The Global Impact of the Systemic Economies and MENA Business Cycles*
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This paper analyzes spillovers from macroeconomic shocks in systemic economies (China, the Euro Area, and the United States) to the Middle East and North Africa (MENA) region as well as outward spillovers from a GDP shock in the Gulf Cooperation Council (GCC) countries and MENA oil exporters to the rest of the world. This analysis is based on a Global Vector Autoregression (GVAR) model, estimated for 38 countries/regions over the period 1979Q2 to 2011Q2. Spillovers are transmitted across economies via trade, financial, and commodity price linkages. The results show that the MENA countries are more sensitive to developments in China than to shocks in the Euro Area or the United States, in line with the direction of evolving trade patterns and the emergence of China as a key driver of the global economy. Outward spillovers from the GCC region and MENA oil exporters are likely to be stronger in their immediate geographical proximity, but also have global implications.


This paper analyzes spillovers from macroeconomic shocks in systemic economies (China, the Euro Area, and the United States) to the Middle East and North Africa (MENA) region as well as outward spillovers from a GDP shock in the Gulf Cooperation Council (GCC) countries and MENA oil exporters to the rest of the world. This analysis is based on a Global Vector Autoregression (GVAR) model, estimated for 38 countries/regions over the period 1979Q2 to 2011Q2. Spillovers are transmitted across economies via trade, financial, and commodity price linkages. The results show that the MENA countries are more sensitive to developments in China than to shocks in the Euro Area or the United States, in line with the direction of evolving trade patterns and the emergence of China as a key driver of the global economy. Outward spillovers from the GCC region and MENA oil exporters are likely to be stronger in their immediate geographical proximity, but also have global implications.

I. Introduction

The GVAR literature almost exclusively focuses on business cycle linkages among advanced and major emerging market economies, with limited attention to growth spillovers to/from the Middle East and North Africa (MENA) countries, in particular the Gulf Cooperation Council (GCC) region. While the international business cycle is very important for the MENA regions’s economic performance, macroeconomic and political developments in this region also have large consequences for the rest of the world, due to the abundance of natural resources in the Middle East and North Africa. We use a GVAR model to disentangle the size and speed of the transmission of different macroeconomic shocks originating from three systemic countries to the Maghreb (Algeria, Libya, Mauritania, Morocco, and Tunisia), Mashreq (Egypt, Jordan, and Syria), and GCC (Bahrain, Kuwait, Oman, Qatar, Saudi Arabia, and the UAE) regions, as well as outward spillovers from the MENA region to the rest of the world. We also focus on the emergence of China as a global force in the world economy, and study how changes in trade patterns between China and the rest of the world may have affected the transmission of the international business cycle to MENA countries and other systemic economies.

Our approach uses a dynamic multi-country framework for the analysis of the international transmission of shocks and is based on the model developed in Cashin et al. (2012). The framework comprises 38 country/region-specific models, among which is a single Euro Area region (comprising 8 of the 11 countries that joined the Euro on January 1, 1999) as well as the GCC region. These individual models are solved in a global setting where core macroeconomic variables of each economy are related to corresponding foreign variables (constructed exclusively to match the international trade pattern of the country under consideration). The model has both real and financial variables: real GDP, inflation, real equity price, real effective exchange rate, short and long-term interest rates, a measure of global oil production, and the price of oil. This framework is able to account for various transmission channels, including not only trade relationships but also financial and commodity price linkages—see Dees et al. (2007) for more details. Compared to Dees et al. (2007), the current paper advances the work on GVAR modelling in the following directions: (i) we extend the geographical coverage of the GVAR model to the Middle East and North Africa region as well as to other major oil-exporters; (ii) we add a measure of global oil production to the GCC model to account for supply side factors in the world oil market, as many supply shortfalls originate in the MENA region (for instance, the more recent Arab Spring and associated supply shortfalls from Libya, or the effects of sanctions on Iran and the resulting drop in its oil exports); and (iii) we investigate the growing impact of China’s macroeconomic shocks on other systemic economies, the MENA region in general, and major oil exporters in particular.

