Title :
Estimation of simultaneous econometric equations using neural networks
Author_Institution :
Coll. of Bus. & Manage., Maryland Univ., College Park, MD, USA
Abstract :
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
Keywords :
economic cybernetics; financial data processing; neural nets; US economy; neural networks; simulation; simultaneous econometric equations; two-stage least squares method; Econometrics; Economic forecasting; Educational institutions; Equations; Learning systems; Least squares methods; Mathematical model; Maximum likelihood estimation; Multi-layer neural network; Neural networks;
Conference_Titel :
System Sciences, 1991. Proceedings of the Twenty-Fourth Annual Hawaii International Conference on
Conference_Location :
Kauai, HI
DOI :
10.1109/HICSS.1991.184051
