Stephen F. Witt

Forecasting tourism demand: A review of empirical research

Abstract Accuracy is particularly important when forecasting tourism demand on account of the perishable nature of the product. The main methods used to forecast tourism demand which are reported in published empirical studies are discussed, together with the empirical findings. The vast majority of such studies are concerned with econometric modelling/forecasting, and the most appropriate explanatory variables are examined. Particular emphasis is placed on empirical comparisons of the accuracy of tourism forecasts generated by different techniques. Considerable scope exists for improving the model specification techniques employed in econometric forecasting of tourism demand. Furthermore, no single forecasting method performs consistently best across different situations, but autoregression, exponential smoothing and econometrics are worthy of consideration as alternatives to the no change model.

Recent Developments in Econometric Modeling and Forecasting

Eighty-four post-1990 empirical studies of international tourism demand modeling and forecasting using econometric approaches are reviewed. New developments are identified, and it is shown that applications of advanced econometric methods improve the understanding of international tourism demand. An examination of the 22 studies that compare forecasting performance suggests that no single forecasting method can outperform the alternatives in all cases. The time-varying parameter (TVP) model and structural time-series model with causal variables, however, perform consistently well.

Tourism as experience: The case of heritage parks.

Abstract The need to consider the experiences and benefits gained by visitors to tourism attractions is addressed, with specific reference to an industrial heritage park. The differing dimensions of experience and the various benefits are examined, as well as factors having influence on them. The consumer groups defined in terms of experiences and benefits derived are described in terms of their motivations for visiting and socioeconomic profile. The study raises questions concerning the usefulness of past emphases on sociodemographic analyses at heritage attractions, as experiential and benefit segmentations appear to be somewhat independent of sociodemographic attributes.

Cointegration versus least squares regression

Abstract Least squares regression models that explain international tourism demand have been shown to generate less accurate forecasts than the naive “no change” model. This study investigates if the reason for such mediocre forecasting performance is the failure to adopt recent developments in econometric methods in the areas of cointegration, error correction models, and diagnostic checking. The empirical results demonstrate that the forecasts produced using these recent methodological developments are more accurate than those generated by least squares regression, but that these newer econometric models still fail to outperform the “no change” model, as well as statistical time series models.

Econometric Models for Forecasting International Tourism Demand

This article presents a set of econometric models for forecasting interna tional tourism demand. The models are developed from tourist visits from West Germany and the United Kingdom to their respective tourist destinations.

Tourism forecasting: accuracy of alternative econometric models

Abstract This study evaluates the forecasting accuracy of six alternative econometric models in the context of the demand for international tourism in Denmark. These econometric models are special cases of a general autoregressive distributed lag specification. In addition, the forecasting accuracy of two univariate time series models is evaluated for benchmark comparison purposes. The forecasting competition is based on annual data on inbound tourism to Denmark. Individual models are estimated for each of the six major origin countries over the period 1969–93 and forecasting performance is assessed using data for the period 1994–97. Rankings of these forecasting models over different time horizons are established based on mean absolute percentage error and root mean square percentage error.

Univariate versus multivariate time series forecasting: an application to international tourism demand

Abstract Tourist numbers from several origin countries to a particular destination country form a vector series. In the presence of a ‘rich’ cross-correlation structure, that is if after allowing for autocorrelation the sample cross-correlation function exhibits meaningful and statistically significant correlations, the accuracy when forecasting a particular origin–destination tourist flow is likely to be improved by utilising information from the other tourist flows. Multivariate time series models may be expected to generate more accurate forecasts than univariate models in this setting. However, in the absence of these conditions, univariate forecasting models may well outperform multivariate models. An empirical investigation of tourism demand from four European countries to the Seychelles shows an absence of such a ‘rich’ structure and that ARIMA exhibits better forecasting performance than univariate and multivariate state space modelling. One implication that an absence of a ‘rich’ cross-correlation structure holds for econometric modelling is that explanatory variables which are strongly correlated with the tourist flow series are likely to be uncorrelated across origin countries.

Substitute prices in models of tourism demand

Abstract Models of international tourism demand which incorporate substitute prices as explanatory variables are specified. In tourism there are two price components, the transport cost to the destination and the living cost in the destination, and hence it is necessary to allow for both costs to and in substitute destinations. The substitute price variables are constructed using a weighting system based on market shares of major competing destinations, but the weights are allowed to alter throughout the estimation period to cater for changing trends. The empirical results support the hypothesis that substitute prices play an important role in determining the demand for international tourism, but there is considerable variation in importance according to the origin under consideration and the mode of transport

The Advanced Econometrics of Tourism Demand

1. Introduction to Tourism Demand Analysis 2. Recent Developments in Tourism Demand Analysis 3. Traditional Methodology of Tourism Demand Modelling 4. General-to-Specific Modelling 5. Cointegration 6. Error Correction Model 7. Vector Autoregression (VAR) and Cointegration 8. Time Varying Parameter Modelling 9. Panel Data Analysis 10. Systems of Demand Equations 11. Evaluation of Forecasting Performance

Tourism Demand Forecasting: A Time Varying Parameter Error Correction Model

The advantages of error correction models (ECMs) and time varying parameter (TVP) models have been discussed in the tourism forecasting literature. These models are now combined to give a new single-equation model, the time varying parameter error correction model (TVP-ECM), which is applied for the first time in the context of tourism demand forecasting. The empirical study focuses on tourism demand, measured by tourism spending per capita, by U.K. residents for five key Western European destinations. The empirical results show that the TVP-ECM can be expected to outperform a number of alternative econometric and time-series models in forecasting the demand for tourism, especially in forecasting the growth rate of tourism demand. A practical implication of this result is that the TVP-ECM approach should be used when forecasting tourism growth is concerned.