System estimation of GVAR with two dominants and network theory: Evidence for BRICs

doi:10.1016/j.econmod.2015.08.033

Economic Modelling

Volume 51, December 2015, Pages 604-616

https://doi.org/10.1016/j.econmod.2015.08.033 Get rights and content

Highlights

•
We propose system estimation for GVAR with k-dominants.
•
We select the dominant entities using netweok theory.
•
We find that US and EU17 are both dominant economies.
•
We show that omission of EU17 as dominant could bias the results.
•
We conclude that EU17 is more vulnerable than the US to a BRIC slowdown.

Abstract

The dynamics of traditional economic structures changed dramatically in the US and globally after 2006. In this context, the need for modeling complex macroeconomic interactions, has led us to develop an upgraded compact global (macro) econometric GVAR model, which is capable of incorporating both the complex interdependencies that exist between the various economic entities and the fact that in the global economy more than one of these entities could have a predominant role, without neglecting the channels of trade and finance. Additionally, based on the trade weight matrix that lies in the core of the GVAR framework, we provide both an analytical procedure and an ex-post econometric criterion for the selection of dominant entities. We demonstrate the dynamics of our model by focusing on the impact of a potential slowdown in the BRICs on the US and EU17 economies. According to our findings, the dominant economies are those of the USA and EU17, while the results suggest that EU17 is more vulnerable than the USA to shocks from the BRICs, implying that a potential slowdown in the BRICs will primarily affect the EU17 economy. Clearly, the proposed model can be easily used for analyzing a number of transmission mechanisms, contagion effects and network interdependencies in various settings.

Introduction

Over the last years, we are in the middle of a devastating global crisis that has significantly affected the economic conditions of the two major economic regions of the world, US and EU17. According to the World Economic Outlook (2013), the IMF cut its global GDP forecast to 3.1% from 3.3%, since growth in advanced economies was trimmed from 1.3% to 1.2%, due to both the EU17 and the US weakness, while emerging markets' growth was cut by 0.3% to 5%. In this context, the so-called BRICs account for about 20% of world GDP and 55% of the output of emerging and developing economies (World Economic Outlook, 2013). Nevertheless, the impact of a potential slowdown of BRICs on other major economies (e.g. US, EU) has attracted limited attention in the literature, so far.

In this paper we attempt to shed light on the impact of BRICs¹ on the two major economic regions of EU17 and US. Of course, when attempting to model the complex interdependencies between the emerging economies of BRICs and the major economies of US and EU17 one should neglect neither the predominant role of US and EU17 in the global economy, nor the fundamental channels of trade and finance that are hailed to be the most important channels of transmission (e.g. Cetorelli and Goldberg, 2011).

In this context, the GVAR approach, introduced by Pesaran et al. (2004), would be a relevant tool for the analysis of such complex dynamics. In the GVAR framework, it has been argued that the US could be considered as being a dominant economy in the model (Chudik and Pesaran, 2013). Nevertheless, the use of the US economy as the only dominant unit in the GVAR model is an ad-hoc approach that is, thus far, justified solely based on economic intuition, as opposed to formal econometric methods. To this end, there are two predominant research questions on the topic of dominant units in a GVAR framework: (a) is the USA indeed dominant according to formal methods? (b) Is there any other dominant economy in the model?

To this end, in this paper we construct an upgraded compact (macro)econometric model that incorporates both the complex interdependencies that exist between the various economic entities and the fact that in the global economy more than one of these entities could have a predominant role. In this context, we modify the GVAR model featuring one dominant economy introduced by Chudik and Pesaran (2013) so as to be able to accommodate more than one dominant entity. Additionally, based on the trade weight matrix that lies in the core of the GVAR framework, we provide both an analytical procedure and an ex-post econometric criterion for the selection of the dominant entities.

The present paper contributes to the literature as follows: (a) it proposes system estimation for the GVAR with K dominants; (b) it formally estimates a GVAR with two (2) dominant economies; (c) it sets out a formal method for indentifying the number of dominant entities in a GVAR framework; (d) it sets out a novel method based on network theory for selecting the dominant entities; (e) it compares the estimation results of GVAR using one dominant and two dominant economies, respectively; (e) it estimates how a slowdown in the BRICs will affect EU17 and USA.

The remainder of the paper is structured as follows: Section 2 sets out the proposed methodology; Section 3 presents the empirical results; Section 4 provides a brief discussion of the main results; and Section 5 concludes.

Section snippets

Methodology

The Global VAR approach (GVAR) provides a flexible technique for assessing relationships between economic variables and constitutes a useful tool for analyzing the transmission of economic shocks between economic regions. While factor augmented vector autoregressions (FAVAR) could be viewed as an alternative approach to GVAR (see e.g. Bernanke et al., 2005, Lagana and Mountford, 2005), the number of estimated factors used in FAVAR would be different for the different countries and it is not

Data and variables

The data are quarterly and cover the period 1992(Q1)–2014(Q4), fully capturing the ongoing recession. For all the economies that enter the SGVAR model i.e. USA, EU17, Brazil, Russia, India, China, Japan, Australia and Canada we used data⁴ regarding their exchange rates to the dollar, GDP deflator, GDP in current prices and interest rates.⁵

Analysis and discussion

We will begin our analysis by the persistent profiles of the country specific shocks. Each persistent profile shows the time profiles of the effects of the variable-specific shocks on the potential cointegrating relations in the SGVAR model. In general, all persistent profiles presented in Fig. 1, Fig. 2, Fig. 3, Fig. 4 are as expected, since, as the time horizon grows, the value of each persistent profile tends to zero. In fact, all persistent profiles die out in less than ten (10) quarters,

Conclusion

The point of departure of our investigation for constructing this model has been the need for an upgraded compact (macro)econometric tool that could incorporate both the complex interdependencies that exist between the various economic entities and the fact that in the global economy more than one of these entities could have a predominant role. In this context, we have extended the GVAR model of Chudik and Pesaran (2013), featuring one dominant economy, in order to incorporate more than one

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^☆: We are indebted to the Editor, Sushanta Mallick, and the two anonymous referees for their constructive comments that have helped us improve the paper significantly. The usual disclaimer applies.

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System estimation of GVAR with two dominants and network theory: Evidence for BRICs☆

Highlights

Abstract

Introduction

Section snippets

Methodology

Data and variables

Analysis and discussion

Conclusion

J. Int. Money Financ.

Econ. Model.

Econ. Model.

Soc. Networks

Econ. Model.

J. Financ. Stab.

J. Int. Money Financ.

Eur. Econ. Rev.

J. Econ.

Econ. Model.

Eur. Econ. Rev.

Phys. A

Soc. Networks

J. Econ. Dyn. Control.

J. Econ.

J. Int. Econ.

Measuring monetary policy: a factor augmented vector autoregressive (FAVAR) approach

Q. J. Econ.

Factoring and weighting approaches to status scores and clique identification

J. Math. Sociol.

Power and centrality: a family of measures

Am. J. Sociol.

How many variables should be entered in a regression equation?

J. Am. Stat. Assoc.

The second eigenvalue of the Leontief matrix

Econ. Syst. Res.

Modelling global trade flows: results from a GVAR model

Global banks and international shock transmission: evidence from the crisis

IMF Econ. Rev.

Econometric analysis of high dimensional VARs featuring a dominant unit

Econ. Rev.

The GVAR approach and the dominance of the U.S. economy

Size, openness, and macroeconomic interdependence

World Factbook

Exploring the international linkages of the Euro area: a global VAR analysis