Using a logistic growth regression model to forecast the demand for tourism in Las Vegas

• Logistic regression model is applied to the demand for Las Vegas tourism.
• Parameters estimated by ordinary least squares method are used to forecast the demand for Las Vegas tourism.
• Forecasts are judged by two accuracy criteria: MAPE and RMSPE.
• Results are compared with those of two benchmark models: Seasonal ARIMA and Naïve 1.
• Diebold-Mariano statistic is used to test for statistically significant differences in the forecasting ability.

For many years significant attention has been devoted to the application of forecasting models, both causal and time series, to the demand for tourism. However, most studies use national data and only a few are destination specific. The present paper applies a logistic growth forecasting model to tourist demand for Las Vegas and the empirical results indicate a superiority of logistic growth model when compared to the benchmark seasonal autoregressive integrated moving average (SARIMA) and Naïve 1 models. Based on the accuracy criteria of mean absolute percentage error and root mean square percentage error, the present study demonstrates that forecasts of tourism demand obtained by logistic growth forecasting model are more accurate (and hence more useful to tourism managers and planners) than forecasts obtained through any of the two benchmark models.