Forecasting International Air Transport Arrivals using Monthly Seasonal Indices: Evidence at Entebbe International Airport

Abstract

The study models international passenger air transport arrivals in SubSaharan Africa using evidence at Entebbe International Airport. Approach was quantitative analysis of monthly trends by generating monthly seasonal index model and using the model to make monthly forecasts one year ahead. Monthly data from 1998 to 2012 were used to describe and test characteristics in arrivals using Microsoft Excel 2007. Modelling of the monthly seasonal indexes was by using a 12- month centred moving average. Findings revealed serial correlation and seasonality in the trends January, July, August, October, November and December with higher volumes and the other months with lower volumes than the annual monthly average volumes. The study revealed
also that there is a significant higher volume of arrivals in the second half of the year than the first half. The model index was analysed using different statistical tests and was proved to have goodness of fit for the data. An estimate regression equation was generated to compute the model evaluation forecasts and later on an actual monthly forecast one year ahead were computed covering the period from June 2012 to May 2013. The study contributes to existing literature on analysing and forecasting international airport arrivals using classical approaches, which are more appreciated to users and more efficient in
capacity management as well as service delivery. The study has policy and management implications as discussed.