{"title":"TOURISM ACCOMODATION INDICATORS’ IMPACT ON THE GDP – A GRANGER CAUSALITY APPROACH","authors":"Alexandru Manole, Andrei Buiga","doi":"10.52370/tisc2277am","DOIUrl":null,"url":null,"abstract":"In this paper, the authors study the impact of selected indicators of tourism accommodation on the Gross Domestic Product. The indicators are related to the number of nights spent, therefore they are not directly related to the GDP, thus justifying the choice of Granger causality as method to assess the impact. The fact that most of the variables are found to be not stationary leads to the application of a special technique, adapted to the dataset and software used. The VAR models used do not pass all the specification tests, the authors have chosen a conservative approach, considering the results achieved with caution. Following the application of the methodology, mixed results have been achieved, showing that there is some degree of causality, which cannot be, however, generalized.","PeriodicalId":371003,"journal":{"name":"The Seventh International Scientific Conference - THE FUTURE OF TOURISM","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"The Seventh International Scientific Conference - THE FUTURE OF TOURISM","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.52370/tisc2277am","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
In this paper, the authors study the impact of selected indicators of tourism accommodation on the Gross Domestic Product. The indicators are related to the number of nights spent, therefore they are not directly related to the GDP, thus justifying the choice of Granger causality as method to assess the impact. The fact that most of the variables are found to be not stationary leads to the application of a special technique, adapted to the dataset and software used. The VAR models used do not pass all the specification tests, the authors have chosen a conservative approach, considering the results achieved with caution. Following the application of the methodology, mixed results have been achieved, showing that there is some degree of causality, which cannot be, however, generalized.