Stochastic Evaluation of Capacity and Demand Management of the Airline Industry

Abstract

In the airline industry, the term load factor defined as the percentage of seats filled by revenue passengers and is used to measure efficiency and performance. This metric evaluates the airlines capacity and demand management. This paper applies stochastic models to analyse the load factor of the Association European Airlines (AEA) for flights of Europe - North Africa and Europe- Sub Saharan Africa. The estimation result prevails that the airlines have better demand management in the flights of Europe- Sub Saharan Africa than in the flight of Europe - North Africa. However, the capacity management of the airlines is poor for both regional flights. The autocorrelation structures for the load factor for both regional flights have both periodic and serial correlations. Consequently, the use of ordinal panel data models is inappropriate to capture the necessary variation of the load factor of the regional flights. Therefore, in order to control for the periodic autocorrelation, the author introduces dynamic time effects panel data regression model. Furthermore, in order to eliminate serial correlation the author applies the Prais–Winsten methodology to fit the model. Finally, the author builds realistic and robust forecasting model of the load factor of the Europe- North Africa and Europe-Sub Saharan Africa flights.