A quantitative approach to passenger car demand in Turkey

This PHd thesis titled as "A Quantitative Approach to Passenger Car Demand in Turkey" is consisted of three parts, namely, demand concept, theoretical approaches that explain demand function (demand theories) and identification of personal automobile demand function, demand forecasting and forecast of Turkish passenger car demand. In the former chapter, demand and demand related concepts are explained, theoretical approaches that explain demand function (demand theories) and personal automobile demand function's identification is presented. Passenger car demand function which was formed by examining demand theories is:D = f (P1, P2, M, S1, S2, i) P1 : passenger car price, P2 : fuel price, S1 : country's savings volume, S2 : consumer loans volume, M : total GDP, i : consumer loans interest rate.This part that constitutes the theoretical structure of the thesis is also expected to contribute to the Turkish literature especially with regard to modern demand theories.In the latter chapter, demand forecasting strategies, demand forecasing methodology, relationship between demand theories and demand forecasting techniques, and demand forecasting techniques are presented. Because it is the theoretical base of the application, quantitative demand forecasting techniques are given weight in this chapter. In the third and the last chapter, which provides the originality of the thesis, proves that the passenger car demand in Turkey may only be forecasted through econometric models with adhering to the demand forecasting methodology that is presented in the second chapter. Application of the generated econometric model was practiced by both traditional and modern forecasting methods which are multiple regression as the traditional method and the artificial neural networks as its modern counterpart. Because of the nonlinear patterns in the demand, and high correlations between explanatory variables; multiple regressions pattern recognition and generalization abilities were not enough for covering this econometric model. The artificial neural networks technique enabled the elemination of some of these drawbacks, thus enhancing the forecasts' performance and accuracy. This is why modern methods such as the artificial neural network, which is assesed to have the ability to pattern recognition and to generalise the nonlinear patterns should be the method of choice instead of traditional methods when forecasting the passenger car demand in Turkey. Regarding thesis assumptions, it is reasonable to conclude that personal automobile demand is expected to rise however with a rapidly decreasing accelaration within the next five years. Taking current developments in the economical conjuncture into account this outcome is assesed as a not-so-remote possibility.