We estimate the GVAR model based on two sets of fixed trade weights at different points in time, being 20 years apart. Specifically, we make use of a set of weights averaged over 1986 and 1988 and another between 2006 to 2008. This allows us to study how the transmission of shocks has changed following the emergence of China as a major driver of the world economy. Our results, using quarterly data between 1979Q2 to 2011Q2, indicate that the impact of a Chinese GDP shock on a typical MENA economy, as well as on other systemic countries and oil exporters, has increased significantly since the mid-1980s. A negative GDP shock in China (using the 2006–08 weights) would have major global repercussions, especially for less-diversified commodity exporters. The effects on other systemic countries are smaller but not trivial. At the same time, the impact of a U.S. GDP shock on a typical MENA economy is large, and has not changed significantly since the mid-1980s. We also find that outward spillovers from the GCC and MENA oil exporters are likely to be stronger in their immediate geographical proximity, but they also have implications for systemic economies and other major oil exporters.

The rest of the paper is organized as follows. Section II describes the GVAR methodology while Section III outlines our modelling approach and presents the country-specific estimates and tests. Section IV focuses on the potential macroeconomic consequences of a GDP shock in systemic countries. Section V investigates the extent to which the macroeconomic conditions in the GCC region and MENA oil exporters affect, and are affected by, the global economy. Finally, Section VI concludes.

II. The Global Var (GVAR) Methodology

We consider N + 1 countries in the global economy, indexed by i = 0, 1, …, N. With the exception of the United States, which we label as 0 and take to be the reference country, all other N countries are modelled as small open economies. This set of individual VARX* models is used to build the GVAR framework. Following Pesaran (2004) and Dees et al. (2007), a VARX* (si, si*) model for the ith country relates a ki x 1 vector of domestic macroeconomic variables (treated as endogenous), Xit, to a ki*x1 vector of country-specific foreign variables (taken to be weakly exogenous), xit*, and to a md x 1 vector of observed global factors, dt, which could include such variables as commodity prices:


for t = 1, 2,…, T, where ai0 and ai1 are ki x 1 vectors of fixed intercepts and coefficients on the deterministic time trends, respectively, and uit is a ki x 1 vector of country-specific shocks, which we assume are serially uncorrelated with zero mean and a non-singular covariance matrix, Σii, namely uiti.i.d. (0, Σii). Furthermore, Φi(L,si)=I-Σi=1siΦiLi,Λi(L,si*)=Σi=0si*ΛiLi, and ϒi(L,si*)=i=0si*ϒiLi are the matrix lag polynomial of the coefficients associated with the domestic, foreign, and global variables, respectively. As the lag orders for these variables, Si and si*, are selected on a country-by-country basis, we are explicitly allowing for Φi(L,si),Λi(L,si*), and ϒi(L,si*) to differ across countries.

The country-specific foreign variables are constructed as cross-sectional averages of the domestic variables using data on bilateral trade as the weights, Wij :


where j = 0, 1, …N, wii = 0, and Σj=0Nwij=1. For empirical application, the trade weights are computed as fixed weights based on the average trade flows measured over the period 2006 to 2008. However, the weights can be based on any time period and can be allowed to be time-varying.

Although estimation is done on a country-by-country basis, the GVAR model is solved for the world as a whole, taking account of the fact that all variables are endogenous to the system as a whole. After estimating each country VARX*(si, si*) model separately, all the k=Σi=0Nki endogenous variables, collected in the k x 1 vector xt=(x0t,x1t,,xNt), need to be solved simultaneously using the link matrix defined in terms of the country-specific weights. To see this, we can write the VARX* model in equation (1) more compactly as:


for i = 0, 1, …, N, where


Note that given equation (2) we can write:


where Wi = (Wi0, Wi1, …, WiN) with Wii = 0 is the (ki+ki*)xk weight matrix for country i defined by the country-specific weights, wij. Using (5) we can write equation (3) as:


where Ai (L, s) is constructed from Ai(L,si,si*) by setting s=max(s0,s1,,sN,s0*,s1*,,sN*) and augmenting the s - si or s - si* additional terms in the power of the lag operator by zeros. Stacking (6), we obtain the Global VAR(s) model in domestic variables only:




For an illustration of the solution of the GVAR model, using a VARX*(1, 1) model, see Pesaran (2004), and for a detailed exposition of the GVAR methodology see Dees et al. (2007). The GVAR(s) model in equation (7) can be solved recursively and used for a number of purposes, such as forecasting or impulse response analysis.

III. A Global Var Model Including The Mena Region

We extend the country coverage of the GVAR dataset used in Dees et al. (2007) by adding 14 countries located in the Middle East and North Africa region as well as three other Organization of the Petroleum Exporting Countries (OPEC) members (see Table 1). Thus, our version of the GVAR model includes 50 countries, covering over 90% of world GDP as opposed to the “standard” 33 country set-up used in the literature, see Smith and Galesi (2010). Of the 50 countries included in our sample, 17 are oil exporters, of which 10 are current OPEC members and one is a former member (Indonesia left OPEC in January 2009). We were not able to include Angola and Iraq, the remaining two OPEC members, due to the lack of sufficiently long time series data. We therefore, extend the country coverage both in terms of major oil exporters and also by including an important region of the world when it comes to oil supply, the MENA region.

Table 1.

Countries and Regions in the GVAR Model Including MENA

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Notes:* indicates that the country has been added to the Smith and Galesi (2010) database. Countries in italics are included in a region for estimation purposes.

For empirical application, we create two regions, one of which comprises the six Gulf Cooperation Council (GCC) countries: Bahrain, Kuwait, Oman, Qatar, Saudi Arabia, and the United Arab Emirates (UAE); and the other is the Euro Area block comprising 8 of the 11 countries that initially joined the Euro in 1999: Austria, Belgium, Finland, France, Germany, Italy, Netherlands, and Spain. The time series data for the GCC block and the Euro Area block are constructed as cross-sectionally weighted averages of the domestic variables (described in detail below), using Purchasing Power Parity GDP weights, averaged over the 2006–2008 period. Thus, as displayed in Table 1, the GVAR model that we specify includes 38 country/region-specific VARX* models.

A. Variables

The macroeconomic variables included in the individual VARX* models depend on both the modelling strategy employed as well as whether data on a particular variable is available. Each country-specific model has a maximum of six domestic (endogenous) variables and five foreign (exogenous) variables. We also include two global variables, each of which is treated endogenously in only one country, while being weakly exogenous in the remaining 37 country models. Below, we describe the different variables included in our model and provide justification for our modelling specification. For various data sources used to build the quarterly GVAR dataset, covering 1979Q2 to 2011Q2, see the Data Appendix.

Domestic Variables

Real GDP, yit, the rate of inflation, πit, short-term interest rate, rit,s long-term interest rate, rit,L and real equity prices, eqit are the five domestic variables that are included in our model, as well as most of the GVAR applications in the literature. These five variables are constructed as:


where GDPit is the real Gross Domestic Product at time t for country i, CPIit is the consumer price index, EQit is a nominal Equity Price Index, and RitS(RitL) is the short-term (long-term) interest rate.

The GVAR literature also typically includes a sixth domestic variable, representing the real exchange rate and defined as eit - pit, that is the log of the nominal exchange rate of country i, ln (Eit), deflated by the domestic CPI. However, in a multi-country set-up, it might be better to consider a measure of the real effective exchange rate, rather than eit - pit. We therefore follow Dees et al. (2007) and construct such a variable, reerit.

To construct the real effective exchange rate for country i, we simply take the nominal effective exchange rate, neerit, add the log of foreign price level (pit*) and subtract the domestic (pit) price level. Note that neerit is a weighted average of the bilateral exchange rates between country i and all of its trading partners j, where j = 0, …, N. In the current application, we have a total of 36 countries and two regions in our model, N = 37; therefore, we can use the nominal exchange rates denominated in United States dollars for each country, eit, to calculate reerit. More specifically,


where the foreign price is calculated as the weighted sum of log price level indices (pjt) of country i’s trading partners, pit*=Σj=037wijpjt, and wij is the trade share of country j for country i. Given that Σj=037wij=1andeit*=Σj=037wijejt, the real effective exchange rate can be written as:


This constructed measure of the real effective exchange rate is then included in our model as the sixth domestic variable.

