A hybrid MIDAS approach for forecasting hotel demand using large panels of search data

• Binru Zhang ^[1] ; Nao Li ^[2] ; Rob Law ^[3] ; Heng Liu ^[4]
  1. [1] Yangtze Normal University

     Yangtze Normal University

     China

     
  2. [2] Beijing Technology and Business University

     Beijing Technology and Business University

     China

     
  3. [3] Hong Kong Polytechnic University

     Hong Kong Polytechnic University

     RAE de Hong Kong (China)

     
  4. [4] University of International Business and Economics

     University of International Business and Economics

     China

     

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• Localización: Tourism economics: the business and finance of tourism and recreation, ISSN 1354-8166, Vol. 28, Nº. 7, 2022, págs. 1823-1847
• Idioma: inglés
• Texto completo no disponible (Saber más ...)
• Resumen

  • The large amounts of hospitality and tourism-related search data sampled at different frequencies have long presented a challenge for hospitality and tourism demand forecasting. This study aims to evaluate the applicability of large panels of search series sampled at daily frequencies to improve the forecast precision of monthly hotel demand. In particular, a hybrid mixed-data sampling regression approach integrating a dynamic factor model and forecast combinations is the first reported method to incorporate mixed-frequency data while remaining parsimonious and flexible. A case study is undertaken by investigating Sanya, the southernmost city in Hainan province, as a tourist destination using 9 years of the experimental data set. Dynamic factor analysis is used to extract the information from large panels of web search series, and forecast combinations are attempted to obtain the final prediction results of the individual forecasts to enhance the prediction accuracy further. The empirical analysis results suggest that the developed hybrid forecast approach leads to improvements in monthly forecasts of hotel occupancy over its competitors.