




































Economy 
ISSN: 2313-8181 
Vol. 1, No. 2, 37-53, 2014 
www.asianonlinejournals.com/index.php/Economy 

 

 

 

 

 

37 

 

The BRICS and Nigeria’s Economic Performance: A Trade 

Intensity Analysis  
 

Maxwell Ekor
1
 --- Oluwatosin Adeniyi

2
 --- Jimoh Saka

3
 

 
1
Preston Consults Limited, Abuja, Nigeria 

2
Department of Economics, University of Ibadan, Nigeria 

3
Department of Economics, Lagos State University, Nigeria 

 

Abstract 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 
This work is licensed under a Creative Commons Attribution 3.0 License 

Asian Online Journal Publishing Group 

 

1. Introduction 
Nigeria is touted as one of the countries with potentials to become one of the top economies in the world and this 

view is shared by proponents of the BRICS (Brazil, