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Validating DSGE Models Through SVARs Under Imperfect Information

Paul Levine, Joseph Pearlman, Alessio Volpicella, Bo Yang Orcid Logo

Oxford Bulletin of Economics and Statistics

Swansea University Author: Bo Yang Orcid Logo

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DOI (Published version): 10.1111/obes.12683

Abstract

We study the ability of SVARs to match impulse responses of a well-established DSGE model where the information of agents can be imperfect. We derive conditions for the solution of a linearized NK-DSGE model to be invertible given this information set. In the absence of invertibility, an approximate...

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Published in: Oxford Bulletin of Economics and Statistics
ISSN: 0305-9049 1468-0084
Published: Wiley 2025
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URI: https://cronfa.swan.ac.uk/Record/cronfa69292
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spelling 2025-10-16T15:25:11.4870968 v2 69292 2025-04-15 Validating DSGE Models Through SVARs Under Imperfect Information d8e17e56a3b9484ba22c3d43807c83bd 0000-0001-5834-6002 Bo Yang Bo Yang true false 2025-04-15 SOSS We study the ability of SVARs to match impulse responses of a well-established DSGE model where the information of agents can be imperfect. We derive conditions for the solution of a linearized NK-DSGE model to be invertible given this information set. In the absence of invertibility, an approximate measure is constructed. An SVAR is estimated using artificial data generated from the model and three forms of identification restrictions: zero, sign and bounds on the forecast error variance. We demonstrate that a VAR may not recover a subset of structural shocks when imperfect information causes the underlying model to be non-invertible. Journal Article Oxford Bulletin of Economics and Statistics 0 Wiley 0305-9049 1468-0084 imperfect information, impulse responses, invertibility-fundamentalness, SVAR-DSGE comparisons, validation of DSGE models 25 4 2025 2025-04-25 10.1111/obes.12683 COLLEGE NANME Social Sciences School COLLEGE CODE SOSS Swansea University SU Library paid the OA fee (TA Institutional Deal) Swansea University 2025-10-16T15:25:11.4870968 2025-04-15T09:14:58.8504116 Faculty of Humanities and Social Sciences School of Social Sciences - Economics Paul Levine 1 Joseph Pearlman 2 Alessio Volpicella 3 Bo Yang 0000-0001-5834-6002 4 69292__34135__9b3a85a521a74f0aa76b815591396902.pdf 69292.VOR.pdf 2025-04-29T13:25:37.3415991 Output 1263722 application/pdf Version of Record true © 2025 The Author(s). Oxford Bulletin of Economics and Statistics published by Oxford University and John Wiley & Sons Ltd. This is an open access article under the terms of the Creative Commons Attribution License (CC BY). true eng http://creativecommons.org/licenses/by/4.0/
title Validating DSGE Models Through SVARs Under Imperfect Information
spellingShingle Validating DSGE Models Through SVARs Under Imperfect Information
Bo Yang
title_short Validating DSGE Models Through SVARs Under Imperfect Information
title_full Validating DSGE Models Through SVARs Under Imperfect Information
title_fullStr Validating DSGE Models Through SVARs Under Imperfect Information
title_full_unstemmed Validating DSGE Models Through SVARs Under Imperfect Information
title_sort Validating DSGE Models Through SVARs Under Imperfect Information
author_id_str_mv d8e17e56a3b9484ba22c3d43807c83bd
author_id_fullname_str_mv d8e17e56a3b9484ba22c3d43807c83bd_***_Bo Yang
author Bo Yang
author2 Paul Levine
Joseph Pearlman
Alessio Volpicella
Bo Yang
format Journal article
container_title Oxford Bulletin of Economics and Statistics
container_volume 0
publishDate 2025
institution Swansea University
issn 0305-9049
1468-0084
doi_str_mv 10.1111/obes.12683
publisher Wiley
college_str Faculty of Humanities and Social Sciences
hierarchytype
hierarchy_top_id facultyofhumanitiesandsocialsciences
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
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description We study the ability of SVARs to match impulse responses of a well-established DSGE model where the information of agents can be imperfect. We derive conditions for the solution of a linearized NK-DSGE model to be invertible given this information set. In the absence of invertibility, an approximate measure is constructed. An SVAR is estimated using artificial data generated from the model and three forms of identification restrictions: zero, sign and bounds on the forecast error variance. We demonstrate that a VAR may not recover a subset of structural shocks when imperfect information causes the underlying model to be non-invertible.
published_date 2025-04-25T05:27:47Z
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score 11.089386

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