External shocks and macroeconomic responses in Nigeria: A global VAR approach

Oyelami, Lukman Oyeyinka; Olomola, P. A. Article External shocks and macroeconomic responses in Nigeria: A global VAR approach Cogent Economics & Finance Provided in Cooperation with: Taylor & Francis Group Suggested Citation: Oyelami, Lukman Oyeyinka; Olomola, P. A. (2016) : External shocks and macroeconomic responses in Nigeria: A global VAR approach, Cogent Economics & Finance, ISSN 2332-2039, Taylor & Francis, Abingdon, Vol. 4, Iss. 1, pp. 1-18, https://doi.org/10.1080/23322039.2016.1239317 This Version is available at: https://hdl.handle.net/10419/194629 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Oyelami & Olomola, Cogent Economics & Finance (2016), 4: 1239317 http://dx.doi.org/10.1080/23322039.2016.1239317 GENERAL & APPLIED ECONOMICS | RESEARCH ARTICLE External shocks and macroeconomic responses in Nigeria: A global VAR approach Lukman Oyeyinka Oyelami 1 * and P.A. Olomola 2 Abstract:This study investigates the macroeconomic responses of Nigerian economy to external shock between 1986 and 2014. Specifically, we examine the effect of oil price shocks and macroeconomic shocks from developed trading partners on Nigerian macroeconomic performances in order to establish pattern of reactions to these shocks in the country. We employ global vector autoregression (GVAR) comprising of the US, EU, China, Japan and Nigeria as the reference country. The adoption as of this method of estimation is necessitated by its capability to effectively model complex high-dimensional system and also offers adequate tools to deal with the curse of dimensionality that can arise from a study of this nature. Having critically examined the econometric properties of our GVAR model, the results from our estimation based on impulse response function show that oil price shocks have direct effect on real gross domestic product and exchange rate in Nigeria but variables like inflation and short-term interest rate do not show immediate response to the shocks. The results also indicate that macroeconomic variables such as short-term interest and inflation show immediate responses to shocks to counterpart variables in developed countries. Based on this, the study concludes that Nigerian economy is vulnerable to external shocks and such shocks are not limited to oil price shocks. Other form of shocks such as growth spillover and financial shocks from developed countries are also relevant in shaping the macroeconomic performances in Nigeria. *Corresponding author: Lukman Oyeyinka Oyelami, Economics Unit, Distance Learning Institute University of Lagos, Akoka Lagos, Lagos, Nigeria E-mail: lo[email protected] Reviewing editor: Mariam Camarero, Universitat Jaume I, Spain Additional information is available at the end of the article ABOUT THE AUTHORS Lukman Oyeyinka Oyelami is a lecturer at Economic Unit, Distance Learning Institute University of Lagos. He just successfully defended his PhD thesis at Obafemi Awolowo University Ile-Ife under the supervision of P.A. Olomola. He currently teaches macroeconomics, statistics, monetary economics and econometrics at undergraduate level. Prof. P.A. Olomola is currently the Head of Economics Department, Obafemi Awolowo University, Ile-Ife. He has authored several articles in reputed international and local journals and also supervised several PhD students. He is a scholar of international repute. PUBLIC INTEREST STATEMENT The whole world has become a global village as result of increasing globalization in recent time and this provides platform for interaction of macroeconomic variables among the countries of the world, especially trading partners. Apart from this cross-country interaction of macroeconomic variables, there exist global variables that influence macroeconomic variables across economies such as price of crude. Changes in all of these variables at one time or the other create shocks which macroeconomics variables in different economies must respond to. Thus, in this study, we examined the responses of Nigerian economy to vagaries of shocks from trading partners as a result of monetary and fiscal policies and crude oil price. The results show that macroeconomic variables in Nigeria does not only respond to oil price shocks but also respond to macroeconomic shocks from critical trading partners and policy-makers in the country must take this into cognizance. Received: 03 July 2016 Accepted: 15 September 2016 Published: 17 October 2016 © 2016 The Author(s). This open access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license. Page 1 of 18 Lukman Oyeyinka Oyelami Page 2 