Forecasting Key Strategic Variables in the Casino Tourism Industry

Document Type


Publication Date

Spring 2005


We examine the issues of forecasting industry gross revenue models in the casino gaming industries of Nevada, Mississippi and Atlantic City. Industry gross revenues are a used as benchmarks for casino performance, a major source of state tax collection, an important part of a state’s tourism industry and an important point of consideration for states contemplating legalizing gambling. We improve upon Preez and Witt’s (2003) approach of aggregating or pooling multiple time-series tourism research. Our model divides the time-series forecasts into two separate components, seasonality and trend. The results show all three states have distinctly different monthly seasonal patterns. The states with multiple geographic reporting regions, Mississippi and Nevada, had conflicting seasonality effects. The two regions in Mississippi have no significant differences with seasonality. Nevada’s eight reporting regions, on the other hand, all follow different monthly seasonal patterns. These findings require that Nevada’s seasonality be addressed at the individual reporting region level, while Mississippi and Atlantic City can be analyzed at the aggregate state level. If a panel was constructed combining the individual Nevada regions or the aggregate Nevada state data with Mississippi and Atlantic City erroneous seasonal patterns would result. Moreover, combining area specific seasonal indices offsets one another resulting in forecasts with grossly underestimated seasonal fluctuations. Trend forecasting models and the presence of interventions such as September 11 are also shown to vary by region. In Mississippi, September 11 had an insignificant effect on either regions casino gaming revenues. The effects of the September 11 intervention vary by region in Nevada. Six of the eight regions within Nevada do not conform to the overall Nevada state model. Aggregating time series data between states or within Nevada will lead to more complex, less accurate forecasts. The results indicate that in most cases aggregated or pooled time-series data should not be used in estimation models centered on forecasting revenues for casino and gaming establishments.


Allied Academies International Conference


Memphis, TN

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