Modified panel data regression model and its applications to the airline industry: Modeling the load factor of Europe North and Europe Mid Atlantic flights

Abstract

Abstract This article conducts a stochastic analysis on the passenger load factor of the airline industry. Used to measure competence and performance of the airline, load factor is the percentage of seats filled by revenue passengers. It is considered a complex metric in the airline industry. Thus, it is affected by several dynamic factors. This paper applies advanced stochastic models to obtain the best fitted trend of load factor for Europe's North Atlantic (NA) and Mid Atlantic (MA) flights in the Association of European Airlines. The stochastic model's fit helps to forecast the load factor of flights within these geographical regions and evaluate the airline's demand and capacity management. The paper applies spectral density estimation and dynamic time effects panel data regression models on the monthly load factor flights of NA and MA from 1991 to 2013. The results show that the load factor has both periodic and serial correlations. Consequently, the author acknowledges that the use of an ordinal panel data model is inappropriate for a realistic econometric model of load factor. Therefore, to control the periodic correlation structure, the author modified the existing model was modified by introducing dynamic time effects. Moreover, to eradicate serial correlation, the author applied the Prais–Winsten methodology was applied to fit the model. In this econometric analysis, the study finds that AEA airlines have greater demand and capacity management for both NA and MA flights. In conclusion, this study prosperous in finding an effective and efficient dynamic time effects panel data regression model fit, which empowers engineers to forecast the load factor off AEA airlines.