The relative advantages and disadvantages of the causal and non-causal approaches to tourism demand forecasting.

Transcription

1 The relative advantages and disadvantages of the causal and non-causal approaches to tourism demand forecasting. By Kostas E. Sillignakis The aim of this essay is to discuss the relative advantages and disadvantages of the causal and non-causal approaches to tourism demand forecasting. The objective of tourism demand forecasting is to predict the most probable level of demand that is likely to occur in the light of known circumstances or, when alternative policies are proposed to show what different levels of demand may be achieved (Archer, 1987: 105). Forecasting plays a major role in tourism planning. It is widely agreed that the concept of planning deals with some form of decision making involving the future. In addition, Archer (1987) pointed out that the need to generate accurate forecasts is especially acute because of the perishable nature of the tourism product. The promotion of tourism projects involving substantial sums of money requires an estimate of future demand and market penetration. The commitment to developing tourism business would be much easier if it were possible to analyse current and past tourist traffic and predict the nature of changes in tourism demand (Cho, 2002). Therefore, reliable and accurate forecasting is needed to help the decision makers to plan more effectively and efficiently (Goh & Law, 2002). The history of tourism forecasting started as early as the 1960s (Witt & Witt, 1995). Following the new development in macroeconomic model building and forecasting, tourism forecasting analysis also experienced considerable changes over the last few decades (Goh &Law, 2002). Most of the studies are concerned with explaining the demand for total tourism or holiday leisure tourism, although the demand for business tourism has also been investigated (Witt, Sykes & Dartus, 1995). 1

2 Forecasting methods of tourism demand can be classified as qualitative or quantitative. Quantitative methods require the use of historical data in conjunction with mathematical models and are aimed to identify relationships and patterns of future tourism demand and its determinants. On the other hand, qualitative approaches do not necessarily require the existence of historic data since they rely on pooled expert opinions. Quantitative methods can be further divided into causal and non-causal (Figure 1.1) depending on whether they try to establish cause and affect relationships or not. The fact that forecasting methods are divided into many sub categories does not necessarily imply that one chosen method disqualifies the others (Frechtling, 1996). Figure 1.1: Classification of approaches to tourism demand forecasting METHODS OF TOURISM DEMAND FORECASTING Quantitative Qualitative Causal Non-Casual Multivariate Regression Naïve Time Series Delphi Model Gravity and Trip Generation Smoothing methods Scenario Writing System Demand Model Classical Decomposition Jury of Executive Opinion Autoregression Subjective Probability Box-Jenkins Source: Adapted from Frechtling (1996: 18) 2

3 The causal approach is alternatively called econometric approach to tourism demand forecasting. The econometric approach has been employed extensively in tourism demand forecasting (Witt and Witt, 1992). Furthermore, it sees demand as a function of its determinants and uses historic data and regression analysis to build causal relationships the demand for tourism those factors that influence it (Witt et al, 1991). For the development of a causal model, it is necessary to collect time series data and then build at least one equation which will include all the independent variables that are significant for the purpose of the forecasting (equation 1.2). (Equation 1.2) Y = f (X1, X2, X3,..Xk) where: Y = the demand for tourism to a given destination from a particular origin and X1,X2,X3, Xk = The independent variables. On the other hand, Non-casual methods, which are also known as univariate time series or extrapolative methods, aims to use past values of tourism demand in order to extrapolate future ones. According to Witt and Mutinho (1994), the dependent variable of tourism demand can be forecasted without considering other factors that might influence the one to be forecasted. In other words, it assumes that the variable s past course can be used to predict its future course (Frechtling, 1996). There does not seem to be agreement as to which of these approaches produces the most accurate forecasts, and they both seem to have advantages and disadvantages which the researcher needs to be aware of before choosing the most appropriate one to use. 3

4 One of the main advantages of the causal approach is that it has explanatory power. The explanatory power of the casual approach is one of its main advantages. Contrasting with the non-causal approach, it allows for an understanding of the relationships among variables, for the reason that it models the degree of influence exerted upon the demand by each of several variables (Athiyaman and Robertson, 1992). Furthermore this is and advantage of the casual approach, especially when researcher s objective is to acquire basic knowledge about tourism industry as a phenomenon. Additionally, the Causal approach helps the researchers to do a what if forecasting, by examining the relationship between demand variables. By addressing a causal relationship that exists in the real world econometric tourism demand forecasting becomes a valuable tool in the hands of planners, strategists and marketers. Plus, it could become a valuable tool for planers strategists and marketers, because it could deliver the reality of the tourism industry. Indeed, it allows them to forecast the effect that certain policy changes (pricing, promotion, regulation) will have on tourism demand (Frechtling, 1996; Witt and Witt, 1992). Nevertheless, causal approach is not especially accurate in forecasting tourism demand. The lack of consistent superior performance of econometric models is disturbing, particularly as the extremely simple models perform nearly as well (Witt and Witt, 1992: 87). The insufficient data on tourism demand and on explanatory variables could be a possible reason for the poor performance of econometric models when compared to the non-casual ones. Furthermore, holiday decision-making is such a complicated process that is not easy to be analysed by specific models and frameworks, because variables are changing all the time (Witt and Witt, 1992). On the technical side we can find two more disadvantages of the casual approach. When building a model, variables with insignificant coefficients have 4

