Predicting Macroeconomic Time Series In Malaysia : Using Neural Network Approaches

In recent years, neural networks have received an increasing amount of intention among macroeconomic forecasters because of their potential to detect and reproduce linear and nonlinear relationship among a set of variables. This study provides an introduction to neural networks and its establishment to standard econometric techniques. An empirical results in forecasting macroeconomic variables to GDP growth in Malaysia was initially introduced. For both the in-sample and the out-of-sample periods, the forecasting accuracy of the neural network is found to be superior to a well established linear regression model, with the error reduction ranging 8 per cent to 57 per cent. A throughout review of the literature suggests that neural networks are generally more
accurate than linear models for out-of-sample forecasting of economic output and various financial variables such as stock prices. However, the literature should still be
considered inconclusive due to the relatively small number of reliable studies on the macroeconomic forecasting. The full potential of neural networks can probably be exploited by using them in conjunction with linear regression models. Hence, neural networks should be viewed as an additional tool to be included in the toolbox of macroeconomic forecasters.