Foreign Variables

We include five foreign variables in our model. In particular, all domestic variables, except for that of the real effective exchange rate, have corresponding foreign variables. The exclusion of reerit* is simply because reerit already includes both domestic, eit - pit, and foreign, eit*-pit*, nominal exchanges rates deflated by the appropriate price levels, see equation (11). Therefore, reerit* does not by itself have any economic meaning. The foreign variables are all computed as in equation (2), or more specifically:


The trade weights, wij, are computed as a three-year average to reduce the impact of individual yearly movements on the weights:1


where Tijt is the bilateral trade of country i with country j during a given year t and is calculated as the average of exports and imports of country i with j, and Tit=Σj=0NTijt (the total trade of country i) for t = 2006, 2007, 2008, in the case of all countries. The trade shares used to construct the foreign variables are given in the 38 x 38 matrix provided in Table 7 of the Data Appendix.

Global Variables

Given the importance of oil price and production for the MENA region, we also include nominal oil prices (in United States dollars), Ptoil, as well as the quantity of oil produced in the world, Qtoil, in our model. As is now standard in the literature, we include log oil prices, Ptoil, as a “global variable” determined in the U.S. VARX* model; that is the price of oil is included in the U.S. model as an endogenous variable while it is treated as weakly exogenous in the model for all other countries. The main reason for this is that the U.S. is the world’s largest oil consumer. On average, about 27% of the world oil between 1979–2010 was consumed by the U.S., which is far larger than compared to the other three major oil importers in the world (China, Euro area, and Japan), even when combined.

On the other hand, the GCC countries produce more than 22% of world oil and export around 30% of the world total. They also possess 36% of the world’s proven oil reserves, and Saudi Arabia, by itself, has the largest spare capacity in the world. Thus, we include log of oil production, qtoil, as an endogenous variable in the GCC block, and as a weakly exogenous variable in all other countries. qtoil, is therefore the second “global variable” in our model.2

Making one region out of Bahrain, Kuwait, Oman, Qatar, Saudi Arabia, and the United Arab Emirates, is not without economic reasoning. The rationale is that the GCC countries have in recent decades implemented a number of policies and initiatives to foster economic and financial integration with a view to establishing a monetary union based on the Euro Area model. Given the increased integration of these economies over the last three decades, the peg to a common currency (the United States dollar), flexible labor markets, and open capital accounts, it is therefore reasonable to group these countries as one region.3

B. Mena Trade Weights

The Middle East and North African countries are globally less competitive relative to their peers. The Middle East accounts for less than 1% of world non-fuel exports, compared with 4% from Latin America, and of its limited global export share, inter-regional trade accounts for less than a tenth, barely more than in 1960. The usual explanation for the poor trade performance in the region is its reliance on crude oil exports, and hence little success in developing significant merchandise exports. Furthermore, since most countries in the region export the same products –oil and gas– they naturally do not tend to actively trade with each other (see Table 2). More trade would enable firms to reap greater economies of scale, increase returns to investment, adopt superior technology, and hence, it would promote growth.4

Table 2.

MENA Trade Weights

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Notes: Trade weights are computed as shares of exports and imports, displayed in columns by region (such that a column, but not a row, sum to 1). Source: Direction of Trade Statistics, IMF.

Looking specifically at Table 2b, we note that the Euro Area is the most important trading partner for the Maghreb countries (Algeria, Libya, Mauritania, Morocco, and Tunisia). More than 48% of their trade originates in or is destined for the Euro Area. U.S. and China are also large trading partners for the Maghreb, with the weights ranging between 3–22% and 2–25% with the U.S. and China, respectively. However, Maghreb’s trade with the GCC is generally limited to less than 1% of total trade in all countries except for Morocco (for which it is 6%).

On the other hand, the Mashreq countries (Egypt, Jordan, and Syria) trade much more with the GCC, where the shares are between 13–28%.5 The Euro Area is nevertheless very important for the region as between 16–32% of Mashreq trade is destined for or originates in the eight Euro Area countries in our sample. Europe is also an important trading partner for Turkey (45%), as compared to China, the GCC, UK, and the U.S., where the individual trade weights are just above 6%. Iran’s largest trading partner is the Euro area (24%), but it also trades substantially with China, the GCC countries, and Japan (all exceeding 12%).

