Modelling Tourism Demand Research Articles

Forecasting tourism demand: Developing a general nesting spatiotemporal model

This study proposes a general nesting spatiotemporal (GNST) model in an effort to improve the accuracy of tourism demand forecasts. The proposed GNST model extends the general nesting spatial (GNS) model into a spatiotemporal form to account for the spatial and temporal effects of endogenous and exogenous variables as well as unobserved factors. As a general specification of spatiotemporal models, the proposed model provides high flexibility in modelling tourism demand. Based on a panel dataset containing quarterly inbound visitor arrivals to 26 European destinations, this empirical study demonstrates that the GNST model outperforms both its non-spatial counterparts and spatiotemporal benchmark models. This finding confirms that spatial and temporal exogenous interaction effects contribute to improved forecasting performance.

Dynamic responses of tourist arrivals in Australia to currency fluctuations

This study examines the dynamic responses of monthly Australian inbound tourist arrivals to higher frequency daily movements in the exchange rate. Many existing models of inbound tourism utilize currency values averaged over months or quarters to match available visitor data, thus discarding valuable dynamic information from higher frequency variations in the exchange rate. We employ a Mixed Data Sampling (MIDAS) approach, modelling monthly Australian inbound tourism from 1998 to 2018, while utilising daily observations of the domestic currency as an explanatory variable. The findings suggest that models that take averages of exchange rate data to match frequencies may substantially underestimate how sensitive tourists are in responding to short-term fluctuations in the exchange rate. Indeed, the overwhelming majority of such responses typically are observed within 15–20 days prior to travel. After considering time horizons of up to 90 days, our findings indicate that travelers are predominantly concerned with exchange rate variations again only within three weeks prior to their departures. The results suggest that the Mixed Data Sampling (MIDAS) methodology can be a useful supplement to traditional approaches to modelling tourism demand. MIDAS models can provide rich insights into the duration and patterns of the dynamic interplay between the variables of interest.

Modeling Tourism Demand of Iran and Selected Countries (Application of ARDL Method)

Today, the tourism industry is one of the most influential industries in the economies of societies, especially in countries seeking a substitute for the export of single-product resources. Having a young population seeking job and great tourism attractions in the region and the world, Iran is in a good place in terms of tourist attraction. Despite the favorable conditions, its status is not favorable in comparison to similar countries. Accordingly, the present study aims to analyze the main determinants of international tourism demand of Iran by examining the current and desirable situation during the period of 1991-2018. Also, it aims to estimate the function of tourism demand in Iran in this time period using the ARDL method. The results of the present study show that the variables of national income of the origin countries (tourist sender), the volume of trade between Iran and other countries, security, as well as the ratio of consumer price index in Iran to consumer price index in origin countries are the most important determinant indicators of Iran’s international tourism demand in the short and long term, respectively. In addition, the negative elasticity of security and price index and the positive elasticity of national income and trade volume, as well as the positive coefficient of the number of tourists in the previous period indicate the effect of these indicators in this study. Keywords: Tourism demand, ARDL, demand determinants.

The volatility of tourism demand and real effective exchange rates: a disaggregated analysis