of 18 Oyelami & Olomola, Cogent Economics & Finance (2016), 4: 1239317 http://dx.doi.org/10.1080/23322039.2016.1239317 Subjects: International Finance; Macroeconomics; Monetary Economics Keywords: macroeconomics; GVAR; shock and price 1. Introduction African countries in general are highly dependent on the volatile prices of primary commodities and aid flow Raddatz (2008). Thus, a thorough understanding of macroeconomic fluctuations in African economies requires a good grasp of the impact of external shocks (Kose & Riezman, 2001). The sources of such shocks may include fluctuations in the prices of exported primary commodities, imported capital goods, intermediate inputs and financial shocks, especially the world real interest rate. In Africa, Nigeria is biggest economy though at the global level the country can be considered a small open economy with strong tendency to respond to global macroeconomic shocks. Nigerian is an oil producing country and depends heavily on proceeds from the sales of crude oil to generate foreign earnings to finance her import. Also, the country depends heavily on importation of capital goods and consumable goods from developed and emerging economies of the world to cater for industrial and household needs and as such the economic fortunes of the country is inextricably tied to global economic activities thus making the country vulnerable to external shocks. External shocks on small, open economies can lead to booms and bursts in employment and output, balance of payment crises and exchange rate instability (Gafar, 1996). Based on this, effective management of external shocks can be considered as one of the key issues in macroeconomic management, especially in developing countries. In Nigeria, studies have examined the effect of oil price shocks on macroeconomics variables in the country (Akpan, 2009; Ayadi, 2005; Olomola & Adejumo, 2006). Unfortunately, most of these studies focused on oil price volatility as the only source of external shocks to the Nigeria economy. While it might be difficult to contest the fact that oil price change and its volatility as the most important source of shock to Nigerian economy, it is also difficult to ascribe all macroeconomic fluctuation to oil price shocks. As a result of this, it is important to take into consideration the implication of monetary and fiscal policy shocks of important trading partners, especially developed and emerging economies in any serious discussion of external shocks in Nigeria. Thus, it is pertinent to examine other sources of shocks vis-a-vis oil price shocks within the global interdependent framework and more importantly to determine the relative contribution of external and internal shocks to macroeconomic performances in the country and that is what this study seeks to achieve. To expand the scope of previous studies, the study focused on the effect of global variables (oil price and world price of raw materials index) and foreign variables from critical trading partners (the US, Euro, China and Japan). Apart from this general introduction, the rest of the paper is structured as follow. Section two gives general overview of macroeconomic characteristics and performances in Nigeria, section three presents literature review, while the methodology is presented in section four and section five discusses the results and findings. 2. Overview of macroeconomic characteristics and performances in Nigeria The Nigerian economy has over the year heavily relied on export of crude oil for foreign exchange earnings and revenues. Particularly, sales of crude oil accounts for over 95% of export earnings and about 85% of government revenues. Despite this, the sector only contribution to 17.85% to GDP. According to Energy Information Administration (2009), Nigeria’s effective oil production capacity to be around 2.7 million barrels per day. However, the country has continued to import refined petroleum products since the collapse of local refineries in the late 1980s. According to Nigerian National Petroleum Corporation 2014 report, the country imports almost 85% of refined products for local consumption. Generally, the economy has experienced a persistent growth in output in recent time especially since the advent of democracy. The average growth rate of real output within the reference period stands at 5.33% as against world real output growth of (2.72%). Also, inflation rate in the economy Page 3 of 18 Oyelami & Olomola, Cogent Economics & Finance (2016), 4: 1239317 http://dx.doi.org/10.1080/23322039.2016.1239317 stands at 20.29% as against (4.3) at the global level and (2.0) in the US and (2.1) Euro area which are the most important trading partners before the emergence of China in recent time. The interest rate in the economy is also far above what obtained at global level, but is gradually coming down as shown in Figure 1 though is still above what obtains in other trading economies like China, the US and Euro area and this might altogether questions proper linkage of Nigerian economy to rest of the world. To get a clearer picture, we plotted three macroeconomic variables that can quickly show the level of external dependence of the economy (exchange rate, foreign reserve and current account balance). The three variables are presented in Figure 2 together with oil price. 