Comparing with other countries in the MENA region, the GCC’s trade is less concentrated on one country/region, trading more than 10% with China, Euro Area, Japan and the U.S. individually. However, as mentioned before, this is mainly due to oil exports to different regions rather than having a more diversified export basket/market.

Comparing the more recent trade weights, averaged over 2006–2008, with those from 20 years ago in Table 2a, we see that for the MENA region as a whole trade with the Euro Area has fallen, but trade with China has increased many fold for all countries. On the other hand, trade with the U.S. has increased for some but decreased for others, while trade between the region and GCC has remained more or less stable, except for Egypt, Iran, Jordan, and Syria for which it has increased between 8 (Iran) and 20 (Syria) percentage points, respectively.

Overall Table 2 illustrates the continuing importance of the Euro Area countries in our sample for the MENA region, but also shows that both China and the U.S. are important for the region. We will therefore focus on spillovers from these three systemic economies to MENA countries in Section IV. Moreover, given the emergence of China in the world economy and its increasing importance for the MENA region, and for the largest oil exporters in the world in particular, we shall also illustrate how China’s impact on the region and other systemic economies has changed over the past two decades.

Table 2 also illustrates that trade between the GCC and other MENA countries is large for a few economies but small for others. This is also the case for the overall trade between the GCC and the rest of the world: the GCC trade weights for China, Euro Area and the U.S. are between 3–4%, although trade with India is more than 20% and the trade shares with Japan and Korea are more than 12%, see Table 7. However, given the importance of the Persian Gulf in determining oil supply (and eventually oil prices), we expect the GCC’s performance to have a global impact through the commodity channel rather than purely via the trade one. Thus, we look at spillovers from the GCC to the rest of the world in Section V.

C. Model Specification

Given the discussion above, we specify three different sets of individual country-specific models. The first specification is common across all countries apart from the United States and the GCC block. These 36 VARX* models include six endogenous/domestic variables, when available, five country-specific foreign variables, and two global variables (see Table 3). Using the same terminology as in equation (1), the 6 x 1 vector of endogenous and the 5 x 1 vector of exogenous variables are given by xit=[yit,πit,eqit,rits,ritL,reerit] and xit*=[yit*,πit*,eqit*,rit*s,rit*L] respectively, while the 2 x 1 vector of global variables is defined as dt=[ptoil,qtoil].

Table 3.

Variables Specification of the Country-Specific VARX* Model

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Notes: See equations (9) and (11) for the definition of the variables.

The second specification relates to the GCC block only, for which the log of oil production, qtoil, is included in the model endogenously in addition to the 3 domestic variables in xit, while xit* and the log of nominal oil prices, Ptoil, are included as weakly exogenous variables.

Finally, the U.S. model is specified differently from the others, mainly because of the dominance of the United States in the world economy. Firstly, based on the discussion above regarding oil consumption, the price of oil is included in the model endogenously. Secondly, given the importance of U.S. financial variables in the global economy, the U.S.-specific foreign financial variables, eqUS,t*,andrUS,t*L, are not included in this model. The exclusion of these two variables was also confirmed by our preliminary analysis, in which the weak exogeneity assumption was rejected for eqUS,t*,andrUS,t*L, in the U.S. model. Finally, since eit is expressed as domestic currency price of a United States dollar, eUS,t - pUS,t, it is by construction determined outside this model. Thus, instead of the real effective exchange rate, we included eUS,t*-pUS,t* as a weakly exogenous foreign variable in the U.S. model.

D. Country-Specific Estimates and Tests

Initial estimations and tests of the individual VARX* (si, si*,) models are conducted under the assumption that the country-specific foreign and global variables are weakly exogenous and integrated of order one, I (1), and that the parameters of the models are stable over time. As both assumptions are needed for the construction and the implementation of the GVAR model, we will test and provide evidence for these assumptions below.

