Forecasting tourism demand with an improved mixed data sampling model
Search 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 invol...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
2020
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| Subjects: | |
| Online Access: | https://eprints.nottingham.ac.uk/60293/ |
| _version_ | 1848799745496055808 |
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| author | Wen, Long Liu, Chang Song, Haiyan Liu, Han |
| author_facet | Wen, Long Liu, Chang Song, Haiyan Liu, Han |
| author_sort | Wen, Long |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | Search 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. |
| first_indexed | 2025-11-14T20:40:33Z |
| format | Article |
| id | nottingham-60293 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T20:40:33Z |
| publishDate | 2020 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-602932020-04-13T01:15:26Z https://eprints.nottingham.ac.uk/60293/ Forecasting tourism demand with an improved mixed data sampling model Wen, Long Liu, Chang Song, Haiyan Liu, Han Search 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. 2020-03-16 Article PeerReviewed application/pdf en cc_by https://eprints.nottingham.ac.uk/60293/1/Long%20wen%20merged.pdf 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) Tourism demand forecasting; MIDAS; Search query data; Generalised dynamic factor model; Nowcasts http://dx.doi.org/10.1177/0047287520906220 doi:10.1177/0047287520906220 doi:10.1177/0047287520906220 |
| spellingShingle | Tourism demand forecasting; MIDAS; Search query data; Generalised dynamic factor model; Nowcasts Wen, Long Liu, Chang Song, Haiyan Liu, Han Forecasting tourism demand with an improved mixed data sampling model |
| title | Forecasting tourism demand with an improved mixed data sampling model |
| title_full | Forecasting tourism demand with an improved mixed data sampling model |
| title_fullStr | Forecasting tourism demand with an improved mixed data sampling model |
| title_full_unstemmed | Forecasting tourism demand with an improved mixed data sampling model |
| title_short | Forecasting tourism demand with an improved mixed data sampling model |
| title_sort | forecasting tourism demand with an improved mixed data sampling model |
| topic | Tourism demand forecasting; MIDAS; Search query data; Generalised dynamic factor model; Nowcasts |
| url | https://eprints.nottingham.ac.uk/60293/ https://eprints.nottingham.ac.uk/60293/ https://eprints.nottingham.ac.uk/60293/ |