研究成果：Forecasting Tourism Demand With a New Time-Varying Forecast Averaging Approach

作者：YuyingSun，Jian Zhang，李新（通讯作者），Shouyang Wang

发表期刊：Journal of Travel Research（ABS4）

Existing research has shown that combination can effectively improve tourism forecasting accuracy compared with single model However, the model uncertainty and structural instability in combination for out-of-sample tourism forecasting may influence the forecasting performance. This paper proposes a novel forecast combination approach based on time-varying jackknife model averaging (TVJMA), which can more efficiently handle structural changes and nonstationary trends in tourism data. Using Hong Kong tourism demand from five major tourism source regions as an empirical study, we investigate whether our proposed nonparametric TVJMA-based approach can improve tourism forecasting accuracy further. Empirical results show that the proposed TVJMA-based approach outperforms other competitors including single model and three combination methods in most cases. Findings indicate the outstanding performance of our method is robust to various forecasting horizons and different estimation periods.

文献来源： Sun Y , Zhang J , Li X ,et al.Forecasting Tourism Demand With a New Time-Varying Forecast Averaging Approach[J].Journal of travel research: The International Association of Travel Research and Marketing Professionals, 2023.