Air traffic modelling based on a long memory approach Air Traffic Modelling

Main Article Content

Luis Alberiko GIL-ALANA
Rebeca Gili

Keywords

Air traffic, Fractional integration, persistence, Covid-19, shock, mean reversion

Abstract

This paper deals with modeling air traffic data using a long memory class of models that use fractional integration. Two datasets have been considered: monthly global Revenue Passenger Kilometers and the number of monthly flights in Europe. The objective of this paper is to investigate whether Covid-19 has had a temporary or permanent impact on the air traffic trends. To do so, we investigate the orders integration of the series. Both datasets produced the same results: the trend was mean reverting when considering data before Covid-19, but the shock was so strong and long-lasting, that it produced a change to non-mean-reversion results after Covid-19. That said, if it is desirable to bring the air traffic trend back to its values before Covid-19, it will require intervention on the part of authorities or external factors since the series will not return by themselves to their original long term projections.

Downloads

Download data is not yet available.
Abstract 22 | PDF Downloads 12