Recently, the tourism sector has started to become an important sector for the economy of the countries. Sustainability of economic growth is one of the most important targets to be achieved for countries. When the economic growth of countries enters a systematic stalemate in certain periods, the tourism sector is seen as an alternative solution to overcome this dilemma. The reason why countries have increased the importance they attach to the tourism sector in recent times includes not only economic but also political, cultural and social reasons. Cultural, social and economic interactions between countries can be realized through tourism activities in the most effective way. With the increasing importance of the tourism sector in the economy, the number of studies examining the place of tourism indicators in the economy has increased. In this context, econometric analysis of tourism related indicators in the determination of tourism policies can draw a road map for decision makers. The course of tourism income over time attracts the attention of academicians and decision-makers. Because the course of this variable over time gives strong ideas for making predictions and decisions about the future. By using the previous period values of tourism income series, tourism incomes in the future can be predicted or the changes that will be made in tourism incomes by tourism investments can be estimated by econometric modeling. However, in order for the predictions and model estimations to yield reliable results, the average and variance of the tourism income series over time should remain constant, that is, it should be stable. Otherwise, the predictions made using the non-stationary tourism income series may be wrong and the estimation of the relationship between the tourism income variable and other variables may be false. In order to avoid these stalemates, the stagnation of the tourism income series should be examined to see whether the effects of sudden shocks on tourism incomes are permanent. 

In this context, this study, Turkey’s tourism incomes series of monthly time series properties have been previously studied using fourier stationarity test used not for tourism income series. In the studies examining the stagnation of the tourism income series, it is seen that unit root tests, which make seasonal adjustments, are generally used (Kulendran & Wong, 2005; Nikolaos, 2008; Hepkorucu & Doğan, 2019). Or, unit root tests of tourism series were used to examine structural changes using dummy variables (Narayan, 2005; Lean & Smyth, 2008; Lee & Chien, 2008). However, in cases where the number and location of structural changes are not known in the course of time, the use of dummy variables or using only seasonally corrected unit root tests may give unreliable results regarding the unit root process of the series. Fourier unit root tests, which allow accurate estimation of structural changes even in cases where the structure and location are not known, have shown strong results in the process followed by the data. Therefore, in this study, the unit root process of the tourism income series was examined by the stationarity test using fourier functions. The fourier stationarity test used in this study was performed by Becker et al. (2006). This stability test proposed by Becker et al. (2006) is an extension of the stability test developed by Kwitkowski et al. (1992) with fourier functions. In the Fourier KPSS stationarity test, structural changes were taken into account by using the Fourier function. The changes in the series can be predicted precisely by Fourier functions. In this study, Turkey’s stability characteristics of the data for the years 2012-2019 monthly tourism income variable is tested using fourier KPSS said stability test due to the strong features. According to the results of the KPSS Fourier stability test, the appropriate frequency for Turkey’s tourism serial number is calculated as 1. For the significance of Fourier terms, the F test statistic was calculated. Becker et al. (2006) ‘s study by comparing the critical values of the fourier terms were found to be significant. In this respect, it can be said that it is appropriate to use the fourier terms in the data creation process of the KPSS model used in stasis testing. Since the obtained Fourier KPSS test statistic was smaller than the critical values, the basic hypothesis could not be rejected. Turkey’s observed monthly data for the period 2012-2019, it has been concluded that there is stationary. The results obtained with this series is that Turkey’s tourism income stationary for the period studied. In other words, the effect of incoming shocks was temporary. This means that, in studies for Turkey and the policy decisions taken, predictions to be made by looking at the tourism income serial and model predictions will give reliable results.