Author(s)

Althea Dianne La Foucade, Samuel Gabriel, Ewan Scott, Karl Theodore, Charmaine Metivier^*

Centre for Health Economics, The University of the West Indies, HEU, St. Augustine Campus, St. Augustine, Trinidad and Tobago.
The University of the West Indies, HEU, Centre for Health Economics, St. Augustine, Trinidad and Tobago.

This paper examines the predictive capacity of two Grey Systems Forecasting models. The original Grey GM(1,1) Forecasting model, introduced by Deng [1] [2] together with an improved Grey GM(1,1) model proposed by Ji et al. [3] are used to forecast medical tourism demand for Bermuda. The paper also introduces a quasi-optimization method for the optimization of the alpha (weight) parameter. Five steps ahead out-of-sample forecasts are produced after estimating the models using four data points. The results indicate that the optimization of the alpha parameter substantially improves the predictive accuracy of the models; reducing the five steps ahead out-of-sample Mean Absolute Percentage Error from roughly 7% to roughly 3.80% across the two models. Largely, the forecasting approaches demonstrate significant potential for use as an alternative to the traditional forecasting methods in circumstances where substantial amounts of high-quality data are not available.

Medical Tourism, Medical Tourism Demand, Grey Forecasting and Parameter Optimization

La Foucade, A. , Gabriel, S. , Scott, E. , Theodore, K. and Metivier, C. (2019) A Survey of Selected Grey Forecasting Models with Application to Medical Tourism Forecasting. Theoretical Economics Letters, 9, 1079-1092. doi: 10.4236/tel.2019.94070.