Modelling sectoral spillovers in the USA (1992–2015)

Abstract

PurposeThis research paper uses a novel methodological approach to investigate the spillover effects among the key sectors of the US economy.Design/methodology/approachThe paper links the US sectors via a node theoretic scheme based on a general equilibrium framework, whereas it estimates the general equilibrium equation as a Global Vector Autoregressive process, taking into consideration the potential existence of dominant units.FindingsBased on our findings, the dominant sector in the US economy, for the period 1992–2015, is the sector of information technology, finance and communications, a fact that gives credence to the view that the US economy is a service-driven economy. In addition, the US economy seems to benefit by the increased labour mobility across knowledge-intensive sectors, thus avoiding the ‘employment trap’ which in turn enabled the US economy to overcome the financial crisis of 2007.Originality/valueFirstly, the paper models by means of a network approach which is based on a general equilibrium framework, the linkages between the US sectors while treating the sector of information, technology, communications and finance as dominant, as dictated by its degree of centrality in the network structure. Secondly, the paper offers a robustness analysis regarding both the existence and the identification of dominant sectors (nodes) in the US economy. Thirdly, the paper studies a wide period, namely 1992–2015, fully capturing the recent global recession, while acknowledging the impact of the global crisis through the introduction of the relevant exogenous dummy variables; Lastly and most importantly, it is the first study to apply the GVAR approach in a network general equilibrium framework at the sectoral level.

Market Integration and Price Dynamics under Market Shocks in European Union Internal and External Cheese Export Markets

“…The global vector autoregressive (GVAR) model is employed in this paper. The GVAR model has been widely applied in macroeconomics, including (see Chudik and Pesaran [ 13 ] for comprehensive surveys) studying the factors and shocks affecting global inflation [ 24 ], global imbalance [ 25 , 26 , 27 , 28 ], effects of fiscal and monetary policy [ 29 , 30 , 31 , 32 , 33 , 34 , 35 ], credit supply shock [ 36 , 37 , 38 ], spill-overs in the labor market [ 39 ], financial market [ 40 ], energy market [ 41 ], trade [ 42 ], and different sectors [ 43 , 44 , 45 ], etc. So far, the GVAR model has been applied to agricultural markets in a few empirical studies, including analyzing linkages among food commodity prices, energy prices, and financial sectors in the major wheat export countries by Gutierrez et al [ 46 ], studying short-run food price shock propagation in Sub-Saharan Africa (SSA) by Pierre and Kaminski [ 47 ], and analyzing spatial price transmission of the global butter export market under different market shocks such as exchange rate fluctuation, shocks to fertilizer prices, palm oil price, and crude oil price by Xue et al [ 48 ].…”

Spatial Price Transmission and Price Dynamics of Global Butter Export Market under Economic Shocks

“…The GVAR model has been applied extensively in macroeconomics, including but not limited to (see Chudik and Pesaran [13] for comprehensive surveys) studying the factors and shocks affecting global inflation [28], global imbalance [29][30][31][32], effects of fiscal and monetary policy [33][34][35][36][37][38][39], credit supply shock [40][41][42], spill overs in the labour market [43], financial market [44], energy market [45], trade [46], and different sectors [47][48][49], etc. So far, the GVAR model has been applied to agricultural markets twice.…”

Crisis and the Chinese miracle: A network—GVAR model

“…Pesaran et al (2004), were the first to propose the use of the GVAR approach in order to model the interactions in a complex highdimensional system, such as the global economy. In addition, the GVAR model not only gives us the opportunity to embed the complex interdependencies among the various economies through the "weight matrix" but also it takes into account both the direct and the indirect linkages between the economic entities (Konstantakis et al, 2020;Tsionas et al, 2016).…”