3. Empirical literature Many studies in the past focused on effect of oil price shocks on macroeconomic performances of either oil exporting countries or oil importing countries, with little attention to other sources of external shocks. To achieve a comprehensive review of literature, we examined studies that focus on oil price shocks and studies that focus on other sources of external shocks, especially growth spillover. One of the consequences of recent global financial crisis is the growing number of studies on transmission of business cycle, especially from developed countries such as the US, European Union, Japan, China, India, to other countries majorly the developing ones. Starting with the work of Bayoumi and Swiston (2009), in their study using vector autoregressions of real growth, they Figure 1. Author’s computation (interest rate). Figure 2. External sector and oil price. Page 4 of 18 Oyelami & Olomola, Cogent Economics & Finance (2016), 4: 1239317 http://dx.doi.org/10.1080/23322039.2016.1239317 estimated growth spillovers between the US, the Euro area, Japan and an aggregate of smaller countries proxying for global shocks. They found out that the US and global shocks generate significant spillovers in developing countries, but those from the Euro area and Japan are small comparing to the US). Similarly, study by Vamvakidis and Arora (2010) examined the growth spillover of China’s economy in recent time employing vector autoregressions approach and they concluded that spillover effects of China’s growth have increased in recent decades and long-term spillover effects are also significant and have extended in recent decades beyond Asia and this has serious implication for a developing country like Nigeria that have serious trade relations with china. Array of similar studies by Samake and Yang (2011), Ding and Masha (2012) and Poirson and Weber (2011) came to similar conclusion on growth spillover. Furthermore, a lot of studies have been carried out on the macroeconomic effect of oil price shocks in oil-producing countries, Nigeria inclusive. Starting with the one of the earlier studies by Hamilton (1983) who argued that several post-war recessions in the US were preceded by oil price shocks other studies by (Ayadi, 2005; Brown & Yücel, 2002; Burbidge & Harrison, 1984; Darby, 1982; Khan & Hampton, 1990) corroborate this assertion in the US or other countries but the major bone of contention is the channels of transmission of the oil price shocks. Considering the study by Abel and Bernanke (2001), they argue that increases in oil prices cause the general price level to rise. Thus, they consider the price as transmission mechanism through which oil prices influences the macroeconomic situations in a typical oil consuming country. Other theories that focus on the production function corroborate this assertion. In another study by Finn (2000), he asserts that an oil price shock create a sharp and simultaneous decreases in energy consumption and capital utilization. Thus, the resulting decline in energy consumption permeates through the production function, directly resulting to a decrease in output and labour’s marginal product. Consequently, the fall in labour’s marginal product brings about reduction in wages, which, in turn, leads to a reduction in the labour supplied. In addition, there exist array of studies in Nigeria with divergent views on the roles of oil shocks in Nigerian economy. While Ayadi (2005) concludes that oil price shocks does not have significant effect on industrial production, Akpan (2009) submits that there exists a marginal impact of oil price fluctuation on industrial output growth in the country. Olomola and Adejumo (2006) conclude that oil price shocks (positive) do not have a direct effect on output and inflation except via real exchange rate and money supply. Apart from this little controversy in the literature of oil price shock in Nigeria, it also important to investigate how other external shocks affect Nigerian economy, especially the growth spillover from other advanced economies of the world. 