Measuring US sectoral shocks in the world input–output network

Ando, Sakai

doi:10.1016/j.econlet.2014.09.007

Cited by 29 publications

(11 citation statements)

References 5 publications

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“…Without further information, it is not possible to identify all N 2 potential contemporaneous linkages amongst the N structural shocks on the N sectors. 3 To circumvent the identification restrictions of standard SVAR models, the approach is to make use of the structural multi-sectoral growth model presented in FSW, in which both output production and capital goods in a given sector are produced using other sectors' output (see Appendix An important feature of the FSW structural model is that as it admits a VARMA representation of the form given in (8), in which Π 0 and Π 1 are simply functions of ϑ and thus can be identified using calibrated values of ϑ. It is also possible to quantify the Φ matrix within the FSW framework, but the approach here is to leave Φ unrestricted and allow for a potentially broader range of additional potential linkages that are not only production based.…”

Section: Hybrid Model Identificationmentioning

confidence: 99%

Real Sectoral Spillovers: A Dynamic Factor Analysis of the Great Recession

Li¹,

Martin²

2018

IMF Working Papers

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This paper studies changes in the transmission of common versus sectoral idiosyncratic shocks across different U.S. nonfarm business sectors during the Great Recession, and evaluates the cross-sectoral spillovers. Shocks are identified by dynamic factor methods. We find that the Great Recession is largely a time of heightened impact of common shockswhich accounts for 3/4 of aggregate volatility-and large spillovers of negative financerelated shocks. Moreover, in contrast with the earlier literature that failed to find a significant role of sectoral shocks (propagated through the input-output linkages across sectors) in driving variability in aggregate industry output, this study allows spillovers of shocks to operate through other mechanisms intertemporally. We find that prior to the recession the majority of aggregate fluctuations is explained by sector-specific shocks.

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Section: Hybrid Model Identificationmentioning

confidence: 99%

Real Sectoral Spillovers: A Dynamic Factor Analysis of the Great Recession

Li¹,

Martin²

2018

IMF Working Papers

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“…In this section, we present a static model of an input-output neo-classical economy, which is a canonical version introduced by [ 13 ] and applied by [ 1 , 7 , 23 ].…”

Section: Methodsmentioning

confidence: 99%

Sectoral networks and macroeconomic tail risks in an emerging economy

2018

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This paper aims to explain the macroeconomic volatility due to microeconomic shocks to one or several sectors, recognizing the non-symmetrical relation in the interaction among the Ecuadorian economic sectors. To grasp the economic structure of this emerging economy, a statistical analysis of network data is applied to the respective input-output matrix of Ecuador from 1975 until 2012. We find periods wherein the production of domestic inputs is concentrated in a few suppliers; for example, in 2010, the concentration significantly affects sectors and their downstream providers, thus influencing aggregate volatility. Compared to the US productive structure, this emerging economy presents fewer sectors and degree distributions with less extreme fat-tail behavior. In this simpler economy, we continue to find a link between microeconomic shocks and aggregate volatility. Two new theoretical propositions are introduced to formalize our results.

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“…With the development of IO database, related researches are based not only on independent national systems but also on multi-regional even global systems, with wide adoption of WIOD as the data source. For instance, Ando measured the importance of industrial sectors under the impact of American gross output in the global IO model [ 5 ]. Antràs, et al derived two distinct approaches to measure industry upstreamness and prove their significant impact on trade flows [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

Measurement and simulation of the relatively competitive advantages and weaknesses between economies based on bipartite graph theory

Guan

et al. 2018

PLoS ONE

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The input-output table is very comprehensive and detailed in describing the national economic systems with abundant economic relationships, which contain supply and demand information among various industrial sectors. The complex network, a theory, and method for measuring the structure of a complex system can depict the structural characteristics of the internal structure of the researched object by measuring the structural indicators of the social and economic systems, revealing the complex relationships between the inner hierarchies and the external economic functions. In this paper, functions of industrial sectors on the global value chain are to be distinguished with bipartite graph theory, and inter-sector competitive relationships are to be extracted through resource allocation process. Furthermore, quantitative analysis indices will be proposed under the perspective of a complex network, which will be used to bring about simulations on the variation tendencies of economies’ status in different situations of commercial intercourses. Finally, a new econophysics analytical framework of international trade is to be established.

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Measuring US sectoral shocks in the world input–output network

Cited by 29 publications

References 5 publications

Real Sectoral Spillovers: A Dynamic Factor Analysis of the Great Recession

Real Sectoral Spillovers: A Dynamic Factor Analysis of the Great Recession

Sectoral networks and macroeconomic tail risks in an emerging economy

Measurement and simulation of the relatively competitive advantages and weaknesses between economies based on bipartite graph theory

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