Forecasting model and analyse of producers price index (PPI) in Turkey by using artificial neural network application

By the process of interaction between economics and statistics structural modelingand forecast modeling has an important role. Forecast modeling is importance because of itsrole in the decision mechanisms. These mechanisms can be considered in two groups. Whilethere exist some studies about measuring and improving the forecast accuracy on the oneside, there are some significant advances about new forecasting techniques on the other side.In line with these advances, some new forecasting methodologies have revealed. Oneof the most important new methodologies is the Artificial Neural Networks (ANN)technique. The ANN technique can be described as an information processing paradigminspired by the way the brain processes information. This method, which is characterized asthe universal function approximate in the literature, has many important features like abilityto learn from data, nonlinearity, generalization etc.In this study, the ANN technique is analyzed as the forecast modeling technique for amacroeconomic variable. Basically, ANN modeling technique is applied to Producers? PriceIndex (PPI) of the Turkish economy. Additionally, the results of the ANN methodology iscompared with some widely used econometric techniques which have high forecastingpower. Then, forecasting performance of the ANN model is compared with forecastingperformance of the Vector Autoregression and Box-Jenkins modeling techniques.Evaluation of the results obtained using the ANN methodology indicated that the ANNmodels can provide satisfactory forecasting performance. Additionally, comparison of theANN and traditional methodologies shows that the ANN modeling technique has a superiorforecasting performance.