Accounting for price endogeneity in airline itinerary choice models: An application to Continental U.S. markets
Laurie A. Garrow,
Matthew Higgins (),
Jeffrey P. Newman and
Transportation Research Part A: Policy and Practice, 2017, vol. 100, issue C, 228-246
Network planning models, which forecast the profitability of airline schedules, support many critical decisions, including equipment purchase decisions. Network planning models include an itinerary choice model that is used to allocate air total demand in a city pair to different itineraries. Multinomial logit (MNL) models are commonly used in practice and capture how individuals make trade-offs among different itinerary attributes; however, none that we are aware of account for price endogeneity. This study formulates an itinerary choice model that is consistent with those used by industry and corrects for price endogeneity using a control function that uses several types of instrumental variables. We estimate our model using a database of more than 10million passenger trips provided by the Airlines Reporting Corporation. Results based on Continental U.S. markets for May 2013 departures show that models that fail to account for price endogeneity overestimate customers’ value of time and result in biased price estimates and incorrect pricing recommendations. The size and comprehensiveness of our database allows us to estimate highly refined departure time of day preference curves that account for distance, direction of travel, number of time zones traversed, departure day of week and itinerary type (outbound, inbound or one-way). These time of day preference curves can be used by airlines, researchers, and government organizations in the evaluation of different policies such as congestion pricing.
Keywords: Air travel behavior; Itinerary choice; Price elasticity; Price endogeneity; Time of day preference (search for similar items in EconPapers)
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Working Paper: Accounting for Price Endogeneity in Airline Itinerary Choice Models: An Application to Continental U.S. Markets (2016)
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