4. Research methodology Generally, in the literature of VAR, three main approaches have been commonly developed for modelling data-sets with a large number of variables which this type of study requires. They are augmented VARs, Bayesian VARs and the global VARs. Out of the three approaches, global VARs has proven to be handier and intuitively appealing (Pesaran and Chudik, 2014). For proper theoretical underlying of GVAR, Pesaran, Schuermann, and Weiner (2004), Dees, di Mauro, Pesaran and Smith (2007) and Pesaran and Chudik (2014) provide a detailed guide. The global VAR (GVAR) approach, developed in Pesaran et al. (2004) was employed to investigate the dynamic interaction of external shocks and macroeconomic performances in Nigeria. The adoption of this methodology is based on the fact that it provides a relatively simple and effective way of modelling complex high-dimensional system and also offers adequate tools to deal with the curse of dimensionality that can arise from a study of this nature. It also provides opportunity to explore different sources of shocks to an economy and this makes the method suitable for a study of this nature with primary goal of exploring different sources of shocks to Nigerian economy. Page 5 of 18 Oyelami & Olomola, Cogent Economics & Finance (2016), 4: 1239317 http://dx.doi.org/10.1080/23322039.2016.1239317 According to Pesaran et al. (2004), the global VAR model can be developed in two stages. The first stage starts with country-specific VARX* models, which is just VAR models augmented by weakly I (1) variables such as domestic variables and cross-section averages of foreign variables. This is further estimated for each country/region separately. In the second stage, the estimated coefficients from the country/region-specific models are stacked and solved in one big system this referred to as global VAR (Table 1). Given a country-specific model which is a VARX* model for each individual country/region, in each country VARX* model, country-specific domestic variables are related to deterministic variables, such as time trend, country-specific foreign variables and global variables. Introducing common endogenous variables in country-specific model of the GVAR: where GDPit = nominal gross domestic product of country i during period t (in local currency), CPIit = consumer price index for country i at time t (with the base year at 100), eit = exchange rate of country i currency at time t in US dollars, i = nominal short-term interest rate per annum, in per cent, rit = foreign exchange reserves, pW t = World price of raw materials, po t = World oil price. In our GVAR model, the US is indexed as country 0 and the exchange rate of the US—E0t—is taken to be 1. In the country-specific model for each country, the endogenous variables are; ( q it ,p it ,i it e it f it r it) and ( q ∗ it p ∗ it i ∗ it e ∗ it f ∗ it r ∗ it , ) represents foreign variables and it contains variables from important trading partners majorly G4 (the US, Euro Zone, Japan and China). In addition to the foreign variables, the GVAR model contained some global variables, namely oil price ( po t ) and world commodity price index ( pW t) . The GVAR model thus allowed for interactions among the different economies through three separate but interrelated channels: the contemporaneous dependence Xit on X∗ it and X∗ it−m ; dependence of the country-specific variables on common global exogenous qit =ln (GDPit∕CPIit) pit = ln ( CPIit )− ln ( CPIi,t−1) eit =ln (Eit∕CPIit) iit =ln (iit) rit =ln (rit) PW t =ln (P W t) Po t=ln (Po t) Table 1. Description of variables Source: Author’s computation. Proxy Measurement Data source q it RGDP it Nominal gross domestic product of country i during period tWDI p it CPI it Consumer price index for country i at time t (with the base year at 100) WDI e it EXH it Exchange rate of country i currency at time t in US dollars IFS i it INT it Nominal short-term interest rate per annum, in per cent IFS PW t WCPI it World commodity price index WTO Po t OILP it World oil price IMF r it RSV it Foreign exchange reserves expressed in terms of at least 6 months of imports IMF Page 6 of 18 Oyelami & Olomola, Cogent Economics & Finance (2016), 4: 1239317 http://dx.doi.org/10.1080/23322039.2016.1239317 variables, d =(p W t and p W t) and nonzero contemporaneous dependence of shocks in country i on the shocks in country j, measured via the cross-country covariances ∑ ij . The (weak) exogenous variables in the country-specific VARX* models