Determinants versus Composite Leading Indicators in Predicting Turning Points in Growth Cycle

Abstract

Turning points occur when the growth rate moves from an expansion period to a contraction period or from a contraction period to an expansion period. To minimize risks, an accurate forecasting of turning points in travel demand is needed. To predict turning points in the Hong Kong inbound tourism growth cycle, this study estimated logistic and probit regression models in the first stage with tourism demand determinants such as income, price at the destination, price at the substitute destination, and oil price. Subsequently, logistic and probit regression models were estimated using the constructed composite leading indicator, OECD leading indicator, and business confidence indicator. These models were subsequently assessed using the quadratic probability score. The logit or probit model with composite leading indicator was found to be the best for predicting turning points. The findings revealed that the change in real income is the most important factor for the occurrence of turn in tourism growth cycle.