Tourism Demand Forecasting - Time-Series Models
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.
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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. 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.
Tideswell, C., T. Mules and B. Faulkner. 2001. "An Integrative Approach to
Tourism Forecasting: A Glance in the Rearview Mirror." Journal of Travel
Research 40(November 2001):162-171. This study evaluated the results of tourism forecasting exercise in South
Australia for the period of 1996-1998. An integrated times-series and Delphi
process was used to forecast tourism demand from both the international and
domestic market. Three methods of generating time-series forecasts were
used: Holt's exponential smoothing; a "naive" method using average annual
rate of change from the past 11 years; and a linear trend using regression
analysis. A group of 26 tourism industry practitioners responded to a Delphi
survey and provided comments in a follow-up group meeting process. The final
forecasting method used was based on the results of the Delphi process.
Grouped forecasting trends (e.g. all international visitors) were quite
accurate, but forecasts for segments of the market (e.g. Japanese visitors)
were not as accurate. This was particularly true for small market segments.
Over all, the "naive" approach to demand forecasting was most accurate,
confirming the results of earlier studies. 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. Choy, D. J. L. 1984. "Forecasting Tourism Revisited." Tourism Management
5:171-176. This article examines the accuracy and efficiency of forecasting
techniques by applying time series regression to forecasting visitor
arrivals. Past studies have shown that simpler time series techniques
perform as well or better than complex forecasting models. Time series
regression is compared to a naive times series model. An assessment of
visitor forecasts developed at regional, destination and individual market
levels suggests that time series regression performs well in producing
annual forecasts of visitors. These forecasts can also serve as a baseline
for evaluating the net returns from applying more complex techniques.
Tourism managers should appreciate the usefulness of simpler formal methods
in developing forecasts of tourism.