For the interpretation of the long-run relations, and also to ensure that we do not work with a mixture of I(1) and I(2) variables, we need to consider the unit root properties of the core variables in our country-specific models (see Table 3). If the domestic, xit, foreign, xit* and global, dt, variables included in the country-specific models are indeed integrated of order one, I(1), we are not only able to distinguish between short- and long-run relations but also to interpret the long-run relations as cointegrating. Therefore, we perform Augmented Dickey-Fuller (ADF) tests on the level and first differences of all the variables. However, as the power of unit root tests are often low, we also utilize the weighted symmetric ADF test (ADF-WS) of Park and Fuller (1995), as it has been shown to have better power properties than the ADF test. This analysis results in over 3200 unit root tests, which overall, as a first-order approximation, support the treatment of the variables in our model as being I(1). For brevity, these test results are not reported here but are available from the authors upon request.

Lag Order Selection, Cointegrating Relations, and Persistence Profiles

We use quarterly observations over the period 1979Q2–2011Q2, across the different specifications in Table 3, to estimate the 38 country/region-specific VARX*(si, si*,) models. However, prior to estimation, we need to determine the lag orders of the domestic and foreign variables, si and si*. For this purpose, we use the Akaike Information Criterion (AIC) applied to the underlying unrestricted VARX* models. However, given the constraints imposed by data limitations, we set the maximum lag orders to smax = 2 and smax* = 1. The selected VARX* orders are reported in Table 4, from which we can see that for most countries a VARX*(2,1) specification seems satisfactory, except for seven countries (Australia, Egypt, Iran, Malaysia, Mexico, Singapore, and the United Kingdom), for which s = s* = 1 is selected by AIC.

Table 4.

Lag Orders of the Country-Specific VARX*(s,s*) Models Together with the Number of Cointegrating Relations (r)

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Notes: si and si*, denote the lag order for the domestic and foreign variables respectively and are selected by the Akaike Information Criterion (AIC). The number of cointegrating relations (ri) are selected using the trace test statistics based on the 95% critical values from MacKinnon (1991) for all countries except for Australia, Euro Area, Indonesia, Iran, Japan, Malaysia, South Africa, Singapore, Switzerland, Thailand, Tunisia, and the United States, for which we use the 95% simulated critical values computed by stochastic simulations and 1000 replications, and for Canada, China, Korea, Peru, Philippines, the UK, for which we reduced ri below that suggested by the trace statistic to ensure the stability of the global model.

Having established the order of the 38 VARX* models, we proceed to determine the number of long-run relations. Cointegration tests with the null hypothesis of no cointegration, one cointegrating relation, and so on are carried out using Johansen’s maximal eigenvalue and trace statistics as developed in Pesaran et al. (2000) for models with weakly exogenous I(1) regressors, unrestricted intercepts and restricted trend coefficients. We choose the number of cointegrating relations (ri) based on the trace test statistics, given that it has better small sample properties than the maximal eigenvalue test, initially using the 95% critical values from MacKinnon (1991).6

We then consider the effects of system-wide shocks on the exactly identified cointegrating vectors using persistence profiles developed by Lee and Pesaran (1993) and Pesaran and Shin (1996). On impact the persistence profiles (PPs) are normalized to take the value of unity, but the rate at which they tend to zero provides information on the speed with which equilibrium correction takes place in response to shocks. The PPs could initially over-shoot, thus exceeding unity, but must eventually tend to zero if the vector under consideration is indeed cointegrated. In our preliminary analysis of the PPs, we noticed that the speed of convergence was very slow for some countries and for a few the system-wide shocks never really died out. In particular, the speed of adjustment was very slow for the following 18 countries (with ri based on critical values from MacKinnon (1991) in brackets): Australia (4), Canada (4), China (2), Euro Area (2), Indonesia (3), Iran (2), Japan (3), Korea (4), Malaysia (2), Peru (3), Philippines (2), South Africa (2), Singapore (3), Switzerland (3), Thailand (3), Tunisia (2), the United Kingdom (2), and the United States (3).

