Modeling and forecasting tourism demand for arrivals with stochastic nonstationary seasonality and intervention

Goh, Carey; Law, Rob

doi:10.1016/s0261-5177(02)00009-2

Cited by 349 publications

(224 citation statements)

References 15 publications

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“…Goh and Law (2002) constructed the seasonal autoregressive integrated moving average (SARIMA) and autoregressive integrated moving average (MARIMA) models with interventions to gauge the influences of the Asian financial crisis, the handover of Hong Kong to mainland China and bird flu on Hong Kong inbound tourism. Song et al (2003) incorporated the effects of the Asian financial crisis by including a crisis dummy in the Autoregressive Distributed Lag Model (ADLM) to assess the impact of the crisis on inbound tourism to Hong Kong.…”

Section: Literature Reviewmentioning

confidence: 99%

Global Financial/Economic Crisis and Tourist Arrival Forecasts for Hong Kong

Song

Lin

Zhang

et al. 2010

Asia Pacific Journal of Tourism Research

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Section: Literature Reviewmentioning

confidence: 99%

Global Financial/Economic Crisis and Tourist Arrival Forecasts for Hong Kong

Song

Lin

Zhang

et al. 2010

Asia Pacific Journal of Tourism Research

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“…The applications of these new developments have been seen in some studies. For example, Goh and Law (2002) introduced a multivariate SARIMA (i.e., MARIMA) model which includes an intervention function to capture the potential spillover effects of the "parallel" demand series on a particular tourism demand series. Their study showed that the MARIMA model significantly improved the forecasting performance of the simple SARIMA as well as other univariate time series models.…”

Section: Modelling and Forecasting Techniquesmentioning

confidence: 99%

Tourism Demand Modeling and Forecasting: A Review of Literature Related to Greater China

2009

Journal of China Tourism Research

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Greater China, including Mainland China, Hong Kong, Macau and Taiwan, contributes significantly to both regional and global tourism developments. Empirical research on tourism demand modelling and forecasting has attracted increasing attention of scholars both within and beyond this region. One hundred and eighty articles are identified that were published in both English and Chinese language journals since the beginning of the 1990s. This study presents the largest scale of literature survey on tourism demand studies. Furthermore, this is the first attempt in tourism demand review studies that focuses exclusively on one geographic region and covers bilingual literature. Particular emphasis of this review is placed on research development, geographic focus, data type and frequency, measurement of tourism demand, modelling and forecasting techniques, demand elasticity analysis, forecasting exercises and emerging research trends. Comparisons between the two bodies of literature published in two languages show a number of research gaps, such as the diversity and sophistication of the research methodology, rigour of the modelling and forecasting process and theoretical foundations of demand analysis. Correspondingly, constructive recommendations are made to further advance tourism demand studies related to Greater China.

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“…The forecasts obtained, in the study conducted by Goh and Law (2002) using models SARIMA and multivariate SARIMA (MARIMA) with intervention, were compared with other eight time series models and found that SARIMA has the highest accuracy in forecasting. Butler (1994) commented that the obvious seasonality in tourist arrival is important and it should be neglected while making forecasts for tourist arrival.…”

Section: Introductionmentioning

confidence: 99%

Forecasting tourist in-flow in South East Asia: A case of Singapore

Kumar

Sharma

2016

TMS

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This study attempts to forecast tourist inflow in South East Asia and choses Singapore as a case. For Singapore, tourism is one of the major sources of foreign exchange earnings since it has no natural resources to support its economy. Therefore, forecasting of tourist arrivals in the country becomes very important for the reason that the forecasting may help tourism related service industries (e.g. airlines, hotels, shopping malls, transporters and catering services, etc.) to plan and prepare their resources and activities in an optimal way. In this paper, seasonal autoregressive integrated moving average (SARIMA) methodology was considered for making monthly predictions on tourist arrival in Singapore. The best model for forecasting is found out to be (1,0,1)(1,1,0)12 and monthly forecasting were obtained for two years in future. Further, various statistical tests (e.g. Dickey Fuller, KPSS, HEGY, Ljung-Box, Box-Pierce etc.) were applied on the time series for adequacy of best model to fit, residual autocorrelation analysis and for the accuracy of the prediction.Keywords: Forecasting, Seasonal ARIMA, Tourist Arrivals, Singapore. ResumoEste estudo tenta prever o fluxo turístico no Sudeste Asiático e escolhe Singapura como um caso. Para Singapura, o turismo é uma das principais fontes de divisas, uma vez que não possui recursos naturais para sustentar a sua economia. Portanto, a previsão de chegadas de turistas no país torna-se muito importante pelo motivo que a previsão pode ajudar as indústrias de serviços relacionados com o turismo (por exemplo, companhias aéreas, hotéis, centros comerciais, transportadoras e serviços de catering, etc.) para planear e preparar os seus recursos e atividades de uma forma otimizada. Neste trabalho, foi utilizada a metodologia SARIMA de modo a fazer previsões mensais de chegadas turísticas a Singapura. O melhor modelo de previsão é considerado ser (1,0,1) (1,1,0) 12, sendo obtidas previsões mensais num prazo de dois anos relativamente ao futuro. Além disso, vários testes estatísticos (por exemplo Dickey Fuller, KPSS, Hegy, Ljung-Box, BoxPierce etc.) foram aplicados sobre as séries cronológicas para adequação do melhor modelo para o ajuste, da análise de auto correlação residual e para a precisão da previsão.Palabras clave: Previsão, Seasonal ARIMA, chegadas de turistas, Singapura.

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Modeling and forecasting tourism demand for arrivals with stochastic nonstationary seasonality and intervention

Cited by 349 publications

References 15 publications

Global Financial/Economic Crisis and Tourist Arrival Forecasts for Hong Kong

Global Financial/Economic Crisis and Tourist Arrival Forecasts for Hong Kong

Tourism Demand Modeling and Forecasting: A Review of Literature Related to Greater China

Forecasting tourist in-flow in South East Asia: A case of Singapore

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