In recent years, neural network techniques have been increasingly used for a wide variety of applications where statistical methods had been traditionally employed. Neural network techniques, for example, have been applied to problems like chemical process control, seismic signals interpretation, machines diagnostic, target marketing, economic forecasting, financial modelling, market share prediction, stock market prediction, and risk management. In contrast, traditional econometric approaches have continued to be used for prediction models in almost all the above areas. This paper proposes the extension of neural network techniques to include prediction models because of two obvious advantages. First, it does not require any assumptions about underlying population distribution; second, it is especially useful in cases where inputs are highly correlated or are missing, or where the systems are nonlinear. This paper presents a comparative case study between neural network and econometric approaches to predict GDP growth in Malaysia using knowledge based economy indicators based on time series data collected from 1995?2000. The findings indicate that the neural network technique has an increased potential to predict GDP growth based on knowledge based economy indicators compared to the traditional econometric approach
