Reinforcement Learning in a New Keynesian Model

Yang, Bo; Deák, Szabolcs; Levine, Paul; Pearlman, Joseph

doi:10.3390/a16060280

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Reinforcement Learning in a New Keynesian Model

Bo Yang

, Szabolcs Deák

, Paul Levine, Joseph Pearlman

Algorithms, Volume: 16, Issue: 6, Start page: 280

Swansea University Author: Bo Yang

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DOI (Published version): 10.3390/a16060280

Abstract

We construct a New Keynesian (NK) behavioural macroeconomic model with bounded-rationality (BR) and heterogeneous agents. We solve and simulate the model using a third-order approximation for a given policy and evaluate its properties using this solution. The model is inhabited by fully rational (RE...

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Published in:	Algorithms
ISSN:	1999-4893
Published:	MDPI AG 2023
Online Access:	Check full text
URI:	https://cronfa.swan.ac.uk/Record/cronfa63621
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first_indexed	2023-06-12T09:09:19Z
last_indexed	2023-06-12T09:09:19Z
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spelling	v2 63621 2023-06-12 Reinforcement Learning in a New Keynesian Model d8e17e56a3b9484ba22c3d43807c83bd 0000-0001-5834-6002 Bo Yang Bo Yang true false 2023-06-12 ECON We construct a New Keynesian (NK) behavioural macroeconomic model with bounded-rationality (BR) and heterogeneous agents. We solve and simulate the model using a third-order approximation for a given policy and evaluate its properties using this solution. The model is inhabited by fully rational (RE) and BR agents. The latter are anticipated utility learners, given their beliefs of aggregate states, and they use simple heuristic rules to forecast aggregate variables exogenous to their micro-environment. In the most general form of the model, RE and BR agents learn from their forecasting errors by observing and comparing them with each other, making the composition of the two types endogenous. This reinforcement learning is then at the core of the heterogeneous expectations model and leads to the striking result that increasing the volatility of exogenous shocks, by assisting the learning process, increases the proportion of RE agents and is welfare-increasing. Journal Article Algorithms 16 6 280 MDPI AG 1999-4893 new Keynesian behavioural model; heterogeneous expectations; bounded rationality; reinforcement learning 27 6 2023 2023-06-27 10.3390/a16060280 http://dx.doi.org/10.3390/a16060280 COLLEGE NANME Economics COLLEGE CODE ECON Swansea University Not Required The ESRC 2023-06-21T14:55:24.5276752 2023-06-12T10:01:40.2480605 Faculty of Humanities and Social Sciences School of Social Sciences - Economics Bo Yang 0000-0001-5834-6002 1 Szabolcs Deák 0000-0003-2467-3202 2 Paul Levine 3 Joseph Pearlman 4 63621__27919__26ebe41ab5a2461bba6b443b903b7ce4.pdf 63621.pdf 2023-06-21T14:53:36.3265335 Output 441731 application/pdf Version of Record true © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license https://creativecommons.org/licenses/by/4.0/ true eng https://creativecommons.org/licenses/by/4.0/
title	Reinforcement Learning in a New Keynesian Model
spellingShingle	Reinforcement Learning in a New Keynesian Model Bo Yang
title_short	Reinforcement Learning in a New Keynesian Model
title_full	Reinforcement Learning in a New Keynesian Model
title_fullStr	Reinforcement Learning in a New Keynesian Model
title_full_unstemmed	Reinforcement Learning in a New Keynesian Model
title_sort	Reinforcement Learning in a New Keynesian Model
author_id_str_mv	d8e17e56a3b9484ba22c3d43807c83bd
author_id_fullname_str_mv	d8e17e56a3b9484ba22c3d43807c83bd_***_Bo Yang
author	Bo Yang
author2	Bo Yang Szabolcs Deák Paul Levine Joseph Pearlman
format	Journal article
container_title	Algorithms
container_volume	16
container_issue	6
container_start_page	280
publishDate	2023
institution	Swansea University
issn	1999-4893
doi_str_mv	10.3390/a16060280
publisher	MDPI AG
college_str	Faculty of Humanities and Social Sciences
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hierarchy_top_title	Faculty of Humanities and Social Sciences
hierarchy_parent_id	facultyofhumanitiesandsocialsciences
hierarchy_parent_title	Faculty of Humanities and Social Sciences
department_str	School of Social Sciences - Economics{{{_:::_}}}Faculty of Humanities and Social Sciences{{{_:::_}}}School of Social Sciences - Economics
url	http://dx.doi.org/10.3390/a16060280
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description	We construct a New Keynesian (NK) behavioural macroeconomic model with bounded-rationality (BR) and heterogeneous agents. We solve and simulate the model using a third-order approximation for a given policy and evaluate its properties using this solution. The model is inhabited by fully rational (RE) and BR agents. The latter are anticipated utility learners, given their beliefs of aggregate states, and they use simple heuristic rules to forecast aggregate variables exogenous to their micro-environment. In the most general form of the model, RE and BR agents learn from their forecasting errors by observing and comparing them with each other, making the composition of the two types endogenous. This reinforcement learning is then at the core of the heterogeneous expectations model and leads to the striking result that increasing the volatility of exogenous shocks, by assisting the learning process, increases the proportion of RE agents and is welfare-increasing.
published_date	2023-06-27T14:55:24Z
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score	11.012924

Reinforcement Learning in a New Keynesian Model

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