Forecasting Tourism Demand with an Improved Mixed Data Sampling Model

Long Wen, Chang Liu, Haiyan Song, Han Liu

Research output: Journal PublicationArticlepeer-review

51 Citations (Scopus)
244 Downloads (Pure)


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 generalized 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 outperforms the former.

Original languageEnglish
Pages (from-to)336-353
Number of pages18
JournalJournal of Travel Research
Issue number2
Publication statusPublished - Feb 2021


  • generalized dynamic factor model
  • nowcasts
  • search query data
  • tourism demand forecasting

ASJC Scopus subject areas

  • Geography, Planning and Development
  • Transportation
  • Tourism, Leisure and Hospitality Management


Dive into the research topics of 'Forecasting Tourism Demand with an Improved Mixed Data Sampling Model'. Together they form a unique fingerprint.

Cite this