Estimation of simultaneous econometric equations using neural networks

Presents an approach to formulating and estimating simultaneous equation based econometric models as neural network mapping problems. Conventional econometric methods are briefly surveyed. Motivation for neural network based simulation is discussed. A system of equations for the US economy is estimated using neural networks, and the results are compared with the popular two-stage least squares method. The results are comparable, indicating that the neural network based approach is promising. The pros and cons of this approach and possible future research are briefly discussed