Forecasting long-haul tourism demand for Hong Kong using error correction models

Forecasting accuracy is particularly important when forecasting tourism demand on account of the perishable nature of the product. This study compares a range of forecasting models in the context of predicting annual tourist flows into Hong Kong from the major long-haul markets of the US, the UK, Germany and major short-haul markets of China, Japan and Taiwan. Econometric forecasting models considered included Error Correction Models (ECMs) based on Permanent Income-Life Cycle (PI-LC) hypothesis (PI-LC ECM) and alternative cointegration approaches: Engle and Granger (1987), Johansen (1988), and Ordinary Least Square (OLS) approaches. Both Autoregressive Integrated Moving Average (ARIMA) and no change model (hereafter NAIVE) models are used as a benchmark time series model for accuracy comparisons. It was hypothesized that PI-LC ECM is a better forecasting model particularly for long-haul tourism demand. The objective of this article is to investigate whether the application of PI-LC ECM could improve the forecasting performance of econometric models relative to time series models. The forecasting results indicate that the PI-LC ECM based on the Engle-Granger (1987) approach produces more accurate forecasts than other alternative forecasting models for all long-haul markets based on Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) criteria. Overall, PI-LC ECMs produce better forecasts of tourism demand than the OLS, ARIMA and NAIVE models for all origin markets and all time horizons.