Approximating small open economy models with neural network trained by genetic algorithm
Author
Coşkun, Yeşim
Advisor
Alemdar, Nedim
Date
2008Publisher
Bilkent University
Language
English
Type
ThesisItem Usage Stats
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Abstract
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.