Approximating small open economy models with neural network trained by genetic algorithm
Please cite this item using this persistent URLhttp://hdl.handle.net/11693/14800
This thesis work presents a direct numerical solution methodology to approximate the small open economy models with debt elastic interest rate premium and with convex portfolio adjustment cost, both studied by Stephanie SchmittGroh´e and Martin Uribe(2003). This recent method is compared with the firstorder approximation to the policy function from the aspect of second moments of endogenous variables and their impulse responses. The proposed methodology, namely genetic algorithm-neural network (GA-NN), parameterizes the policy function with a feed-forward neural network that is trained by a genetic algorithm. Thus, unlike the first-order approximation, GA-NN does not require the continuity and the existence of derivatives of objective and policy functions. Importantly, since genetic algorithm is an evolutionary algorithm that enables global search over the feasible set, it provides a robust result in any solution space. Also GA-NN method gives not only the moments of the model but also the optimal path.