Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/213847
Authors: 
Ponomarenko, Alexey
Year of Publication: 
2020
Citation: 
[Journal:] Economics: The Open-Access, Open-Assessment E-Journal [ISSN:] 1864-6042 [Volume:] 14 [Year:] 2020 [Issue:] 2020-3 [Pages:] 1-15
Abstract: 
The author set up a simplistic agent-based model where agents learn with reinforcement observing an incomplete set of variables. The model is employed to generate an artificial dataset that is used to estimate standard macro econometric models. The author shows that the results are qualitatively indistinguishable (in terms of the signs and significances of the coefficients and impulse-responses) from the results obtained with a dataset that emerges in a genuinely rational system.
Subjects: 
microfoundations
bounded rationality
reinforcement learning
agent- based model
JEL: 
B41
C63
D83
Persistent Identifier of the first edition: 
Creative Commons License: 
https://creativecommons.org/licenses/by/4.0/
Document Type: 
Article

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