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            <title xml:lang="en">The Summer's Call. A Forecasting Framework for Tourism Demand in Corsica</title>
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                <forename type="first">Jean-Toussaint</forename>
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                <title xml:lang="en">The Summer's Call. A Forecasting Framework for Tourism Demand in Corsica</title>
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                    <forename type="first">Jean-Toussaint</forename>
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                <term xml:lang="en">Tourism  forecasting</term>
                <term xml:lang="en">Time Series</term>
                <term xml:lang="en">Tourism Economics</term>
                <term xml:lang="en">Transportation</term>
                <term xml:lang="en">Corsica</term>
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              <classCode scheme="jel" n="L.L8.L83">L - Industrial Organization/L.L8 - Industry Studies: Services/L.L8.L83 - Sports • Gambling • Restaurants • Recreation • Tourism</classCode>
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              <p>Tourism is a vital resource for small island economies, and Corsica is no exception. Although it is difficult to estimate precisely the contribution of tourism to Corsica’s gross domestic product, official statistics highlight the importance of the sector for the island’s economy. The analysis we are proposing, one of the first to focus specifically on Corsican tourism to the best of our knowledge, uses several econometric models (SARIMA, SVAR, DCC-GARCH) to lay the foundations for a monitoring and forecasting framework. Using data on air and sea arrivals between January 2001 and July 2023, SARIMA shows a positive effect of past tourist arrivals, French, Italian and German household confidence, and oil prices. Covid-19 harms demand, while inflation has no effect. The results obtained from the S(easonal)VAR show only a positive effect from past arrivals and a negative effect from Covid-19. While the pandemic had an immediate effect on arrivals to Corsica in 2020 and 2021 (−43% of arrivals during the tourist season - April-August - 2020 compared with 2019; −16% for 2021), we note that the consequences of Covid-19 are not persistent, demonstrating a significant level of resilience. The SARIMA model has better forecasts than the SVAR model, with an average error (underestimation of real demand) of 2% and 5%, which seems reasonable for a first draft. In the future, further modelling involving AI models, sentiment analysis and high frequency data could enhance our monitoring and forecasting capabilities.</p>
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