References
- Bangwayo-Skeete, P. F., & Skeete, R. W. (2015). Can Google data improve the forecasting performance of tourist arrivals? Mixed-data Sampling Approach. Tourism Management, 46(1), 454–464.
- Baumeister, C., Guérin, P., & Kilian, L. (2015). Do high-frequency financial data help forecast oil prices? The MIDAS touch at work. International Journal of Forecasting, 31(2), 238–252. https://doi.org/https://doi.org/10.1016/j.ijforecast.2014.06.005
- Bhati, A. S., Mohammadi, Z., Agarwal, M., Kamble, Z., & Donough-Tan, G. (2021). Motivating or manipulating: The influence of health-protective behaviour and media engagement on post-COVID-19 travel. Current Issues in Tourism, 24(15), 2088–2095. https://doi.org/https://doi.org/10.1080/13683500.2020.1819970
- Chernis, T., Cheung, C., & Velasco, G. (2020). A three-frequency dynamic factor model for nowcasting Canadian provincial GDP growth. International Journal of Forecasting, 36(3), 851–872. https://doi.org/https://doi.org/10.1016/j.ijforecast.2019.09.006
- Chien, G. C. L., & Law, R. (2003). The impact of the severe acute respiratory syndrome on hotels: A case study of Hong Kong. International Journal of Hospitality Management, 22(3), 327–332. https://doi.org/https://doi.org/10.1016/s0278-4319(03)00041-0
- Coshall, J. T. (2009). Combining volatility and smoothing forecasts of UK demand for international tourism. Tourism Management, 30(4), 495–511. https://doi.org/https://doi.org/10.1016/j.tourman.2008.10.010
- Dergiades, T., Mavragani, E., & Pan, B. (2018). Google Trends and tourists’ arrivals: Emerging biases and proposed corrections. Tourism Management, 66, 108–120. https://doi.org/https://doi.org/10.1016/j.tourman.2017.10.014
- Fildes, R., Wei, Y., & Ismail, S. (2011). Evaluating the forecasting performance of econometric models of air passenger traffic flows using multiple error measures. International Journal of Forecasting, 27(3), 902–922. https://doi.org/https://doi.org/10.1016/j.ijforecast.2009.06.002
- Foroni, C., & Marcellino, M. (2014). A comparison of mixed frequency approaches for nowcasting Euro area macroeconomic aggregates. International Journal of Forecasting, 30(3), 544–568. https://doi.org/https://doi.org/10.1016/j.ijforecast.2013.01.010
- Gounopoulos, D., Petmezas, D., & Santamaria, D. (2012). Forecasting tourist arrivals in Greece and the impact of macroeconomic shocks from the countries of tourists’ origin. Annals of Tourism Research, 39(2), 641–666. https://doi.org/https://doi.org/10.1016/j.annals.2011.09.001
- Gunter, U., & Önder, I. (2016). Forecasting city arrivals with Google analytics. Annals of Tourism Research, 61(10), 199–212. https://doi.org/https://doi.org/10.1016/j.annals.2016.10.007
- Hassani, H., Silva, E. S., Antonakakis, N., Filis, G., & Gupta, R. (2017). Forecasting accuracy evaluation of tourist arrivals. Annals of Tourism Research, 63, 112–127. https://doi.org/https://doi.org/10.1016/j.annals.2017.01.008
- Havranek, T., & Zeynalov, A. (2019). Forecasting tourist arrivals: Google Trends meets mixed-frequency data. Tourism Economics, 27(1), 1–20. https://doi.org/https://doi.org/10.1177/1354816619879584
- Hirashima, A., Jones, J., Bonham, C. S., & Fuleky, P. (2017). Forecasting in a mixed up world: nowcasting Hawaii tourism. Annals of Tourism Research, 63, 191–202. https://doi.org/https://doi.org/10.1016/j.annals.2017.01.007
- Huang, B., & Hao, H. (2020). A novel two-step procedure for tourism demand forecasting. Current Issues in Tourism, 1–12. https://doi.org/https://doi.org/10.1080/13683500.2020.1770705.
- Irem, Ö., Ulrich, G., & Arno, S. (2019). Forecasting tourist arrivals with the help of web sentiment: A mixed-frequency modeling approach for big data. Tourism Analysis, 24(4), 437–452. https://doi.org/https://doi.org/10.3727/108354219X15652651367442
- Ivars-Baidal, J. A., Celdrán-Bernabeu, M. A., Mazón, J.-N., & Perles-Ivars, Á. F. (2019). Smart destinations and the evolution of ICTs: A new scenario for destination management? Current Issues in Tourism, 22(13), 1581–1600. https://doi.org/https://doi.org/10.1080/13683500.2017.1388771
- Knotek, E. S., & Zaman, S. (2019). Financial nowcasts and their usefulness in macroeconomic forecasting. International Journal of Forecasting, 35(4), 1708–1724. https://doi.org/https://doi.org/10.1016/j.ijforecast.2018.10.012
- Kuo, H. I., Chen, C. C., Tseng, W. C., Ju, L. F., & Huang, B. W. (2008). Assessing impacts of SARS and avian Flu on international tourism demand to Asia. Tourism Management, 29(5), 917–928. https://doi.org/https://doi.org/10.1016/j.tourman.2007.10.006
- Lahiri, K., & Monokroussos, G. (2013). Nowcasting US GDP: The role of ISM business surveys. International Journal of Forecasting, 29(4), 644–658. https://doi.org/https://doi.org/10.1016/j.ijforecast.2012.02.010
- Lahiri, K., & Monokroussos, G. (2015). Forecasting consumption: The role of consumer confidence in real time with many predictors. Journal of Applied Econometrics, 31(7), 1254–1275. https://doi.org/https://doi.org/10.1002/jae.2494
- Li, G., Wu, D. C., Zhou, M., & Liu, A. (2019). The combination of interval forecasts in tourism. Annals of Tourism Research, 75, 363–378. https://doi.org/https://doi.org/10.1016/j.annals.2019.01.010
- Li, H., Hu, M., & Li, G. (2020). Forecasting tourism demand with multisource big data. Annals of Tourism Research, 83. https://doi.org/https://doi.org/10.1016/j.annals.2020.102912.
