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

Binru Zhang, Nao Li, Rob Law and Heng Liu
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Binru Zhang: 117787Yangtze Normal University, China
Nao Li: Beijing Technology and Business University, China; State Key Laboratory of Resources and Environmental Information System, China
Rob Law: Hong Kong Polytechnic University, Hong Kong
Heng Liu: University of International Business and Economics, China

Tourism Economics, 2022, vol. 28, issue 7, 1823-1847

Abstract: 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.

Keywords: Dynamic factor model; forecast combinations; hotel demand; hybrid MIDAS approach; mixed-frequency data; search engine data (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:sae:toueco:v:28:y:2022:i:7:p:1823-1847

DOI: 10.1177/13548166211015515

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