Forecasting GDP Growth Using ANN Model with Genetic Algorithm

Abstract

Applying nonlinear models to estimation and forecasting economic models are now becoming more common, thanks to advances in computing technology. Artificial Neural Networks (ANN) models, which are nonlinear local optimizer models, have proven successful in forecasting economic variables. Most ANN models applied in Economics use the gradient descent method as their learning algorithm. However, the performance of the ANN models can still be improved by using more flexible and general learning algorithm. In this paper, we develop an ANN model combined with Genetic Algorithm to forecast the Iranian GOP growth. In order to evaluate the performance of the model with other ANN and traditional econometric models, we compare the results of the model with other linear and nonlinear competing models such as ARMA, VAR, and ANN with gradient descent learning algorithm. We use the recently produced extended data on the Iranian GOP from 1937 to 2002. The results indicate that the GA can improve the forecasting performance of ANN model over other standard ANN and econometrics models.