Modelling the Economic Growth of Türkiye Dependent on the Sectoral Energy Consumptions, Labour Force and the Capital Formation Using Machine Learning Methods

Abstract

Energy is an important input of the economic growth and the energy policies of countries are crucial for reaching their economic targets. In this study, the economic growth of Türkiye for the period of 1990-2021 is modelled dependent on the sectoral energy consumption, labour force and the capital formation. The dependent and independent data are taken from the respective sources and then the pairwise Granger causality test is applied on these data. As the next step, the economic growth of Türkiye is taken as the dependent variable and modelled as a function of the other variables. Considering the low number of samples available and the nonlinearity of the data, machine learning methods are utilized for the modelling. Specifically, deep learning multilayer perceptron networks which are coded in Python programming language are employed for the modelling of the economic growth. The 70% of the available data are used as the training data while 30% of the data are utilized as the test data. The training and test data are split using special classes of the Python programming language to provide objectivity. Then, the actual economic growth and the deep learning model results are graphed which show a high degree of overlap indicating the accuracy of the developed model. In addition, the performance metrics of the developed model namely the coefficient of determination, mean absolute error, mean absolute percentage error and the root mean square error are computed which also indicate the high accuracy of the developed model. The approach used for the modelling of the economic growth is considered to be useful for modelling the other econometric data related to the growth hypothesis.