trade weighted foreign core macrovariables (denoted by an “*”). In most country-specific models, foreign variables are constructed as follows: The weights wij for i, j = 0, 1, …, N are trade weights between country i and country j which will be computed using the simple average of monthly total trade of a country during the 1990–2013 period. wii is 0 for any country i. Also, for robustness check a time-varying weight will also computed and employed to determine the sensitivity of our model to changing trade relationship between foreign economies and Nigerian economy. Furthermore, It is assumed that variables are I (1), the countryspecific exogenous variables are weakly exogenous, and that the parameters of the country-specific models remain stable over time. Also, the order of the individual country VARX*(pi, qi) models, where pi denotes the lag order of endogenous variables (or domestic variables) and qi denotes the lag order of exogenous variables (or foreign variables) selected. The values of a VARX*(1, 1).Then, for all countries the country-specific VARX*(1, 1) models can be written as follows: where t is the linear time trend. In line with Pesaran et al. (2004), the country-specific VARX* models was estimated individually with the restriction that both the foreign and global variables are weakly exogenous I(1) variables. Assuming the weak exogeneity of the foreign variables implies that each country, with the exception of the US, is considered as a small open economy. The global variables, d =(p o t and p W t) , were thus treated as endogenous in the US model. The exogeneity assumptions hold in practice depends on the relative sizes of the countries/regions in the global model and on the degree of cross-country dependence of the idiosyncratic shocks, ɛit as captured by the cross-covariances ∑ ij (Pesaran et al., 2004). The weak exogeneity in the context of co-integrating models implies no long run feedback from Xit to X∗ it without ruling out lagged short run feedback between the two sets of variables (Dées, Karadeloglou, Kaufmann, & Sánchez, 2007). Consider the V ARX* model for country-specific model without the global variables and group both the domestic and foreign variables as Z it = ( X� it,X∗� it ) . Such country-specific model could be written as follows: where A i = ( I ki ,𝛬 i0) , Bi = ( 𝛷 i ,𝛬 i1) and Bi are Ki ×( Ki + K∗ i) , and Ai has a full row rankrank (Ai)=Ki . Collecting all the country-specific variables together in the K × 1 global vector where k = N ∑ i=0 K i the total number of the endogenous variables, the country-specific variables can be written as follows: (1) y ∗ it = N ∫ j=0 wijyjt,𝜋∗ it = N ∫ j=0 wij𝜋jt,e∗ it = N ∫ j=0 Wijejt , (2) q ∗ it = N ∫ j=0 wijqjt,r∗ it = N ∫ j=0 wijr jt (3) Xit = 𝛿 i0+ 𝛿 i1t+Φ iXi,t−1+Λ i0X∗ it +Λ i1X∗ i,t−1+Γ i0dt+Γ i1dt−1+ 𝜀 it (4) AiZit =aio +ai1t+BiZi,t−1+𝜀it (5) Xit = ( X� 0t ,X� 1t ,…X� Nt)�, (6) Zit =Wixt,i=0, 1, 2, …,N Page 7 of 18 Oyelami & Olomola, Cogent Economics & Finance (2016), 4: 1239317 http://dx.doi.org/10.1080/23322039.2016.1239317 where Wi is a ( k i +k ∗ i) × k country-specific link matrix constructed on the basis of trade weights that allows the country-specific models to be written in terms of the global variable vector. Substituting Equation (4) in (5), we have Stacking all the equations yields where a 0= ⎛ ⎜ ⎜ ⎜ ⎜ ⎝ a00 a10 … aN0 ⎞ ⎟ ⎟ ⎟ ⎟ ⎠ ,a1= ⎛ ⎜ ⎜ ⎜ ⎜ ⎝ a01 a11 … aN1 ⎞ ⎟ ⎟ ⎟ ⎟ ⎠ ,𝜀t= ⎛ ⎜ ⎜ ⎜ ⎜ ⎝ 𝜀0t 𝜀1t … 𝜀Nt ⎞ ⎟ ⎟ ⎟ ⎟ ⎠ and where G is a k × k—dimension matrix with full rank and hence non-singular. As a result, G matrix could be inverted to obtain the GVAR model in its reduced form as follows: The GVAR model in Equation (9) can be solved recursively and the dynamic properties of the model will be analysed using generalized impulse response functions (GIRFs). 5. Definition and measurement of variables In order to examine the dynamic interaction between external shocks and macroeconomic performances of Nigeria, the study utilized quarterly data over the period of 1990–2014. In all, six endogenous variables were employed for Nigeria. The variables are nominal gross domestic product of each country, consumer price index, exchange rate of country i currency at time t in US dollars, foreign direct investment as percentage of GDP, nominal short-term interest rate per annum, the real equity price and foreign exchange reserves. Similarly, seven foreign variables were employed in the model as weak exogenous variables. The variables were nominal gross domestic product of each country, consumer price index, exchange rate of country i currency at time t in US dollars, nominal short-term interest rate per