A reinforcement learning approach to solving incomplete market models with aggregate uncertainty

Andrei Jirnyi () and Vadym Lepetyuk ()
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Andrei Jirnyi: Kellogg School of Management
Vadym Lepetyuk: Universidad de Alicante

Working Papers. Serie AD from Instituto Valenciano de Investigaciones Económicas, S.A. (Ivie)

Abstract: We develop a method of solving heterogeneous agent models in which individual decisions depend on the entire cross-sectional distribution of individual state variables, such as incomplete market models with liquidity constraints. Our method is based on the principle of reinforcement learning, and does not require parametric assumptions on either the agents' information set, or on the functional form of the aggregate dynamics.

Keywords: Heterogeneous agents; macroeconomics; dynamic programming; reinforcement learning. (search for similar items in EconPapers)
JEL-codes: C63 C68 E20 (search for similar items in EconPapers)
Pages: 23 pages
Date: 2011-09
New Economics Papers: this item is included in nep-dge and nep-mic
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:ivi:wpasad:2011-21

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