Improving Forecasting Accuracy by Combining Statistical and Judgmental Forecasts in Tourism

Abstract

This study aims to examine the forecasting accuracy of a combined method by using quarterly tourism arrival data in Hong Kong. The voluntary integration of statistical forecasts and experts’ judgmental revisions are achieved through a Delphi procedure in a Web-based Tourism Demand Forecasting System (TDFS). The forecasting performance is evaluated using the absolute percentage error (APE), mean absolute percentage error (MAPE) and root mean square percentage error (RMSPE). This study also compares the forecast performance of the combined method to a number of alternative forecasting models, i.e. the Naïve models, exponential smoothing and Box-Jenkins time-series models. The empirical results show that the combined forecasts consistently outperform the baseline forecasts obtained from vector autoregression (VAR) models over the forecasting period of 2008Q1–2011Q4, suggesting the value of adopting such an integration procedure. The findings indicate that some gains are obtained from integrating experts’ judgments into statistical forecasts, particularly for the short term. In addition, combined forecasting does not always lead to satisfactory forecasting performance, particularly when there is a lack of important contextual information.