Long-term air travel demand forecasting: An integrated method with ARDL bounds testing approach and scenario planning

This chapter proposes an integrated long-term forecasting method for Chinese national air travel demand, based on the TEI@I methodology. The framework emphasizes the importance of experts' domain knowledge in the demand forecasting process, especially for a long-term future. The main objective of the long-term demand forecasting is to estimate the future air travel demand for a specific market with certain expected changes in the national economy and demography, or the development policy. The chapter describes the specific methods and models used in different modules, including the Autoregressive Distributed Lag bound testing approach, the logistic growth model, the Markov-switching regime model and the scenario planning technique. In the long-term demand forecasting literature, many studies have discussed the application of cointegration relationships in demand forecasting, and most of them adopted the traditional vector error correction framework of Engle and Granger to model the cointegration relationship.