| Publisher | University of Pittsburgh | ||
|---|---|---|---|
| Format | 142.0KB PDF, requires Acrobat Rdr 5 | Date added | 02 Jul 2001 |
| Topics | Artificial Intelligence | ||
| Downloads | 80 | ||
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.
This paper compares alternative methods for approximating and solving the stochastic growth using the method of parameterized expectations. We distinguish
between polynomial and neural network specifications for expectations and between gradient-descent and genetic algorithm methods for solving these models of parameterized expectations.
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