dc.contributor.author | Carriero, A | |
dc.contributor.author | Marcellino, M | |
dc.contributor.author | Tornese, T | |
dc.date.accessioned | 2024-02-28T12:40:07Z | |
dc.date.available | 2024-02-22 | |
dc.date.available | 2024-02-28T12:40:07Z | |
dc.identifier.citation | Andrea Carriero, Massimiliano Marcellino, Tommaso Tornese,
Blended identification in structural VARs,
Journal of Monetary Economics,
2024,
103581,
ISSN 0304-3932,
https://doi.org/10.1016/j.jmoneco.2024.103581.
(https://www.sciencedirect.com/science/article/pii/S0304393224000345)
Abstract: The proposed blended approach combines identification via heteroskedasticity with sign/narrative restrictions, and instrumental variables. Since heteroskedasticity can point identify shocks, its use results in a sharp reduction of the potentially large identified sets stemming from other approaches. Conversely, sign/narrative restrictions or instrumental variables offer natural solutions to the labeling problem and can help when conditions for point identification through heteroskedasticity are not met. Blending these methods together resolves their respective key issues and leverages their advantages. We illustrate the benefits of the approach in Monte Carlo experiments, and apply it to several examples taken from the literature.
Keywords: SVAR; Identification; Heteroskedasticity; Sign restrictions; Proxy variables | |
dc.identifier.issn | 1873-1295 | |
dc.identifier.uri | https://qmro.qmul.ac.uk/xmlui/handle/123456789/94952 | |
dc.description.abstract | The proposed blended approach combines identification via heteroskedasticity with sign/narrative restrictions, and instrumental variables. Since heteroskedasticity can point identify shocks, its use results in a sharp reduction of the potentially large identified sets stemming from other approaches. Conversely, sign/narrative restrictions or instrumental variables offer natural solutions to the labeling problem and can help when conditions for point identification through heteroskedasticity are not met. Blending these methods together resolves their respective key issues and leverages their advantages. We illustrate the benefits of the approach in Monte Carlo experiments, and apply it to several examples taken from the literature. | |
dc.publisher | Elsevier | en_US |
dc.relation.ispartof | Journal of Monetary Economics | |
dc.rights | © 2024. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/ | |
dc.subject | SVAR | en_US |
dc.subject | Identification | en_US |
dc.subject | Heteroskedasticity | en_US |
dc.subject | Sign restrictions | en_US |
dc.subject | Proxy variables | en_US |
dc.title | Blended Identification in Structural VARs | en_US |
dc.type | Article | en_US |
dc.identifier.doi | doi.org/10.1016/j.jmoneco.2024.103581 | |
pubs.notes | Not known | en_US |
pubs.publication-status | Accepted | en_US |
pubs.publisher-url | https://jme.rochester.edu/index.html | en_US |
dcterms.dateAccepted | 2024-02-22 | |
rioxxterms.funder | Default funder | en_US |
rioxxterms.identifier.project | Default project | en_US |