Obtaining simultaneous equation models from a set of variables through genetic algorithms

Traditionally, Simultaneous Equation Models (SEM) have been developed by people with a wealth of experience in the particular problem represented by the model. Developing a SEM is very difficult when there is a large number of variables. It would be useful to have an algorithm which gives a satisfactory SEM according to an information criterion. Because of the huge number of SEM possible, exhaustive search methods are not well suited, so an algorithm to obtain a SEM from a set of variables has been designed. The algorithm combines genetic and greedy methods. The behaviour of the algorithm is studied, and the results of some experiments are discussed.