Moreover, we noticed that a couple of eigenvalues of the GVAR model were larger than unity. Therefore, to ensure the stability of the global model, as well as to deal with the possible overestimation of the number of cointegrating relations based on asymptotic critical values, we estimated a cointegrating VARX* model, based on the lag orders in Table 4, for each of the 18 countries separately and used the trace test statistics together with the 95% simulated critical values, computed by stochastic simulations using 127 observations from 1979Q4 to 2011Q2 and 1000 replications, to determine the number of cointegrating vectors.7

We then re-estimated the global model reducing the number of cointegrating relations (for the 18 countries only) one by one and re-examined the PPs after each estimation to ensure stability of the model. The final selection of the number of cointegrating relations are reported in Table 4. For 12 of the 18 countries we selected ri based on the trace statistic and the simulated critical values. For four countries (China, Peru, Philippines, and the UK) the asymptotic and simulated critical values were the same so we reduced ri until the PPs for each country were well behaved; this was also done for Canada and Korea.

The persistence profiles for the set of 23 focus countries, eleven MENA countries, five systemic countries and seven other oil exporters in our model (see Table 1), together with their 95% bootstrapped error bands are provided in Figure 1. The profiles overshoot for only 5 out of the 36 cointegrating vectors before quickly tending to zero. The speed of convergence is very fast, the half-life of the shocks are generally less than 3 quarters, and equilibrium is established before 6 years in all cases except for Egypt, Jordan and Libya. Amongst the 23 countries, Iran shows the fastest rate of convergence (around 3 years),8 and Libya the slowest rate of convergence (8-9 years). The 95% error bands are quite tight and initially widen somewhat before narrowing to zero. The speed of convergence, although relatively fast, is in line with that observed for major oil exporters in Esfahani et al. (2012a).

Figure 1.
Figure 1.

Persistence Profiles of the Effect of a System-wide Shock to the Cointegrating Relations

Citation: IMF Working Papers 2012, 255; 10.5089/9781475581645.001.A001

Notes: Figures are median effects of a system-wide shock to the cointegrating relations with 95% bootstrapped confidence bounds.

Testing the Weak Exogeneity Assumption

Weak exogeneity of the country-specific foreign variables, xit*=[yit*,πit*,eqit*,rit*S,rit*L], and the global variables, ptoil, and qtoil, with respect to the long-run parameters of the conditional model is vital in the construction and the implementation of the GVAR model. We formally test this assumption following the procedure in Johansen (1992) and Harbo et al. (1998). To this end, we first estimate the 38 VARX*(si, si*,) models separately under the assumption that the foreign and global variables are weakly exogenous. We then run the following regression for each lth element of xit*:


where ECMi,t-1j, j = 1, 2, …, ri are the estimated error correction terms corresponding to the ri cointegrating relations found for the ith country model, ni = 2 (although it could be set equal to si*), and Δx˜it*=[Δx˜it*,=Δreerit,*Δptoil,Δqtoil]..9 Under the null hypothesis that the variables are weakly exogenous, the error correction term must not be significant; therefore, the formal test for weak exogeneity is an F-test of the joint hypothesis that ϒij,l = 0 for each j = 1, 2, …, ri in equation (14). The test results together with the 95% critical values are reported in Table 5, from which we see that the weak exogeneity assumption cannot be rejected for the overwhelming majority of the variables considered. In fact, only 7 out of 263 exogeneity tests turned out to be statistically significant at the 5% level.

Table 5.

F-Statistics for Testing the Weak Exogeneity of the Country-Specific Foreign Variables, Oil Prices, and Oil Production

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Notes: *denotes statistical significance at the 5% level.

More specifically, in terms of the variables in xit*, only foreign output in the Indonesian model and foreign short-term interest rates in the model for Argentina, Japan, and Nigeria cannot be considered as weakly exogenous. This assumption is also rejected for the price of oil in the Canadian model, and oil production in the Euro Area and Iranian models. However, considering the significance level assumed here, even if the weak exogeneity assumption is always valid, we would expect up to 14 rejections, 5% of the 263 tests. Overall, the available evidence in Table 5, therefore, supports our treatment of the foreign and global variables in the individual VARX* models as weakly exogenous.

