learning in the Norwegian aviation industry and how the number of air passengers potentially can be used as a real-time indicator of GDP. Therefore, the thesis has been divided into two parts. The first part has aimed to use machine learning to predict the number of domestic and total passengers per capita in Norway. More precisely, we applied the methods OLS, elastic net, and random forest. The purpose of the second part has been to investigate the causal relationship between air passengers and GDP by conducting a strict linear Granger causality test. We particularly questioned whether air passengers could be used as a real-time indicator of GDP. The findings suggest that machine learning is applicable for predicting the number of air passengers per capita in Norway, where elastic net yield the best results. In relation to the second part of the thesis, the findings reveal a causal relationship running from air passengers to GDP. Consequently, we find that there is a potential of using the number of air passengers as a real-time indicator of GDP in Norway. Keywords – Machine learning, the Norwegian aviation industry, economic growth, causality, real-time indicators Contents iii Contents 1 Introduction 1 1.1 Background . .1 1.2 Purpose and Research Question . .3 1.3 Structure . .4 2 The Norwegian Aviation Industry 6 2.1 Development in Air Traffic Volume . .6 2.2 Market Participants . .7 2.2.1 Airlines . .8 2.2.2 Airport operators . .8 2.3 Profitability . .9 2.4 Public Service Obligation . .9 2.5 Machine Learning in the Aviation Industry . 10 I Predicting Air Traffic in Norway Using Machine Learning Methods 11 3 Literature Review 12 3.1 Factors Affecting Air Passenger Traffic . 13 3.1.1 Alternative price indicators . 13 3.1.2 Air passenger traffic and gross domestic product . 13 3.1.3 Demographics . 14 3.2 Other Drivers . 14 4 Data 15 4.1 Dependent Variables . 15 4.2 Predictors . 16 4.2.1 Gross domestic product . 16 4.2.2 Price index . 16 4.2.3 World jet fuel price . 17 4.2.4 Unemployment rate . 18 4.2.5 Real effective exchange rate . 18 4.2.6 Tourism . 18 4.2.7 Number of departures . 19 4.2.8 Air passenger tax . 19 4.2.9 Other possible predictors . 19 5 Methodology 21 5.1 Clarifications . 22 5.2 The Bias-Variance Trade-Off . 22 5.3 Time Series Cross-Validation . 24 5.4 Machine Learning Methods . 25 5.4.1 Ordinary least squares . 25 5.4.2 Elastic net . 26 5.4.3 Random forest . 27 iv Contents 6 Analysis 29 6.1 Descriptive Statistics . 29 6.2 Performance . 32 6.3 Air Passenger Predictions . 33 6.3.1 Domestic air passengers per capita . 33 6.3.2 Total air passengers per capita . 34 6.4 Conclusion of Predictions . 35 7 Discussion 36 7.1 Discussion of Predictions . 36 7.1.1 Domestic passengers . 36 7.1.2 Total passengers . 37 7.1.3 Comparing domestic and total passengers . 37 7.2 Discussion of Predictors . 37 7.3 Value Creation of the Predictions . 40 7.4 Prediction Weaknesses . 42 7.5 Recommendations for Future Research . 42 II The Causal Relationship Between Air Traffic and GDP in Norway 44 8 Air Passenger Traffic and GDP 45 8.1 Introduction . 45 8.2 Air Passenger Traffic and Gross Domestic Product . 46 8.3 Literature Review . 47 8.4 Methodology . 48 8.4.1 Granger causality . 48 8.4.2 Stationarity . 49 8.4.3 The vector autoregressive model . 49 8.5 Analysis . 50 8.6 Discussion . 51 8.7 Recommendations for Future Research . 52 9 Conclusion 54 References 56 Appendix 61 A1 Backcasts of Price Index . 61 A1.1 Domestic passengers . 61 A1.2 Total passengers . 61 A2 Variables . 62 A3 MSE as a Function of λ ........................... 62 A3.1 Domestic passengers . 62 A3.2 Total passengers . 63 A4 Descriptive Statistics . 63 A5 Predictions . 64 A5.1 Quarterly domestic predictions . 64 Contents v A5.2 Quarterly total predictions . 64 A6 Packages in R . 65 A6.1 References . 65 vi List of Figures List of Figures 2.1 Development in the number of air passengers in Norway from 2002 to 2019 (Avinor, 2020) . .6 2.2 Development in the number of flights from 2002 to 2019 (Avinor, 2020) .7 4.1 Crude oil and jet fuel prices (U.S. Energy Information Administration, 2020) 17 5.1 Rolling window time series cross-validation . 25 6.1 Quarterly passengers per capita . 30 6.2 Seasonal plots . 30 6.3 Seasonally adjusted quarterly passengers per capita . 31 6.4 Quarterly predictions of domestic air passengers per capita . 33 6.5 Quarterly predictions of total air passengers per capita . 34 7.1 Variable importance plots . 38 8.1 The relationship between air passengers and GDP . 46 8.2 Development in GDP and air passengers per capita in Norway from 2002 to 2019 . 47 A1.1 Backcasts for 2002 to 2006 domestic price index . 61 A1.2 Backcasts for 2002 to 2006 total price index . 61 A3.1 MSE as a function of λ given α = 0.1 . 62 A3.2 MSE as a function of λ given α = 0.9 . 63 List of Tables vii List of Tables 6.1 Set-up of the training set . 29 6.2 Performance domestic passengers . 32 6.3 Performance total passengers . 32 7.1 Elastic net coefficients total passengers . 39 A2.1 Variable names . 62 A4.1 Descriptive statistics . 63 A5.1 Predictions of quarterly domestic passengers per capita . 64 A5.2 Predictions of quarterly total passengers per capita . 64 A6.1 R-packages . 65 1 1 Introduction 1.1 Background Norway is an elongated country in the outskirts of Europe, with challenging topography and a scattered population (NOU 2019: 22, p. 37). Hence, air travel is a vital part of the Norwegian infrastructure and the only realistic transport alternative for specific areas, particularly for the northern and western parts. To provide an example, the distance by car between the two largest cities in Norway and Sweden are relatively similar, at 464 and 473 kilometers between Oslo and Bergen, and Stockholm and Gothenburg, respectively. However, the corresponding travel times by car are seven and five hours. Similarly, train travel time is only three hours between the two Swedish cities and almost seven hours for the Norwegian city pair. In.

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