Design/Methodology/approachArticles on tourism and hotel demand modeling and forecasting published in both Science Citation Index (SCI) and Social Sciences Citation Index (SSCI) journals were identified and analyzed. FindingsThis review finds that the studies focused on hotel demand are relatively less than those on tourism demand. It is also observed that more and more studies have moved away from the aggregate tourism demand analysis, while disaggregate markets and niche products have attracted increasing attention. Some studies have gone beyond neoclassical economic theory to seek additional explanations of the dynamics of tourism and hotel demand, such as environmental factors, tourist online behavior, and consumer confidence indicators, among others. More sophisticated techniques such as Research limitations/implicationsThe main limitation of this review is that the articles included in this study only cover the English literature. Future review of this kind should also include articles published in other languages. The review provides a useful guide for researchers who are interested in future research on tourism and hotel demand modeling and forecasting. Practical implicationsThis review provides important suggestions and recommendations for improving the efficiency of tourism and hospitality management practices. Originality/valueThe value of this review is that it identifies the current trends in tourism and hotel demand modeling and forecasting research and points out future research directions.

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The recent economic crisis and swine flu pandemic have had significantly negative impacts on global tourism. Tourism in the United Kingdom has also suffered as a result of the two crises, although their actual impacts have yet to be evaluated. This study analyzes the impacts of these two phenomena on the demand for U.K. inbound tourism during the 2008Q1-2009Q2 period among visitors from the country’s 14 major visitor source markets. An econometric framework is proposed to separate and estimate the impacts of swine flu and the economic crisis on U.K. tourism demand.

Existing non-tourism related literature shows that forecast combination can improve forecasting accuracy. This study tests this proposition in the tourism context by examining the efficiency of combining forecasts based on three different combination methods. The data used for this study relate to tourist arrivals in Hong

He has a background in economics with research interests including tourism demand forecasting and tourism impact assessment.

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Multivariate forecasting methods are intuitively appealing since they are able to capture the interseries dependencies, and therefore may forecast more accurately. This study proposes a multiseries structural time series method based on a novel data restacking technique as an alternative approach to seasonal tourism demand forecasting. The proposed approach is analogous to the multivariate method but only requires one variable. In this study, a quarterly tourism demand series is split into four component series, each component representing the demand in a particular quarter of each year; the component series are then restacked to build a multiseries structural time series model. Empirical evidence from Hong Kong inbound tourism demand forecasting shows that the newly proposed approach improves the forecast accuracy, compared with traditional univariate models.