Testing for Structural Breaks

Although the possibility of structural breaks is a fundamental problem in macroeconomic modelling in general, this is more likely to be a concern for a particular set of countries in our sample (i.e., emerging economies and non-OECD oil exporters) which have experienced both social and political changes since 1979. However, given that the individual VARX* models are specified conditional on the foreign variables in xit* they are more robust to the possibility of structural breaks in comparison to reduced-form VARs, as the GVAR setup can readily accommodate co-breaking. See Dees et al. (2007) for a detailed discussion.

We test the null of parameter stability using the residuals from the individual reduced-form error correction equations of the country-specific VARX*(si, si*,) models, initially looking at the maximal OLS cumulative sum statistic (PKsup) and its mean square variant (PKmsq) of Ploberger and Krämer (1992). We also test for parameter constancy over time against non-stationary alternatives as proposed by Nyblom (1989) (NY), and consider sequential Wald statistics for a single break at an unknown change point. More specifically, the mean Wald statistic of Hansen (1992) (MW), the Wald form of the Quandt (1960) likelihood ratio statistic (QLR), and the Andrews and Ploberger (1994) Wald statistics based on the exponential average (APW). Finally, we also examine the heteroscedasticity-robust versions of NY, MW, QLR, and APW.

Table 6 presents the number of rejections of the null hypothesis of parameter constancy per variable across the country-specific models at the 5% significance level. For brevity, test statistics and bootstrapped critical values are not reported here, but are available on request. Overall, it seems that most regression coefficients are stable; however, the results vary considerably across different tests. In the case of the two PK tests, the null hypothesis is rejected between 3.4–7.8% of the time. For the NY, MW, QLR and APW tests on the other hand, we note that the rejection rate is much larger, between 17.9–52.5%. The QLR and APW rejection rates, for the joint null hypothesis of coefficient and error variance stability, are particularly high with 94 and 89 cases, respectively, out of 179 being rejected. However, looking at the robust version of these tests, we note that the rejection rate falls considerably to between 10.1% and 18.4%. Therefore, although we find some evidence for structural instability, it seems that possible changes in error variances rather than parameter coefficients is the main reason for this. We deal with this issue by using bootstrapped means and confidence bounds when undertaking the impulse response analysis discussed later.

Table 6.

Number of Rejections of the Null of Parameter Constancy per Variable Across the Country-Specific Models at the 5 Percent Significance Level

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Notes: The test statistics PKsup and PKmsq are based on the cumulative sums of OLS residuals, NY is the Nyblom test for time-varying parameters and QLR, MW and APW are the sequential Wald statistics for a single break at an unknown change point. Statistics with the prefix ‘robust’ denote the heteroskedasticity-robust version of the tests. All tests are implemented at the 5% significance level. The number in brackets are the percentage rejection rates.

IV. Inward Spillovers

This section studies whether the increasing economic integration at the world level and the resulting emergence of large economic players, such as China, have weakened the role of the U.S. economy or the Euro Area as drivers of global growth. To do so, we look at the effects of negative U.S., Euro Area, and Chinese real output shocks on the MENA region, other oil exporters, and systemic economies.10

A. Shock to U.S. GDP

As a result of the dominance of the United States in the world economy, any slowdown in this country can bring about negative spillovers to other economies. As the recent global economic crisis has shown, the history of past U.S. recessions usually coincides with significant reductions in global growth. Furthermore, the continuing dominance of U.S. debt and equity markets, backed by the still-strong global role of the U.S. dollar, is also playing an important role. The results of our GVAR model, presented in Figure 2b, show first that countries with a substantial trade exposure to the U.S. economy have a relatively large sensitivity to U.S. developments. Specifically, in response to a one percent decline in U.S. GDP, Canadian (70%), Mexican (69%), and Nigerian (36%) real outputs fall by 0.37, 0.56, and 0.66 percent respectively, with this effect being statistically significant (the numbers in brackets are corresponding trade weights which are reported in Table 7).

Table 7.

Trade Weights, Averages over 2006–2008

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Notes: Trade weights are computed as shares of exports and imports, displayed in columns by region (such that a column, but not a row, sum to 1). Source: Direction of Trade Statistics, 2006–2008, IMF.