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A Comparison of Biased Estimation Methods for Predicting Tourism Income of Turkey

E. Polat


Tourism sector in Turkey has shown great progress since 1980s. Contribution of foreign currency, while the country was having economic problems, helped to decrease foreign debt and unemployment. The expenditures made for requirements such as accommodation, travel-fun, food-beverage, transportation, shopping, souvenir etc. by tourists in a country, where they visited, will cause the increase of that country’s income. More clearly, the tourists’s expenditures will generate or contribute to people’s income in that country. Hence, in this study tourism income is considered as a dependent variable. The annual data set of Turkey (1985-2014), including the five factors affecting the tourism income, is examined. Biased estimation methods Ridge Regression, Principal Component Regression and Partial Least Squares Regression methods are compared in terms of fitting to data and predictive ability. Therefore, best model, giving the best prediction of tourism income, is selected.


multicollinearity, principal component regression, partial least squares regression, ridge regression, prediction, tourism income.

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