Purpose One of the main factors that can impact the cost of holidays to a particular destination is the exchange rate; exchange rate fluctuations impact the overall price of the holiday and should be expected to effect tourism demand. This paper aims to scrutinize the volatility of the real effective exchange rate between the source market relative to the holiday destination and tourism demand volatility, where the influence of disaggregated data is noted. Design/methodology/approach The study uses multivariate conditional volatility regressions to simulate the time-varying conditional variances of international visitor demand and exchange rates for the relatively mature Caribbean tourist destination of Barbados. Data on the country’s main source markets, the UK, the USA and Canada is used, where the decision to disaggregate the analysis by market allows the authors to contribute to policymaking, particularly the future of tourism marketing. Findings The volatility models used in the paper suggests that shocks to total arrivals, as well as the USA and UK markets tend to die out relatively quickly. Asymmetric effects were observed for total arrivals, mainly due to the combination of the different source markets and potential evidence of Butler’s (1980) concept of a tourist area’s cycle of growth. The results also highlight the significance of using disaggregated tourism demand models to simulate volatility, as aggregated models do not adequately capture source market specific shocks, due to the potential model misspecification. Exchange rate volatility is postulated to have resulted in the greater utilization of packaged tours in some markets, while the effects of the market’s online presence moderates the impact of exchange rate volatility on tourist arrivals. Markets should also explore the potential of attracting higher numbers of older tourist, as this group may have higher disposable incomes, thereby mitigating the influence of exchange rate volatility. Research limitations/implications Some of the explanatory variables were not available on a high enough frequency and proxies had to be used. However, the approach used was consistent with other papers in the literature. Practical implications The results from the paper suggest that the effects of exchange rate volatility in key source markets were offset by non-price factors in some markets and the existence of the exchange rate peg in others. In particular, the online presence of the destination was one of those non-price factors highlighted as being important. Originality/value In most theoretical models of tourism demand, disaggregation is not normally considered a significant aspect of the model. This paper contributes to the literature by investigating the impact real effective exchange rate volatility has on tourism demand at a disaggregated source country level. The approach highlights the importance of modeling tourism demand at a disaggregated level and provides important perspective from a mature small island destination.

Modeling tourism demand: Theoretical and empirical considerations for future research

Although tourism literature is replete with tourism demand studies, numerous empirical and theoretical issues remain unresolved. The majority of the extant studies center around replication of well-established theory of tourism demand with applications of contemporary empirical techniques for different sets of tourist-originating and receiving countries. However, the application of empirical techniques, especially panel data methods that are utilized to model tourism demand, seems to be arbitrarily chosen without due consideration of theoretical and empirical ramifications. The purpose of this study is to present a critical review of the tourism demand literature. We assessed the theoretical and methodological inaccuracies when modeling tourism demand in general and when applying panel data models in particular. We provided a guide to application of panel data techniques when modeling tourism demand. The article ends with discussions of an agenda for future research to fill the gaps in the extant literature and advance tourism demand research.

Tourist arrivals versus tourist expenditures in modelling tourism demand

In empirical research, aggregate tourist arrivals and tourist expenditures are often indistinctly used as measures of tourism demand, depending on the aim of the analysis or, simply, on the availability of data. However, when a literature review was conducted, we found differences in the estimated elasticities, accordingly to the measure that was used. This article investigates these two measures, exploring the theoretical link between them in the context of tourism demand modelling at a destination level. Having established the theoretical connection between the two variables with implications on the estimated elasticities, we estimate tourism demand models using international arrivals and tourist expenditures for 191 countries from 1998 to 2016, providing evidence for the theoretical connection. Our results show that when both tourist demand measures are used, the estimated elasticities may differ.

Habit formation or word of mouth: What does lagged dependent variable in tourism demand models imply?

The effects of habit formation/persistence (HFP) and word of mouth (WOM) each play a critical role in influencing tourists’ decisions regarding whether to visit tourism destinations and therefore tourism policies and tourism management resource allocations. Nevertheless, in previous tourism demand studies, the two effects have been represented by the same time-lagged dependent variable, which makes the variable have an ambiguous meaning and biases the empirical results. The purpose of this study is to solve the ambiguity of a lagged dependent variable in tourism demand. We used economic theories regarding internal habits and external habits to clarify the meanings of HFP and WOM and revised the tourism demand model into a spatial dynamic panel model (SDPM). The empirical results suggested that an SDPM is a more accurate model for modeling tourism demand. The effects of variables in an SDPM are more consistent with theoretical expectations.

