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Modeling competition among airline itineraries

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  • Lurkin, Virginie
  • Garrow, Laurie A.
  • Higgins, Matthew J.
  • Newman, Jeffrey P.
  • Schyns, Michael

Abstract

Discrete choice models are commonly used to forecast the probability an airline passenger chooses a specific itinerary. In a prior study, we estimated an itinerary choice model based on a multinomial logit specification that corrected for price endogeneity. In this paper, we extend the analysis to include inter-itinerary competition along three dimensions: nonstop versus connecting level of service, carrier, and time of day using nested logit (NL) and ordered generalized extreme value (OGEV) models. To the best of our knowledge, these are the first NL and OGEV itinerary choice models to correct for price endogeneity. Despite the many structural changes that have occurred in the airline industry, our results are strikingly similar to models estimated more than a decade ago. These results are important because it suggests that customer preferences, on average, have been stable over time and are similar across distribution channels. The stability in inter-itinerary competition patterns provides an important practical implication for airlines, namely it reduces the need to frequently update the parameter estimates for these models.

Suggested Citation

  • Lurkin, Virginie & Garrow, Laurie A. & Higgins, Matthew J. & Newman, Jeffrey P. & Schyns, Michael, 2018. "Modeling competition among airline itineraries," Transportation Research Part A: Policy and Practice, Elsevier, vol. 113(C), pages 157-172.
  • Handle: RePEc:eee:transa:v:113:y:2018:i:c:p:157-172
    DOI: 10.1016/j.tra.2018.04.001
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    References listed on IDEAS

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    1. Jerry Hausman & Gregory Leonard & J. Douglas Zona, 1994. "Competitive Analysis with Differentiated Products," Annals of Economics and Statistics, GENES, issue 34, pages 143-157.
    2. repec:adr:anecst:y:1994:i:34:p:06 is not listed on IDEAS
    3. Small, Kenneth A, 1987. "A Discrete Choice Model for Ordered Alternatives," Econometrica, Econometric Society, vol. 55(2), pages 409-424, March.
    4. Coldren, Gregory M. & Koppelman, Frank S., 2005. "Modeling the competition among air-travel itinerary shares: GEV model development," Transportation Research Part A: Policy and Practice, Elsevier, vol. 39(4), pages 345-365, May.
    5. Lurkin, Virginie & Garrow, Laurie A. & Higgins, Matthew J. & Newman, Jeffrey P. & Schyns, Michael, 2017. "Accounting for price endogeneity in airline itinerary choice models: An application to Continental U.S. markets," Transportation Research Part A: Policy and Practice, Elsevier, vol. 100(C), pages 228-246.
    6. Jerry A. Hausman, 1996. "Valuation of New Goods under Perfect and Imperfect Competition," NBER Chapters, in: The Economics of New Goods, pages 207-248, National Bureau of Economic Research, Inc.
    7. Koppelman, Frank S. & Coldren, Gregory M. & Parker, Roger A., 2008. "Schedule delay impacts on air-travel itinerary demand," Transportation Research Part B: Methodological, Elsevier, vol. 42(3), pages 263-273, March.
    8. Wen, Chieh-Hua & Koppelman, Frank S., 2001. "The generalized nested logit model," Transportation Research Part B: Methodological, Elsevier, vol. 35(7), pages 627-641, August.
    9. Newman, Jeffrey P. & Lurkin, Virginie & Garrow, Laurie A., 2018. "Computational methods for estimating multinomial, nested, and cross-nested logit models that account for semi-aggregate data," Journal of choice modelling, Elsevier, vol. 26(C), pages 28-40.
    10. J. Miguel Villas-Boas & Russell S. Winer, 1999. "Endogeneity in Brand Choice Models," Management Science, INFORMS, vol. 45(10), pages 1324-1338, October.
    11. Coldren, Gregory M. & Koppelman, Frank S. & Kasturirangan, Krishnan & Mukherjee, Amit, 2003. "Modeling aggregate air-travel itinerary shares: logit model development at a major US airline," Journal of Air Transport Management, Elsevier, vol. 9(6), pages 361-369.
    12. Fosgerau, Mogens & McFadden, Daniel & Bierlaire, Michel, 2010. "Choice probability generating functions," MPRA Paper 24214, University Library of Munich, Germany.
    13. Guevara, C. Angelo, 2015. "Critical assessment of five methods to correct for endogeneity in discrete-choice models," Transportation Research Part A: Policy and Practice, Elsevier, vol. 82(C), pages 240-254.
    14. Douglas Staiger & James H. Stock, 1997. "Instrumental Variables Regression with Weak Instruments," Econometrica, Econometric Society, vol. 65(3), pages 557-586, May.
    15. Rivers, Douglas & Vuong, Quang H., 1988. "Limited information estimators and exogeneity tests for simultaneous probit models," Journal of Econometrics, Elsevier, vol. 39(3), pages 347-366, November.
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    Cited by:

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