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

MoreLess

Year of publication:	2011
Authors:	Jirnyi, Andrei
Other Persons:	Lepetyuk, Vadym (contributor)
Publisher:	[2011]: [S.l.] : SSRN
Subject:	Theorie \| Theory \| Unvollkommener Markt \| Incomplete market \| Risiko \| Risk \| Lernprozess \| Learning process \| Entscheidung unter Unsicherheit \| Decision under uncertainty

freely available

Full text |

More access options

doi.org

Check Google Scholar

In German libraries (KVK)

I need help

Extent:	1 Online-Ressource (20 p)
Type of publication:	Book / Working Paper
Language:	English
Notes:	Nach Informationen von SSRN wurde die ursprüngliche Fassung des Dokuments May 5, 2011 erstellt
Other identifiers:	10.2139/ssrn.1832745 [DOI]
Classification:	C63 - Computational Techniques ; C68 - Computable General Equilibrium Models ; E20 - Consumption, Saving, Production, Employment, and Investment. General
Source:	ECONIS - Online Catalogue of the ZBW

Persistent link: https://www.econbiz.de/10013125597