Tourism Demand Forecasting With Multiple Mixed-Frequency Data: A Reverse Mixed-Data Sampling Method

Peihuang Wu, Gang Li, Long Wen, Han Liu

Research output: Journal PublicationArticlepeer-review

1 Citation (Scopus)

Abstract

Due to the limitations of existing tourism demand forecasting models, data with frequencies lower than those of the tourism demand need to be processed in advance and cannot be directly used in a model, which leads to the loss of timeliness and accuracy in tourism demand forecasting. Taking the inbound tourism of the United States prior to and during the COVID-19 pandemic as an example, this study systematically examines the impact of data frequency processing on tourism demand modeling and forecasting, through the construction and comparison of three categories of models, with a particular focus on the first developed multiple mixed-frequency specification of reverse mixed-data sampling (RMIDAS) model. The results confirm the positive effect of multiple mixed-frequency models, which can directly use various original data frequencies, in improving the accuracy of tourism demand forecasting. This study also provides important guidance for future research on high-frequency tourism variables forecasting.

Original languageEnglish
JournalJournal of Travel Research
DOIs
Publication statusAccepted/In press - 2023

Keywords

  • frequency processing
  • multiple mixed-frequency
  • reverse mixed-data sampling model
  • tourism demand forecasting

ASJC Scopus subject areas

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

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