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

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.

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Year of publication:	2011-09
Authors:	Jirnyi, Andrei ; Lepetyuk, Vadym
Institutions:	Instituto Valenciano de Investigaciones Económicas (IVIE)
Subject:	Heterogeneous agents \| macroeconomics \| dynamic programming \| reinforcement learning

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Extent:	application/pdf
Series:	Working Papers. Serie AD.
Type of publication:	Book / Working Paper
Notes:	Published by Ivie 23 pages
Classification:	C63 - Computational Techniques ; C68 - Computable General Equilibrium Models ; E20 - Consumption, Saving, Production, Employment, and Investment. General
Source:	RePEc - Research Papers in Economics

Persistent link: https://www.econbiz.de/10009321868