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Article . 2023 . Peer-reviewed
Data sources: Crossref
SSRN Electronic Journal
Article . 2022 . Peer-reviewed
Data sources: Crossref
SSRN Electronic Journal
Article . 2022 . Peer-reviewed
Data sources: Crossref
https://dx.doi.org/10.34989/sw...
Other literature type . 2022
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Research . 2022
Data sources: EconStor
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Research . 2022
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Sectoral Uncertainty

Authors: Castelnuovo, Efrem; TUZCUOGLU, KEREM; Uzeda Garcia, Luis Henrique;

Sectoral Uncertainty

Abstract

Nous proposons un nouveau cadre empirique qui permet de décomposer simultanément la variance conditionnelle des séries chronologiques de données économiques en deux facteurs : l’incertitude agrégée et l’incertitude sectorielle. Nous appliquons notre cadre à un vaste ensemble de données désagrégées relatives à la production industrielle aux États-Unis. Nos résultats indiquent qu’avant la pandémie, l’incertitude agrégée et l’incertitude liée aux biens non durables ont toutes deux atteint leur sommet durant la récession de 1973-1975. L’incertitude liée aux biens durables a quant à elle culminé pendant la crise financière mondiale de 2008-2009. Des exercices d’autorégression vectorielle permettent d’établir que les variations imprévues de l’incertitude liée aux biens durables sont des facteurs de ralentissement économiquement et statistiquement significatifs, tandis que les hausses inattendues de l’incertitude liée aux biens non durables ont une action expansionniste. Nos résultats donnent à penser que 1) l’incertitude est hétérogène au niveau sectoriel, et 2) l’incertitude liée aux biens durables peut être à l’origine de certains effets du cycle économique qui sont habituellement attribués à l’incertitude agrégée.

We propose a new empirical framework that jointly decomposes the conditional variance of economic time series into a common and a sector-specific uncertainty component. We apply our framework to a large dataset of disaggregated industrial production series for the US economy. Our results indicate that common uncertainty and uncertainty linked to nondurable goods both recorded their pre-pandemic global peaks during the 1973-75 recession. In contrast, durable goods uncertainty recorded its pre-pandemic peak during the global financial crisis of 2008-09. Vector autoregression exercises identify unexpected changes in durable goods uncertainty as drivers of downturns that are both economically and statistically significant, while unexpected hikes in non-durable goods uncertainty are expansionary. Our findings suggest that: (i) uncertainty is heterogeneous at a sectoral level; and (ii) durable goods uncertainty may drive some business cycle effects typically attributed to aggregate uncertainty.

Keywords

Monetarypolicy and uncertainty, ddc:330, sectors, C51, Econometric and statistical methods, E44, stochastic volatility, uncertainty, C55, Business fluctuations and cycles, dynamic factor, E32

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
2
Average
Average
Average
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