A decision support system for in-sample simultaneous equation systems forecasting using artificial neural systems

摘要

Decision support systems have been proposed for many forecasting applications. Unfortunately no work has been done in the development of decision support systems for simultaneous equation systems (SESs) forecasting, a very complex and difficult forecasting problem. In this paper the applicability of an artificial intelligence technology, artificial neural systems, for decision support in SESs forecasting is shown. The discussion is focused on the multi-layer feed-forward neural network (MLFFNN). Performance of the MLFFNN versus traditional methods of SES forecasting is evaluated by comparing their in-sample forecast accuracy in a Monte Carlo experiment and on Klein's Model 1.