2022
DOI: 10.3390/ijerph192013482
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Did Human Microbes Affect Tourist Arrivals before the COVID-19 Shock? Pre-Effect Forecasting Model for Slovenia

Abstract: In 2020, with a substantial decline in tourist arrivals slightly before the time of COVID-19, the innovative econometric approach predicted possible responses between the spread of human microbes (bacteria/viruses) and tourist arrivals. The article developed a conceptually tested econometric model for predicting an exogenous shock on tourist arrivals driven by the spread of disease using a time series approach. The reworked study is based on an autoregressive integrated moving average (ARIMA) model to avoid sp… Show more

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Cited by 5 publications
(3 citation statements)
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“…Considering how many researchers have informed stakeholders about approaching pan-demics, high inflation rates, recessions, and wars is essential. These situations must be analyzed and predicted beforehand, not after the fact (Gričar and Bojnec 2022), as is commonly found in the tourism literature, which is surprising (Gössling et al 2021). The main research question in the present study is: When predicting tourism demand, what insights can be gained from analyzing time series data?…”
Section: Methodsmentioning
confidence: 99%
“…Considering how many researchers have informed stakeholders about approaching pan-demics, high inflation rates, recessions, and wars is essential. These situations must be analyzed and predicted beforehand, not after the fact (Gričar and Bojnec 2022), as is commonly found in the tourism literature, which is surprising (Gössling et al 2021). The main research question in the present study is: When predicting tourism demand, what insights can be gained from analyzing time series data?…”
Section: Methodsmentioning
confidence: 99%
“…The case illustrates an archetypal development of knowledge and how to transform this into a simulated, prediction-based model. A publication by Gričar and Bojnec (2022) provides another example of the development and application of such a model.…”
Section: Implications and Further Researchmentioning
confidence: 99%
“…On the other hand, time series models are also widely applied in tourism demand forecasting [75,76], typically using ARIMA and SARIMA models [77][78][79][80][81][82], the Kalman filter econometric-based methodology [83,84] and artificial intelligence-based methods [85,86] as [87] pointed out. To date, hybrid methods in tourism forecasting are not universally preferred [88].…”
Section: Introductionmentioning
confidence: 99%