Are Neural Network Models Truly Effective at Forecasting? An Evaluation of Forecast Performance of Traditional Models with Neural Network Model for the Macroeconomic Data of G-7 Countries

Abstract

Forecasting macroeconomic and financial data are always difficult task to the researchers. Various statistical and econometrics techniques have been used to forecast these variables more accurately. Furthermore, in the presence of structural break, linear models are failed to model and forecast. Therefore, this study examines the forecasting performance of economic variables of G7 countries: France, Italy, Canada, Germany, Japan, United Kingdom and United States of America using non-linear autoregressive neural network (ARNN) model, linear auto regressive (AR) and Auto regressive integrated moving average model (ARIMA) models. The economic variables are inflation, exchange rate and Gross Domestic Product (GDP) growth for the period from 1970 to 2015. To measure the performance of the considered model Root, Mean Square Error, Mean Absolute Error and Mean Absolute Percentage Error are used. The results show that the forecasts from the non-linear neural network model are undoubtedly better as compared to the AR and the Box–Jenkins ARIMA models.