Exploring Appropriate Search Engine Data for Interval Tourism Demand Forecasting Responding a Public Crisis in Macao: A Combined Bayesian Model

Abstract

Public crises can bring unprecedented damage to the tourism industry and challenges to tourism demand forecasting, which is essential for crisis management and sustainable development. Existing studies mainly focused on point forecasts, but point forecasts may not be enough for the uncertain environments of public crises. This study proposes a combined Bayesian interval tourism demand forecasting model based on a forgetting curve. Moreover, considering tourists’ travel plans may be adjusted due to changing crisis situations, the choice of search engine data for forecasting tourism demand is investigated and incorporated into the proposed model to yield reliable results. Through an empirical study, this study figures out that the Baidu Index had better tourism predictive capabilities before the public crisis, whereas the Google Index effectively captured short-term fluctuations of tourism demand within the crisis period. The results also indicate that integrating both Baidu and Google Index data obtains the best prediction performance after the crisis outbreak. Our main contribution is that this study can generate flexible forecasting results in the interval form, which can effectively handle uncertainties in practice and formulate control measures for practitioners. Another novelty is successfully discovering how to select appropriate search engine data to improve the performance of tourism demand forecasts across different stages of a public crisis, thus benefiting daily operations and crisis management in the tourism sector.