Analysis of Macroeconomic Variables Affecting International Tourism Consumption in Sweden

Sammanfattning: There is an evident trend of growing tourism in the world. Tourism in Sweden is gaining more economic and social attention. The main purpose of this thesis is to discover what macroeconomic variables contribute to the annual international tourism income in Sweden. A multiple linear regression approach over a time period of 1978-2017 is used for the analysis. The final results show that GDP is the major macroeconomic factor that drives the annual international tourism income in Sweden across all time periods. NOK-SEK exchange rate seem to another relevant variable in the long term from 1978-2017, but not in shorter periods of time. USD-SEK exchange rate and unemployment rate hold no significant relevance to the international tourism consumption in Sweden for all time. The devaluation of Swedish krona in 1992 did not change the relationship between these variables and the response variable. However, these results can be unstable due to the limited number of observations used in the analysis, and therefore, we recommend other regression approaches, such as panel data regression, for this subject.There is an evident trend of growing tourism in the world. Tourism in Sweden is gaining more economic and social attention. The main purpose of this thesis is to discover what macroeconomic variables contribute to the annual international tourism income in Sweden. A multiple linear regression approach over a time period of 1978-2017 is used for the analysis. The final results show that GDP is the major macroeconomic factor that drives the annual international tourism income in Sweden across all time periods. NOK-SEK exchange rate seem to another relevant variable in the long term from 1978-2017, but not in shorter periods of time. USD-SEK exchange rate and unemployment rate hold no significant relevance to the international tourism consumption in Sweden for all time. The devaluation of Swedish krona in 1992 did not change the relationship between these variables and the response variable. However, these results can be unstable due to the limited number of observations used in the analysis, and therefore, we recommend other regression approaches, such as panel data regression, for this subject.