Modelling tourism demand volatility using a seasonal autoregressive integrated moving average autoregressive conditional heteroscedasticity model for Victoria Falls Rainforest arrivals in Zimbabwe

Accurate tourism volatility forecasts for popular tourist destinations, like the Victoria Falls Rainforest, are vital to tourism destination managers and policymakers. The Victoria Falls Rainforest in Zimbabwe is under the town of Victoria Falls and is one of the natural wonders of the world. The rainforest has many exceptional plant species not common in the region and hence attracts many tourists. Financial, political and economic environments differently affect the Zimbabwean tourism industry, as evidenced by large tourist arrival fluctuations. Previous research focused more on tourism determinants than tourism volatilities. Researchers noted political instability and exchange rates as the major Zimbabwean tourism determinants. Estimates of the Victoria Falls Rainforest tourist arrival volatilities are projected using the monthly tourist arrival figures from the Zimbabwe Parks and Wildlife Management Authority and Zimbabwe Tourism Authority. The first difference of logarithmic transformed series is stationary. The univariate SARIMA(2,1,0)(2,0,0)12-ARCH(1) model fits extremely well and provides an informative out-of-sample volatility forecast because it captures tourism volatility effects, dynamics and non-linearity of conditional variances. The results indicate that positive tourism shocks affect tourist arrival volatility positively. Volatility estimates indicated minimal uncertainty in the first half of the forecasted year and then became constant throughout the year. This encourages the continuation of the implementation of new favourable policies and marketing strategies by the government and tourism destination managers to keep the destination distinctive and attractive. The New Zimbabwe political dispensation is likely to enhance investment opportunities at the Victoria Falls Rainforest as a destination because of minimal uncertainties exhibited by volatility forecasts. Potential employment creation, improved economic environment and other positives are some of the expectations from the model results.

THE INTERNATIONAL DEMAND FOR EUROPEAN TOURISM IN THE COUNTY OF PRIMORJE-GORSKI KOTAR

The paper studies the demand for European tourism in the County of Primorje-Gorski kotar. First an attempt to model geometrically the demand for European tourism in the County of Primorje-Gorski kotar is given, follow by the choice and the descriptions of the dependent variable and a number of explanatory variables. The analysis is based on the Almost Ideal System of Demand, which provides a useful framework for modelling tourism demand across destinations.

An econometric approach to tourism demand: Evidence from Turkey

This study focuses on modelling tourism demand with using number of tourists who came to Turkey between the years 1966-2012 (47 years)and bed numbers belonging to accommodation facilities with tourism operation licensed. The number of tourists and beds have only been reached dealingwith the years 1966-2012. Except from these variables, many variables such asstates investment incentives on tourism, tourism revenues, the annual averagetemperature, the parity of foreign currency like Euro, dollar and power of purchasing in the country could be taken into account. The aim of this paper is tocreate a forecasting model about the number of coming tourists and to provideprior information for tourism policies by regarding introduced models. In thissense, forecasting of international tourist arrivals for 2013-2020 is purposed

Gravity models for tourism demand: theory and use

Neglected by the tourism demand literature for the last decades, gravity models have re-emerged as a way for modeling tourism demand when the role of structural factors on tourism has to be evaluated. From the initial formulation of the gravity model, more sophisticated specifications have been developed including a more complete set of explanatory variables and allowing differentiation between origin and destination countries. In this paper, we propose a theoretical background to the gravity model for bilateral tourism flows derived from the individual utility theory. The issues in distinguishing the recent versions of gravity models from aggregated demand models are shown and the suitability of this methodology when structural factors have to be evaluated and quantified in the context of tourism demand is discussed.

How to evaluate the effects of climate change on tourism

The aim of this study is to assess the most relevant quantitative approaches to evaluating the effects of climate change on tourism. In recent years, numerous empirical studies have conducted evaluations of this kind, based on different methodologies and perspectives. This review shows that the effects of climate change can first be assessed through changes in physical conditions essential to tourism; secondly, by using climate indexes to measure the attractiveness of tourist destinations; and, thirdly, by modelling tourism demand with the inclusion of climate determinants. The review suggests that, although some methodologies are in the early stages of development, different approaches result in a similar map of those areas mainly affected by the problem.