- Macao Government. (2020). MGTO Special webpage against epidemics. https://www.ssm.gov.mo/apps1/PreventCOVID-19/en.aspx#clg17458
- Önder, I., & Gunter, U. (2016). Forecasting tourism demand with Google Trends for a major European city destination. Tourism Analysis, 21(2–3), 203–220. https://doi.org/https://doi.org/10.3727/108354216X14559233984773
- Önder, I., Gunter, U., & Scharl, A. (2019). Forecasting tourist arrivals with the help of web sentiment: A mixed-frequency modeling approach for big data. Tourism Analysis, 24(4), 437–452. https://doi.org/https://doi.org/10.3727/108354219X15652651367442
- Pan, B., Chenguang Wu, D., & Song, H. (2012). Forecasting hotel room demand using search engine data. Journal of Hospitality and Tourism Technology, 3(3), 196–210. https://doi.org/https://doi.org/10.1108/17579881211264486
- Pan, B., & Yang, Y. (2016). Forecasting destination weekly hotel occupancy with big data. Journal of Travel Research, 56(7), 957–970. https://doi.org/https://doi.org/10.1177/0047287516669050
- Park, E., Kang, J., Choi, D., & Han, J. (2020). Understanding customers’ hotel revisiting behaviour: A sentiment analysis of online feedback reviews. Current Issues in Tourism, 23(5), 605–611. https://doi.org/https://doi.org/10.1080/13683500.2018.1549025
- Prideaux, B., Laws, E., & Faulkner, B. (2003). Events in Indonesia: Exploring the limits to formal tourism trends forecasting methods in complex crisis situations. Tourism Management, 24(4), 475–487. https://doi.org/https://doi.org/10.1016/s0261-5177(02)00115-2
- Ritchie, B. W., & Jiang, Y. (2019). A review of research on tourism risk, crisis and disaster management: Launching the annals of tourism research curated collection on tourism risk, crisis and disaster management. Annals of Tourism Research, 79, 102812. https://doi.org/https://doi.org/10.1016/j.annals.2019.102812
- Schumacher, C., & Breitung, J. (2008). Real-time forecasting of German GDP based on a large factor model with monthly and quarterly data. International Journal of Forecasting, 24(3), 386–398. https://doi.org/https://doi.org/10.1016/j.ijforecast.2008.03.008
- Sun, Y.-Y. (2016). Decomposition of tourism greenhouse gas emissions: Revealing the dynamics between tourism economic growth, technological efficiency, and carbon emissions. Tourism Management, 55, 326–336. https://doi.org/https://doi.org/10.1016/j.tourman.2016.02.014
- Tse, A. C. B., So, S., & Sin, L. (2006). Crisis management and recovery: How restaurants in Hong Kong responded to SARS. Internatioal Journal of Hospitality Management, 25(1), 3–11. https://doi.org/https://doi.org/10.1016/j.ijhm.2004.12.001
- Volchek, K., Liu, A., Song, H., & BUhalis, D. (2019). Forecasting tourist arrivals at attractions: Search engine empowered methodologies. Tourism Economics, 25(3), 425–447. https://doi.org/https://doi.org/10.1177/1354816618811558
- Wang, L., Ma, F., Liu, J., & Yang, L. (2020). Forecasting stock price volatility: New evidence from the GARCH-MIDAS model. International Journal of Forecasting, 36(2), 684–694. https://doi.org/https://doi.org/10.1016/j.ijforecast.2019.08.005
- Wen, L., Liu, C., Song, H., & Liu, H. (2020). Forecasting tourism demand with an improved mixed data sampling model. Journal of Travel Research, 60(2), 336–353. doi: https://doi.org/10.1177/0047287520906220
- Wu, D. C., Song, H., & Shen, S. (2017). New developments in tourism and hotel demand modeling and forecasting. International Journal of Contemporary Hospitality Management, 29(1), 507–529. https://doi.org/https://doi.org/10.1108/ijchm-05-2015-0249
- Wu, E. H. C., Law, R., & Jiang, B. (2010). The impact of infectious diseases on hotel occupancy rate based on independent component analysis. International Journal of Hospitality Management, 29(4), 751–753. https://doi.org/https://doi.org/10.1016/j.ijhm.2009.07.001
- Yang, Y., & Zhang, H. (2019). Spatial-temporal forecasting of tourism demand. Annals of Tourism Research, 75, 106–119. https://doi.org/https://doi.org/10.1016/j.annals.2018.12.024
- Yu, G., & Schwartz, Z. (2006). Forecasting short time-series tourism demand with artificial intelligence models. Journal of Travel Research, 45(2), 194–203. doi:https://doi.org/10.1177/0047287506291594