Forecasting tourism demand with an improved mixed data sampling modelTools Wen, Long, Liu, Chang, Song, Haiyan and Liu, Han (2020) Forecasting tourism demand with an improved mixed data sampling model. Journal of Travel Research . 004728752090622. ISSN 0047-2875 (In Press)
Official URL: http://dx.doi.org/10.1177/0047287520906220
AbstractSearch query data reflect users’ intentions, preferences and interests. The interest in using such data to forecast tourism demand has increased in recent years. The mixed data sampling (MIDAS) method is often used in such forecasting, but is not effective when moving average (MA) dynamics are involved. To investigate the relevance of the MA components in MIDAS models to tourism demand forecasting, an improved MIDAS model that integrates MIDAS and the seasonal autoregressive integrated moving average process is proposed. Its performance is tested by forecasting monthly tourist arrivals in Hong Kong from mainland China with daily composite indices constructed from a large number of search queries using the generalised dynamic factor model. The forecasting results suggest that this new model significantly outperforms the benchmark model. In addition, comparing the forecasts and nowcasts shows that the latter generally outperform the former.
Actions (Archive Staff Only)
|
Tools
Tools