Modelling tourism demand for South Africa: An almost ideal demand system approach

This paper models tourism demand for South Africa from the UK and the USA, using an almost ideal demand system. An error-correction almost ideal demand system (EC-AIDS) is applied to quantify the responsiveness of UK and USA tourism demand for South Africa, relative to changes in tourism prices and expenditure or income. Short-term own-price, cross-price and expenditure elasticities are derived from the EC-AIDS models. One of the key findings of the paper is that tourism from the UK and USA is not sensitive to price changes in South Africa in the short term. Tourism to South Africa is found to be more income-elastic than price-elastic, indicating that the country is vulnerable to changing world economic conditions. Even though price competitiveness does not yet seem to be a key concern, significant substitution effects are present, with especially Spain and Malaysia benefiting from a decline in South Africa’s price competitiveness.

Estimating demand elasticities for intra-regional tourist arrivals to Hong Kong – the ‘bounds’ testing approach

The aim of this study is to develop econometric models for estimating tourism demand elasticities using the Autoregressive Distributed Lag (ARDL) approach to cointegration analysis, and to explain the effects of economic determinants on inbound tourist flows to Hong Kong from four major short-haul markets. The cointegration test used is the ‘bounds’ test of Pesaran et al. (2001) that is based on the estimation of an Unrestricted Error-Correction Model (UECM). This article addresses one of the major problems of how to use a relatively small sample to estimate tourism demand elasticities using cointegration approaches, which is faced by many researchers in modelling tourism demand. The results show that permanent income is the most important explanatory variable for all origin countries, but there are substantial variations between countries with the long-run elasticity ranging between 1.74 for China and 3.05 for Australia. Price elasticity is the next most important variable with the long-run elasticities ranging from 0.35 (Australia) to 0.98 (Taiwan). The findings are consistent with economic theory and have implications for government policies and strategies on investment, marketing and promotion and pricing.

Research Note: Modelling Tourism Demand – an Econometric Analysis of North American Tourist Expenditure in Ireland, 1985–2004

The monetary contribution of inbound North American tourism to the Irish exchequer is economically substantial, generating more tourist revenue earnings per capita in Ireland than visitors from any other country. The purpose of this paper is to present a macroeconometric analysis of North American tourist expenditure in Ireland, thus providing an insight into the effects on expenditure patterns of the adjustment of key macroeconomic variables. Using an econometric causal model, key macroeconomic and demographic variables are regressed on real expenditure of the North American, US and Canadian regions. Among the main findings, it is revealed that the real exchange rate variable and the over-45 age cohort exert a positive and statistically significant result in the case of all three regions. The paper acknowledges that appropriate strategies are required to maximize the potential of those subsectors providing the greatest ‘value for entry’ from an Irish tourism perspective to enable the future proliferation of inbound revenues for the tourism industry.

An Evaluation of Price Measures in Tourism Demand Models

In contrast with the rapid development of forecast modeling in tourism, there has been little attention given to identifying the best measures of important explanatory variables. This article focused on selecting the best surrogate of tourism price from two measures considered important determinants in modeling tourism demand: the exchange rate-adjusted consumer price index ratio of origin and destination countries, and the divided variables of exchange rate and relative consumer price indexes. Two different models using these measures of tourism prices were compared in terms of their forecasting performance using the Johansen multivariate cointegration approach. We used aggregate data on international tourism demand in Korea. Test results, including forecasting performance and forecast error decompositions, were not clear cut but indicated that the divided measures of tourism prices variables with exchange rate and relative consumer price index are likely best for tourism demand modeling.

The Demand for Tourism to Greece: A Cointegration Approach

Cointegration analysis in modelling tourism demand has rarely been used in previous empirical research studies. This paper attempts to answer two main questions: first, whether certain economic factors are interconnected in the long term with tourism demand, and second, whether a short-run dynamic specification of this demand exists which is statistically adequate and which has appropriate forecasting properties.