Bootstrap estimation intervals using bias corrected accelerated method to forecast air passenger demand

The aim of this paper is to propose an approach for forecasting passenger (pax) demand between airports based on the median pax demand and distance. The approach is based on three phases. First, the implement of bootstrap procedures to estimate the distribution of the mean pax demand and the median pax demand for each block of routes distance; second, the estimate pax demand by calculating boostrap confidence intervals for the mean pax demand and the median pax demand using bias corrected accelerated method (BCa); and third, by carrying out Monte Carlo experiments to analyse the finite sample performance of the proposed bootstrap procedure. The results indicate that in the air transport industry it is important to estimate the median of the pax demand.