The role of disaggregated search data in improving tourism forecasts: Evidence from Sri Lanka
Author(s)
Wickramasinghe, K
Ratnasiri, S
Griffith University Author(s)
Year published
2020
Metadata
Show full item recordAbstract
Formulation of effective policies to enhance the resilience of tourism following the COVID-19 pandemic essentially requires comprehensive empirical information on changes in tourism demand and associated economic costs. The paper makes a novel contribution to tourism literature by employing regionally and temporally disaggregated tourism data and Google search data in improving the accuracy of tourism forecasts. Further, the paper adopts two timeseries variables namely tourist arrivals and guest nights in order to understand the changes due to COVID-19 in tourism demand more comprehensively. Monthly data on international ...
View more >Formulation of effective policies to enhance the resilience of tourism following the COVID-19 pandemic essentially requires comprehensive empirical information on changes in tourism demand and associated economic costs. The paper makes a novel contribution to tourism literature by employing regionally and temporally disaggregated tourism data and Google search data in improving the accuracy of tourism forecasts. Further, the paper adopts two timeseries variables namely tourist arrivals and guest nights in order to understand the changes due to COVID-19 in tourism demand more comprehensively. Monthly data on international tourist arrivals, guest nights and Google trends from 2004 to 2019 are used to produce regionally disaggregated (Europe, Asia, the Pacific, America, Other) monthly tourism forecasts for Sri Lanka. We find that SARMAX models outperform the other models (ARIMA, ARIMAX, SARIMA) in forecasting tourism demand following COVID-19. Interestingly, the paper makes a further step in utilizing forecasts in estimating foregone economic benefits due to COVID-19 pandemic. We find a notable difference in estimated direct economic loss depending on the variable used in estimates. The percentage loss is 40% when arrival forecasts are used in estimates and 29% when guest night forecasts are used in estimates. This provides important policy implications for improving post-COVID tourism.
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View more >Formulation of effective policies to enhance the resilience of tourism following the COVID-19 pandemic essentially requires comprehensive empirical information on changes in tourism demand and associated economic costs. The paper makes a novel contribution to tourism literature by employing regionally and temporally disaggregated tourism data and Google search data in improving the accuracy of tourism forecasts. Further, the paper adopts two timeseries variables namely tourist arrivals and guest nights in order to understand the changes due to COVID-19 in tourism demand more comprehensively. Monthly data on international tourist arrivals, guest nights and Google trends from 2004 to 2019 are used to produce regionally disaggregated (Europe, Asia, the Pacific, America, Other) monthly tourism forecasts for Sri Lanka. We find that SARMAX models outperform the other models (ARIMA, ARIMAX, SARIMA) in forecasting tourism demand following COVID-19. Interestingly, the paper makes a further step in utilizing forecasts in estimating foregone economic benefits due to COVID-19 pandemic. We find a notable difference in estimated direct economic loss depending on the variable used in estimates. The percentage loss is 40% when arrival forecasts are used in estimates and 29% when guest night forecasts are used in estimates. This provides important policy implications for improving post-COVID tourism.
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Journal Title
Current Issues in Tourism
Note
This publication has been entered as an advanced online version in Griffith Research Online.
Subject
Tourism economics