dc.contributor.author | Carriero, A | |
dc.contributor.author | Volpicella, A | |
dc.date.accessioned | 2024-02-08T11:29:30Z | |
dc.date.available | 2024-02-02 | |
dc.date.available | 2024-02-08T11:29:30Z | |
dc.identifier.citation | Andrea Carriero & Alessio Volpicella (2024) Max Share Identification of Multiple Shocks: An Application to Uncertainty and Financial Conditions, Journal of Business & Economic Statistics, DOI: 10.1080/07350015.2024.2316829 | |
dc.identifier.issn | 1537-2707 | |
dc.identifier.uri | https://qmro.qmul.ac.uk/xmlui/handle/123456789/94514 | |
dc.description.abstract | We generalize the Max Share approach to allow for simultaneous identification of a multiplicity of shocks in a Structural Vector Autoregression. Our machinery therefore overcomes the well-known drawbacks that individually identified shocks (i) tend to be correlated to each other or (ii) can be separated under orthogonalizations with weak economic ground. We show that identification corresponds to solving a non-trivial optimization problem. We provide conditions for non-emptiness of solutions and point-identification, and Bayesian algorithms for estimation and inference. We use the approach to study the effects of uncertainty and financial shocks, allowing for the possibility that the former responds contemporaneously to other shocks, distinguishing macroeconomic from financial uncertainty and credit supply shocks. Using US data we find that financial uncertainty mimics a demand shock, while the interpretation of macro uncertainty is more mixed. Furthermore, variation in uncertainty partially represents the endogenous response of uncertainty to other shocks. | |
dc.publisher | Taylor and Francis Group | en_US |
dc.relation.ispartof | Journal of Business & Economic Statistics | |
dc.rights | This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way. | |
dc.subject | causality | en_US |
dc.subject | Forecast Error Variance | en_US |
dc.subject | Identification | en_US |
dc.subject | Structural Vector Autoregression | en_US |
dc.subject | Uncertainty | en_US |
dc.title | Max Share Identification of Multiple Shocks: An Application to Uncertainty and Financial Conditions | en_US |
dc.type | Article | en_US |
dc.rights.holder | © 2024 The Author(s). Published with license by Taylor and Francis Group, LLC | |
dc.identifier.doi | 10.1080/07350015.2024.2316829 | |
pubs.notes | Not known | en_US |
pubs.publication-status | Accepted | en_US |
dcterms.dateAccepted | 2024-02-02 | |
rioxxterms.funder | Default funder | en_US |
rioxxterms.identifier.project | Default project | en_US |