Notice of RetractionAirline demand forecast based on panel data model

Notice of Retraction

After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.

We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.

The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.

Airline demand forecast is a very important task for air companies to operate an existing airline or open a new airline. In this paper we introduce panel data model to forecast airline demand that gives consideration to both advantages of time series method and cross sectional regression method, which takes the specific characteristic of each individual airline into account. We construct four demand forecasting models by classifying Flying Range and Ticket Price and get function expression for each model. We find that when Flying Range is less than 1000 km and the Ticket Price is lower than 1000 Y, the Airline Demand is mainly subject to Ground Traffic and the Airline Demand of current period is probably affected by the one of prior period, otherwise, both independent variables of Gross Region Product and Ground Traffic have significant positive effects to Airline Demand. Lastly we use the constructed models to forecast some airline demands in 2006 and the results show that the models are well predictable and satisfactory.