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

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

12 Scopus Citations
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Original languageEnglish
Pages (from-to)527-549
Journal / PublicationApplied Economics
Volume43
Issue number5
Publication statusPublished - Feb 2011

Abstract

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 NAÏVE) 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 NAÏVE models for all origin markets and all time horizons. © 2011 Taylor & Francis.