Spectral density estimation of European airlines load factors for Europe-Middle East and Europe-Far East flights

[摘要] PurposeIn the airline industry the term load factor is defined as the percentage of seats filled by revenue passengers. The load factor is a metric that measures the airline’s capacity and demand management. This paper aimed to identify serial and periodic autocorrelation on the load factors of the Europe-Mid East and Europe-Far East airline flights. Identifying the autocorrelation structure is helpful to develop the best fitted forecasting model of the load factors.MethodsThe paper applies spectral density estimation to investigate the structure of serial and periodic autocorrelation on the load factors. Then the paper applied multivariate trend model to develop a forecasting model of the load factors of the regional flights. The multivariate trend model is fitted using the Prais–Winsten recursive autoregression methodology.ResultsThe primary analysis of the study identified that the airlines have better a demand than capacity management system for both the Europe-Mid East and Europe-Far East flights. The spectral density estimates showed that the load factors have both periodic and serial correlations for both regional flights. Therefore, in order to control the periodic autocorrelation, we introduce transcendental time functions as predictors of the load factor in the multivariate trend model. Finally, we build realistic and robust forecasting model of the load factors of the Europe-Mid East and Europe-Far East flights.ConclusionsThe econometric estimation results confirm that the load factors of the Europe-Mid East and Europe-Far East flights are both seasonal and differ between flights. The analysis implies that the load factor is still far from stable and stabilizing policies by airlines has so far not been successful. The AEA may therefore continuously focus on the stabilization and the improvement of the load in the industry.

[发布日期] 2015-04-16

[关键词] Airlines;Load factor;Spectral density estimation;Multivariate trend analysis