annum in per cent, the real equity price to GDP and foreign exchange reserves expressed in terms of at least 6 months of imports. Also, two global variables were included in the model, which are oil price and world commodity price index. In the US model, exchange rate was excluded and the global variables are treated as endogenous variables thus making the variables in the model nine. Apart from foreign reserve and foreign direct investment introduced as endogenous variables in Nigerian model, all other macroeconomic and financial variables have been widely employed in similar studies outside Zone Pesaran, Shin, and Smith (2000), Pesaran et al. (2004), Pesaran, Schuermann, and Smith (2009), Han and Ng (2011) and Gurara and Ncube (2013). (7) AiWixt = aio + ai1t + BiWi,xt−1 +𝜀 it (8) G x t=a 0 +a 1 t+Hx t−1 + 𝜀 it G = ⎛ ⎜ ⎜ ⎜ ⎜ ⎝ A0W0 A1W1 … ANWN ⎞ ⎟ ⎟ ⎟ ⎟ ⎠ ,H= ⎛ ⎜ ⎜ ⎜ ⎜ ⎝ B0W0 B1W1 … BNWN ⎞ ⎟ ⎟ ⎟ ⎟ ⎠ (9) X t=G −1 a 0 +G −1 a 1 t+G −1 Hxt−1+G −1 𝜀 t Page 8 of 18 Oyelami & Olomola, Cogent Economics & Finance (2016), 4: 1239317 http://dx.doi.org/10.1080/23322039.2016.1239317 6. Econometrics properties of the data 6.1. Unit root tests Weighted symmetric augmented Dickey–Fuller (WS-ADF) unit root test performed on all the domestic variables. The results indicate that the variables in the models are I (1) and this suggests that while the hypothesis of non-stationarity is rejected at level but accepted at first difference. Thus, it is assumed that all our variables are suitable for estimation of GVAR. The results is included in Appendix 1 Similarly, WS-ADF unit root test performed on all the foreign variables and the results also indicate that the variables are I (1). Also, WS-ADF unit root test was employed to test the stationarity of global variables and the results also indicate that the variables in the models are I (1). The results are presented in Appendices 2 and 3. 6.2. Choice criteria for selecting the order of the VARX* For lag order of domestic variables (p) and foreign variables (q), AIC and SBC were used to select lag order for each country-specific VARX* models. Maximum lag orders of two are allowed for both (p) and (q). The test results indicate VARX*(2, 1) for all countries-specific VARX* models. The results also contains choice criteria for selecting the order of the VARX* models together with corresponding residual serial correlation F-Statistics. The results are presented in Appendices 4 and 5, respectively. 6.3. Cointegration results By default, the GVAR program will create the worksheet coint_max&traceVARX. It contains both the trace and maximum eigenvalue statistics used for determining the dimension of the cointegration space of the individual models, as well as the critical values for the trace statistic. Tests are usually conducted using the trace statistic at the 5% level of significance. The critical values for models including weakly exogenous variables are obtained from Mackinnon, Haug and Michelis (1999). In this model, estimate of VARX* was performed for each country in the GVAR system, based on the number of cointergrating vector imposed according to the result of the trace statistics with the lag selected by AIC. The results from cointegrating tests indicate five cointegrating relations for Nigeria and Japan, and four for USA. It indicates three cointegrating relations for China and Euro. The detail of the results are presented in Appendix 6. 7. Model estimation In line with the objective of the study which is to investigate the macroeconomic responses of Nigerian economy to external shocks, we analysed the time profile of the effects of a one standard shocks to foreign variables on Nigerian macroeconomic variables. We investigated the implications of two different external shocks: one standard error negative shocks to real output in the US, China, Euro and Japan and one standard error positive shocks to global oil price. To achieve our objective, we employed the GIRF proposed in Koop, Pesaran, and Potter (1996), developed further in Pesaran and Shin (1998) for vector error-correcting models. This is because there is no strong a priori information to identify the short run dynamics of our system couple with the fact that large restrictions it would require for proper identification. Also, this prevents the model from being sensitive to ordering of variables and country which is very important in big macroeconomics model. In panel two of Figure 3, consider the effect of one standard negative shocks to the US real output which is equivalent to a fall of around 0.3–0.4% in real output at the point of impact in the US. The transmission of the shock takes