Tourism Demand Forecasting - Econometric Models
Methodology  -  Lake State Examples - Other Examples         
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Methodology:

Uysal, M. and J. L. Crompton. 1985. "An Overview of Approaches Used to Forecast Tourism Demand." Journal of Travel Research 23(Spring):7-15.

This paper presents a brief review of the tourism forecasting literature as of 1985. Three qualitative techniques are examined: simple survey techniques, Delphi models and judegement-aided models. Three quantitative techniques are also reviewed: Time-series, gravity and trip generation models and multivariate regression models.

Archer, B. H. 1980. "Forecasting Demand, Quantitative and Intuitive Techniques." International Journal of Tourism Management 1(1):5-12.

This paper reviews the art of demand forecasting in tourism as of 1980. Time-series analysis, the causal methods of multivariate regression and gravity and trip generation models, and the qualitative techniques of surveys, expert group processes and Delphi modeling are all discussed. The author concludes that integrated techniques, which combine quantitative methods with expert judgment may be the most accurate.

Archer, B. H. 1976. Demand Forecasting in Tourism. Bangor, University of Wales Press.

This book explores the sate of the art of tourism forecasting in 1976. In particular the techniques of multi-variable regression models, gravity and trip generation models, linear system analysis and expert-based delphi models are explained. The components and theory of tourism demand are detailed and the theoretical basis of each model is outlined.

Lake States Examples:

Other Examples:

Song, H., S. F. Witt and T. C. Jensen. 2003. "Tourism forecasting: accuracy of alternative econometric models." International Journal of Forecasting 19(1):123-141.

The accuracy of econometric tourism demand models has been the subject of considerable academic investigation and literature over the past several decades. Despite the theoretical elegance of the models, in most cases they have poorer accuracy than more simple techniques. This paper claims to present the most comprehensive comparison to date of the performance of econometric forecasting models with a tourism context. The six econometric models evaluated are all special cases of a general autoregressive distributed lag specification. Two time-series models are also evaluated for baseline comparison. Each of the models are evaluated for inbound tourism to Denmark for one, two, three and four year predictions. The time-varying parameter (TVP) and long-run static cointegration regression model perform most consistently. The naive or no-change time-series model is not far behind in terms of accuracy, especially for predictions in years one and two.

Greenidge, K. 2001. "Forecasting Tourism Demand: An STM Approach." Annals of Tourism Research 28(1):98-112.

This study utilized a Structural Time Series Model to explain and forecast tourist arrivals to Barbados from its major generating markets. A structural times series model combines econometric regression and time series analysis. This study was able to capture most of the information that is normally left in the residuals of common tourism demand regression models. Furthermore, it was able to do so using components which have direct interpretations and which can give the planner further insights into tourism behavior.

Kulendran, N. and S. F. Witt. 2001. "Cointegration Versus Least Squares Regression." Annals of Tourism Research 28(2):291-311.

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 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 model.

Martin, C. A. and S. F. Witt. 1989. "Accuracy of Econometric Forecast in Tourism." Annals of Tourism Research 16:407-428.

This paper presents the results of a study which compared the accuracy of several econometric forecasting models. The models evaluated include a log-linear econometric model, two "naive" models, an exponential smoothing model, a trend curve analysis model, a Gompertz trend curve model and a step-wise autoregression model. Each model was used to predict the travel flows of international tourists 1 and 2 years in the future. A naive "no-change" model was found to be more accurate than econometrics in 70% of the cases.

Witt, S. F. and C. A. Martin. 1987. "Econometric Models for Forecasting International Tourism Demand." Journal of Travel Research 26(Winter):23-30.

This study builds a set of econometric international tourism demand models for various worldwide destinations for tourists originating in the U.K. and in Germany. The equations were estimated using ordinary least squares, but where the Durbin-Watson statistic indicated the presence of autocorrelation the equation was re-estimated using the Cochrane-Orcutt iterative procedure. The models indicate that different factors lead to tourism demand in Germany and the U.K. British vacationers are more likely to regard foreign holidays as "luxuries" whereas Germans regard them more as "necessities". U.K. vacationers demonstrated a higher "brand loyalty" than Germany tourist suggesting more promotion would be required to get U.K. visitors to switch destination markets. German vacationers were more likely to respond to differences in price and quality.

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