0306E - Faculty of Social Sciences - Université d'Ottawa

5 Concluding Remarks
In this paper we used six time series models to forecast seasonally unadjusted
Canadian E/D air passenger data. The results show that forecast performance
di¤ers depending on the forecast horizon and the series estimated.
We …nd that our forecasts are very similar to Transport Canada’s forecasts
for the transborder and international series while for the domestic sector our
forecasts show a slower recovery than Transport Canada forecasts.
For future research, we suggest two further applications of time series
techniques which would investigate the possibility of nonlinear characteristics
of Canadian E/D passenger data 17 . If justi…ed that air demand cycles
in Canada have asymmetric characteristics, modelling the air data using
nonlinear time series techniques would further improve the …t and forecasts
of air data. The two methods that we propose are the nonlinear error correction
model as used in Ericsson et al. (1998) and the Seasonal Smooth
Transition Autoregresive model (SEASTAR) which has been used in modelling
asymmetric behaviour of unemployment rates in US by Franses et al.
(2000).
References
[1] Box, G. E. P. and G. M. Jenkins (1970), Time Series Analysis: Forecasting
and Control, Holden-Day, San Francisco.
[2] Brons, M., E. Pels, P. Nijkamp, P. Rietveld (2002), “Price Elasticities
of Demand for Passenger Air Travel: A Meta-analysis,” Journal of Air
Transport Management 8, 165-175.
[3] Dielbold, F.X. and R.S. Mariano, (1995), ”Comparing predictive accuracy,”
Journal of Business and Economic Statistics 13, 253-263.
[4] Durbin, J. and G. S. Watson (1951), “Testing for Serial Correlation in
Least Squares Regression II,” Biometrika 38, 159-178.
[5] Davidson, R and Mackinnon (1993), ”Estimation and Inference in
Econometrics,” Oxford University Press.
17 Already the Air Forecasting Department of Transport Canada uses Box-Cox transformation
in estimating airport speci…c forecasts.
17