5 to be rejected according to the principles of econometrics (Dharmaratne, 1995). Therefore, in order to generate forecasts from a regression model the values of the explanatory variables for the time period in question need also to be forecast (Morley, 1993). Furthermore, probability of a change in the relationships between variables increases with the projection period. While forecasts are formed from causal models, the modelled relationship between the variables will remain unchanged in the future (Uysal and Crompton, 1985). Other disadvantages of the causal approach involve cost and time requirements in order to gather and analyse the data (Frechtling, 1996). To do that a researcher must have adequate knowledge of the variables and to be able to use complicated software programs (Witt and Witt, 1992). According to Witt and Martin (1989), non-causal methods have been accused of being naive and not justifiable in the basis of theory, because of the notion that the factors, which have been the major reasons for growth in the past, will continue to be in the future. However, this assumption may not always be true and also it implies that tourism demand is stationary (Witt and Witt, 1995). Once more, according to Witt and Martin (1989), the reason for using non-casual approach is essentially pragmatic, and it believed to be associated with problems such as lack of data, inadequate information or limited user understanding on casual structure, cost and time constraints. Nevertheless, when in a case, fluctuations in the variable to be measured are caused by non-economic factors but rather by political events (terrorism, special events), seasonality or the weather, then, the non-causal technique would be more appropriate (Witt and Witt, 1995). On the practical side, non-causal techniques are more cost effective when compared to econometric models, due 5

6 to their limited data requirements, and they can be implemented in a shorter period time (Frechtling, 1996). After 30 years of forecasting research in tourism, it is very difficult to indicate which methodology is the most convenient for estimating future levels of tourists arrivals. Indeed, Fildes and Lusk (1984) argued that no reasonable forecaster could identify the best method form the various forecasting competitions and adopt the one of his/her forecasting problems. All forecasting formulas have distinct advantages and disadvantages. It is then a prerequisite for the forecaster to select and compare as many forecasting tools as the time and money would allow, so as having more chances to produce accurate results. REFERENCES Archer, B. (1987) Demand Forecasting and Estimation. In Travel Tourism and Hospitality Research, J. R. B. Ritchie and C. R. Goeldner, eds, pp New York: Wiley. Athiyaman, A., & Robertson, R. W. (1992). Time series forecasting techniques: Short-term planning in tourism. International Journal of Contemporary Hospitality Management, 4(4), Carey Goh and Rob Law, (2002), Modeling and forecasting tourism demand for arrivals with stochastic nonstationary seasonality and intervention, Tourism Management Volume 23, Issue 5, October 2002, Pages Chang, Y. M (1987), A Comprehensive Review of the Tourism Forecasting Literature. Journal of Travel Research 26(2) Cho, V., (2002), A comparison of three different approaches to tourist arrival forecasting, Tourism Management, In Press, Corrected Proof, Department of Management, The Hong Kong Polytechnic University, Hung Hom, Hong Kong. Frechtling, D., (1996), Practical Tourism Forecasting. Oxford: Butterworth Heineman. Dharmaratne, G. S Forecasting tourist arrivals in Barbados. Annals of Tourism Research 22: Fildes,R.and Lusk,E.J., (1984), The choice of a forecasting model,oinega,12 (5), H. Song and S.F. Witt, (2000), Tourism demand modelling and forecasting, Pergamon, Oxford. Johann du Preez & Stephen F. Witt, (2002), Univariate versus multivariate time series forecasting: an application to international tourism demand, International Journal of Forecasting, In Press, Corrected Proof, International Journal of Forecasting

7 Morley, CL 1993 Forecasting tourism demand using extrapolative time series methods, Journal of Tourism Studies, Australia, vol 4 pp 19-24, Moutinho, Luiz and Witt, Stephen F. (1995), 'Forecasting the tourism environment using a consensus approach', Journal of Travel Research, Vol. XXXIII, No. 4 (spring), pp S.F. Witt and C.A. Witt, (1995), Modelling and forecasting demand in tourism, Academic Press, San Diego. Stavridou P., (1998), Forecasting tourism demand for Greece, MSc Thesis University of Surrey. Stephen F Witt, Alan M Sykes and Mireille Dartus, (1995), Forecasting international conference attendance, Tourism Management, Volume 16, Issue 8, December 1995, Pages Uysal, Muzaffer, and J.L. Crompton. Deriving a Relative Price Index for Inclusion in International Tourism Demand Estimation Models. Journal of Travel Research. Volume 24, Number 1, Summer 1985, pp Witt, S and Mutinho, (1994), Public Sector Policies, Tourism Marketing and Management Handbook, Prentice, (2nd Ed) Cambridge. Witt, Stephen F. and Martin, Christine A. (1987), 'International tourism demand models-inclusion of marketing models', Tourism Management, Vol. 8, No. I (March), pp Witt, Stephen F. and Witt, Christine A. (1991), 'Tourism forecasting: Error magnitude, direction of change error, and trend change error', Journal of Travel Research, Vol. XXX, No. 2 (fall), pp Witt, Stephen F. and Witt, Christine A. (1992), Modeling and Forecasting Demand in Tourism, Academic Press, vii pp 7