Modeling Tourist Flows to Indonesia and Malaysia

Summary

The purpose of this study was to examine the major factors that influence the flow patterns of tourists from six important tourist-generating countries to Indonesia and Malaysia. The primary determinants included in the demand models were income, prices, and time trend. Two models that employed different indicators for the price variable were estimated; one with exchange rates in addition to relative prices, whereas the other included only an exchange rate adjusted-relative price variable. Annual time-series data covering the period 1980 to 1997 were used for estimation. The results generally indicated that the factors provide reasonably good explanations for the demand for Indonesian and Malaysian tourism. The measure of thejoint effect of the changes in exchange rates and relative prices also seems to be a better indicator for the price variable for both destination countries. The study has important marketing implications for the tourism industries in Indonesia and Malaysia.     

The determinants of Chinese visitors to Australia: A dynamic demand analysis

Although China has progressively become an important inbound tourism market for Australia, its demand elasticities have been little studied to date. This study examines the determinants of Chinese visitors to Australia using a dynamic time-series estimator. Interesting findings include a high income elasticity as a source of the continuous doubledigit growth rates in Chinese arrivals that Australia has experienced over the past two decades, together with relatively high total trip price elasticities for both short run and long run. A trend of Chinese outbound to Australia is also identified. From a policy perspective, the results confirm that keeping a low cost of visiting Australia, both ground and travel costs, is a good strategy to secure greater numbers of Chinese tourists.     

Modelling Australian domestic and international inbound travel: a spatial–temporal approach

In this paper Australian domestic and international inbound travel are modelled by an anisotropic dynamic spatial lag panel Origin-Destination (OD) travel flow model. Spatial OD travel flow models have traditionally been applied in a single cross-sectional context, where the spatial structure is assumed to have reached its long run equilibrium and temporal dynamics are not explicitly considered. On the other hand, spatial effects are rarely accounted for in traditional tourism demand modelling. We attempt to address this dichotomy between spatial modelling and time series modelling in tourism research by using a spatial-temporal model. In particular, tourism behaviour is modelled as travel flows between regions. Temporal dependencies are accounted for via the inclusion of autoregressive components, while spatial autocorrelations are explicitly accounted for at both the origin and the destination. We allow the strength of spatial autocorrelation to exhibit seasonal variations, and we allow for the possibility of asymmetry between capital-city neighbours and non-capital-city neighbours. Significant temporal and spatial dynamics have been uncovered for both domestic and international tourism demand. For example we find strong seasonal temporal autocorrelations, significant trends and significant spatial autocorrelations at both the origin and the destination. Moreover, the spatial patterns are found to be most significant during peak holiday seasons. Understanding these patterns in tourist behaviour has important implications for tourism operators.     

A piecewise linear approach to modeling and forecasting demand for Macau tourism

In this paper, we construct and use a piecewise linear method to model and forecast, on a monthly basis, the demand for Macau tourism. Data over the period January 1991–December 2005 and a seasonally adjusted series for tourism demand are used. The study examines 4 forecasting horizons ranging from 6 to 24 months in advance. Mean absolute percentage errors and root mean square errors are adopted as criteria for evaluating the accuracy of the forecasting exercises. Finally, the forecasts of piecewise linear model are compared with those of autoregressive trend model, seasonal autoregressive integrated moving average and its arch-rival fractionally integrated autoregressive moving average models. The piecewise linear model is more accurate than the three benchmark models tested and the improvement is practically significant.     

Mapping time series into networks as a tool to assess the complex dynamics of tourism systems

This paper contributes to filling two gaps: i) the presence of a limited amount of studies focused on tourism demand turning points, ii) the prevalent recourse to linear models in demand analysis, disregarding the complex structure of tourism destinations. The paper uses the Horizontal Visibility Graph Algorithm, a technique able to transform a time series of observations into a network whose topology preserves some fundamental characteristics of the system examined. The empirical work focuses on Livigno, an Italian alpine destination.Findings reveal four turning points in the last 50 years; these changes are built around shifts in the origin market segments. The network's degree distribution confirms the complex structure of the destination and reconfirms the importance of non-linear models and methods for the analysis of tourism demand.     

Demand elasticity estimates for New Zealand tourism

Demand elasticities for New Zealand tourism are estimated for 16 different international visitor segments. Segments are differentiated by origin, purpose of visit, and travel style. Elasticities for both international visitor arrivals and on-the-ground expenditure per arrival are estimated for each segment using time-series data. In general, on-the-ground consumption per arrival is more price sensitive than number of arrivals, and Asian market segments are found to be more price sensitive, both in terms of arrivals and on-the-ground expenditure, compared to international visitors from other regions. An application of the results is presented giving the total effect of exchange rate changes on expenditure by international visitors in New Zealand, and management implications are discussed.     

Implications of a long-term increase in oil prices for tourism

It is expected that global oil prices will increase in the future. Assessing the overall economic impacts on tourism is difficult, as oil price rises will be concomitant with global changes in other commodity prices, exchange rates, and incomes. A general equilibrium perspective is therefore presented in this paper. The model couples a global general equilibrium model with a purpose-built CGE model of New Zealand, which focuses on describing tourism supply and demand in some detail. The results indicate a decrease in real gross national disposable income of 1.7% for a doubling of oil price and a 9% reduction in the real value of tourism exports. As a result of segment-specific price increases and differing income and exchange rate effects and elasticities, the reduction in demand for tourism in New Zealand by 18 segments differs substantially. The greatest reduction in demand is observed for tourists from the United Kingdom.