2001), Approximating and Simulating the Stochastic Growth Model: Parameterized Expectations, Neural Networks and the Genetic Algorithm

This paper suggests a new approach to solving the one-sector stochastic growth model using the method of parameterized expectations. The approach is to employ a "global " genetic algorithm search for the parameters of the expectation function followed by a "local " gradient-descent optimization method to ensure fine-tuning of the approximated solution. We use this search procedure in combination with either polynomial or neural network specifications for the expectation function. We find that our approach yields highly accurate solutions in the case where an exact analytic solution exists as well as in cases where no closed-form solution exists. Our results further suggest that neural network specifications for the expectation function may be preferred to the more commonly used polynomial specification. JEL classification: C63; O41 Key words: Solution methods; stochastic growth model; parameterized expectations; genetic algorithm; neural networks