effect in Nigeria decreasing real output in the country by 0.1% at the beginning and average of 0.6% over the period of two years and it is statistically significant for the country. Also, in panel three, one standard negative shocks to Euro real output which amount to a fall of around 0.06% on the average in real output in Euro area over the period of two years. The shock is transmitted to Nigerian economy by decreasing real output by 0.24% immediately and average of 0.36% over the rest of the period. The results also show that the effect of the shock is Page 15 of 18 Oyelami & Olomola, Cogent Economics & Finance (2016), 4: 1239317 http://dx.doi.org/10.1080/23322039.2016.1239317 Appendix 3. Unit root tests for the global variables at the 5% significance level Global variables Test Critical value Statistic poil (with trend) ADF −3.45 −2.65371 poil (with trend) WS −3.24 −1.51367 poil (no trend) ADF −2.89 −0.83716 poil (no trend) WS −2.55 −1.24184 Dpoil ADF −2.89 −4.77851 Dpoil WS −2.55 −5.01184 DDpoil ADF −2.89 −8.49038 Dpoil WS −2.55 −8.67356 pmat (with trend) ADF −3.45 −2.48389 pmat (with trend) WS −3.24 −2.68366 pmat (no trend) ADF −2.89 −1.86129 pmat (no trend) WS −2.55 −2.0775 Dpmat ADF −2.89 −7.26762 Dpmat WS −2.55 −7.44684 DDpmat ADF −2.89 −10.0536 Dpmat WS −2.55 −10.2226 Appendix 4. VARX* order of individual models p q China 2 1 Euro 2 1 Japan 2 1 Nigeria 2 1 USA 2 1 Source: p: lag order of domestic variables, q: lag order of foreign variables. Appendix 5. Country China Euro Japan Nigeria USA Detailed cointegration results for the maximum eigenvalue statistic at the 5% significance level # endogenous variables 6 7 7 7 6 # foreign (star) variables 8 8 8 8 7 r = 0 78.9202 122.1013 103.9452 121.0383 79.06888 r = 1 66.60998 74.22136 80.66769 86.32578 64.42501 r = 2 57.48288 67.20414 58.37289 63.10249 61.12445 r = 3 35.04075 50.76612 50.46779 54.35957 41.0228 r = 4 27.99926 38.36555 45.25905 48.97911 30.78174 r = 5 24.58472 26.53633 35.64912 30.51452 21.05921 r = 6 15.84632 24.96751 21.54006 Page 16 of 18 Oyelami & Olomola, Cogent Economics & Finance (2016), 4: 1239317 http://dx.doi.org/10.1080/23322039.2016.1239317 Country China Euro Japan Nigeria USA Detailed cointegration results for the trace statistic at the 5% significance level # endogenous variables 6 7 7 7 6 # foreign (star) variables 8 8 8 8 7 r = 0 290.6378 395.0411 399.3292 425.8598 297.4821 r = 1 211.7176 272.9398 295.384 304.8215 218.4132 r = 2 145.1076 198.7185 214.7163 218.4957 153.9882 r = 3 87.62474 131.5143 156.3435 155.3933 92.86375 r = 4 52.58399 80.7482 105.8757 101.0337 51.84095 r = 5 24.58472 42.38265 60.61662 52.05458 21.05921 r = 6 15.84632 24.96751 21.54006 Critical values for trace statistic at the 5% significance level (MacKinnon, Haug, & Michelis, 1999) # endogenous variables 6 7 7 7 6 # foreign (star) variables 8 8 8 8 7 r = 0 223.88 273.21 273.21 273.21 210.8 r = 1 178.46 223.88 223.88 223.88 167.47 r = 2 136.94 178.46 178.46 178.46 128 r = 3 99.12 136.94 136.94 136.94 92.29 r = 4 64.91 99.12 99.12 99.12 60.22 r = 5 33.87 64.91 64.91 64.91 31.35 r = 6 33.87 33.87 33.87 Page 17 of 18 Oyelami & Olomola, Cogent Economics & Finance (2016), 4: 1239317 http://dx.doi.org/10.1080/23322039.2016.1239317 Appendix 6. Choice criteria for selecting the order of the VARX* models together with corresponding residual serial correlation F-statistics p q AIC SBC logLik Fcrit_0.05 q p e i f R China 1 1 1684.746 1498.629 1828.746 F(4,70) 2.502656 8.189651 1.564907 2.462695 9.183939 6.135149 China 2 1 1688.599 1455.952 1868.599 F(4,64) 2.515318 6.773907 2.511887 4.804452 12.69236 8.063808 Euro 1 1 2108.48 1882.296 2283.48 F(4,69) 2.504609 3.694212 2.770983 2.055817 4.710312 4.519165 1.885145 Euro 2 1 2149.579 1860.063 2373.579 F(4,62) 2.520101 1.264306 3.200754 2.885382 1.420919 6.268444 2.963375 Japan 1 1 2079 1852.816 2254 F(4,69) 2.504609 2.326133 2.105789 3.623013 10.14303 3.891195 3.83714 Japan 2 1 2120.381 1830.864 2344.381 F(4,62) 2.520101 3.014501 1.563412 8.332579 4.695485 2.778412 1.176284 Nigeria 1 1 1364.221 1138.037 1539.221 F(4,69) 2.504609 12.05326 7.611088 4.156059 1.982354 6.000239 3.085369 Nigeria 2 1 1417.941 1128.425 1641.941 F(4,62) 2.520101 12.8129 3.498573 4.47379 3.134451 8.969576 1.06387 USA 1 1 1853.366 1682.758 1985.366 F(4,72) 2.498919 0.557548 4.84741 3.685563 4.627759 14.39122 2.771737 USA 2 1 1866.493 1649.356 2034.493 F(4,66) 2.510833 0.836394 3.087703 4.148998 0.853242 17.78231 1.341592 Page 18 of 18 Oyelami & Olomola, Cogent Economics & Finance (2016), 4: 1239317 http://dx.doi.org/10.1080/23322039.2016.1239317 © 2016 The Author(s). 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