Improving the forecasting of inbound tourism demand based on the mixed-frequency data sampling approach
Evidence from Australia
Bibliographic Data
| ID | 21699014 |
|---|---|
| Authors | Yuting Gong (0000-0003-4029-4130, Shanghai University), Mengjie Jin (0000-0003-2782-9399, Nanjing University of Finance and Economics), Kum Fai Yuen (0000-0002-9199-6661, Nanyang Technological University), Xueqin Wang (0000-0001-9134-5327, Chung-Ang University), Wenming Shi (0000-0001-6551-0499, Australian Maritime College, University of Tasmania, corresponding author) |
| Year | 2025 |
| Volume | 28 |
| Issue | 17 |
| Pages | 2744-2763 |
| Publication date | 2025-09-02 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Current Issues in Tourism (JOURNAL) |
| Journal identifiers | ISSN: 1368-3500 • E-ISSN: 1747-7603 |
| Publisher | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1080/13683500.2024.2381248 |
| OpenAlex | W4400778873 |
| Language | EN |
| Citations received | 1 |
| References cited | 52 |
This study explores how the mixed-frequency data sampling (MIDAS) approach enhances the forecasting of Australia’s inbound tourism demand by employing an autoregressive distributed lag (ARDL)-MIDAS model. The main findings are as follows: First, after capturing the effects of control variables, both daily exchange rate returns and daily exchange rate volatility affect Australia’s inbound tourism demand. Second, the monthly growth rate of inbound tourist arrivals follows a mean-reverting process and incorporating its historical fluctuation information from the past 3 months significantly increases the explanatory power of the ARDL-MIDAS model. Third, the results of the out-of-sample predictive performance indicate that the two MIDAS-based models significantly outperform the benchmark model and the other two candidate models due to the incorporation of intra-month exchange rate information. These findings provide insights into the forecasting of inbound tourism demand and lay the foundation for further tourism business planning, resource allocation, and policymaking
Business · Demand forecasting · Econometrics · Economics · Geography · Telecommunications · Tourism · Computer Science · Diverse Aspects of Tourism Research · Economic and Environmental Valuation · Wine Industry and Tourism · Marketing
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| Unique citing works | 1 |
|---|---|
| Citations per year | 1 |
| Citation span | 2026 - 2026 (1) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 1 |