Forecasting Tourism Trends in Eastern Economic Corridor (EEC) with Google Trends
DOI:
https://doi.org/10.55164/ecbajournal.v16i4.267214Keywords:
Google Trends, Tourism, Mixed frequency, EEC, ForecastingAbstract
This study aims to investigate the potential of Google Trends variables in predicting short-term tourism trends and to assess the impact of the Coronavirus Disease 2019 (COVID-19) situation on the tourism industry in the Eastern Economic Corridor (EEC), which is a key source of household income. The data used in this research includes the number of tourists (monthly frequency) and Google Trends data related to tourism (weekly frequency), covering the period from January 2015 to June 2021. The study employs an Autoregressive model and the ADL-MIDAS model, followed by an out-of-sample forecast. The results indicate that Google Trends variables enhance the ability to explain changes in visitor counts with a positive correlation. Moreover, the forecast accuracy is improved by including Google Trends variables in the Autoregressive model. The ADL-MIDAS model, on the other hand, is more effective for forecasting under unique circumstances, such as during economic recessions or disease outbreaks. In addition, the study of the consequences of Coronavirus Disease 2019 is shown to reduce travel-related searches, visitor numbers, and tourism revenue. Policymakers can utilize Google Trends data to better anticipate and respond to such disruptions.
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