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Density tourism demand forecasting revisited

Haiyan Song, Long Wen and Chang Liu

Annals of Tourism Research, 2019, vol. 75, issue C, 379-392

Abstract: This study used scoring rules to evaluate density forecasts generated by different time-series models. Based on quarterly tourist arrivals to Hong Kong from ten source markets, the empirical results suggest that density forecasts perform better than point forecasts. The seasonal autoregressive integrated moving average (SARIMA) model was found to perform best among the competing models. The innovation state space models for exponential smoothing and the structural time-series models were significantly outperformed by the SARIMA model. Bootstrapping improved the density forecasts, but only over short time horizons.

Keywords: Tourism demand; Density forecasts; Scoring rules; Bootstrap (search for similar items in EconPapers)
Date: 2019
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Handle: RePEc:eee:anture:v:75:y:2019:i:c:p:379-392