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Time Series Analysis of Global Airline Passengers Transportation Industry


  • Radoslaw R. Okulski

    () (Technology Management, Economics, and Policy Program (TEMEP), Seoul National University)

  • Almas Heshmati

    () (Department of Food and Resource Economics, College of Life Sciences and Biotechnology, Korea University)


Technological developments and the global economic crisis are two types of developments that have affected the commercial airline industry in the last decade. This paper investigates time series analysis of the airline industry. The research has been conducted and is being presented, in a number of steps. First, a new, large database covering the global airline industry was assembled. Second, as part of the descriptive analysis of the industry and modeling a number of statistical tests are investigated. Third, the passenger airline transportation services models are estimated, to investigate their transportation entry and exit activities, as well as issues of heterogeneity and autocorrelation. Finally, we predict future developments within the industry. The empirical results are based on a large panel of 130 airlines observed monthly from January 2001 to April 2009. The airline produce two services of passenger and goods separately or jointly. The results show that specialized passenger companies cannot obtain sufficient revenues to stay at the market for long time. Airlines reduce costs through adding additional products. The worst performed joint service airlines¡¯ result of carrying passengers is much better than the result of specialized best practice airlines. In order to gain profit and improved survival rate, airlines specialized in passenger transportation must diversify their practice to carry both goods and passengers together.

Suggested Citation

  • Radoslaw R. Okulski & Almas Heshmati, 2010. "Time Series Analysis of Global Airline Passengers Transportation Industry," TEMEP Discussion Papers 201065, Seoul National University; Technology Management, Economics, and Policy Program (TEMEP), revised Jul 2010.
  • Handle: RePEc:snv:dp2009:201065

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    References listed on IDEAS

    1. Marcel Risch & Jorn Altmann, 2009. "Enabling Open Cloud Markets Through WS-Agreement Extensions," TEMEP Discussion Papers 200920, Seoul National University; Technology Management, Economics, and Policy Program (TEMEP), revised Oct 2009.
    2. Jorn Altmann & Juthasit Rohitratana, 2009. "Software Resource Management Considering the Interrelation between Explicit Cost, Energy Consumption, and Implicit Cost: A Decision Support Model for IT Managers," TEMEP Discussion Papers 200936, Seoul National University; Technology Management, Economics, and Policy Program (TEMEP), revised Nov 2009.
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    More about this item


    Airlines; time series analysis; ARIMA model; forecasts; information technology.;

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • D21 - Microeconomics - - Production and Organizations - - - Firm Behavior: Theory
    • L20 - Industrial Organization - - Firm Objectives, Organization, and Behavior - - - General
    • L93 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Air Transportation

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