Dynamic Pricing, Advance Sales and Aggregate Demand Learning in Airlines


  • This paper is an extension of my doctoral thesis (Escobari [2008]). I am grateful to my committee members Li Gan, Hae-shin Hwang, Steven Puller, and Ximing Wu. I appreciate useful comments from Damian Damianov, Paan Jindapon, Qi Li, Joon Park, Thomas R. Saving, John K. Wald and Steven Wiggins. Two anonymous referees and the Editor provided insightful comments that helped improve the paper. Stephanie C. Reynolds provided outstanding assistance with the data. Financial support from the Private Enterprise Research Center at Texas A&M and the Bradley Foundation is gratefully appreciated.


This paper uses a unique U.S. airlines panel data set to study empirically the dynamic pricing of inventories with uncertain demand over a finite horizon. I estimate a dynamic pricing equation and a dynamic demand equation that jointly characterize the adjustment process between prices and sales as the flight date nears. I find that the price increases as the inventory decreases, and decreases as there is less time to sell. Consistent with aggregate demand learning and price adjustment, demand shocks have a positive and much larger effect on prices than the positive effect of anticipated sales.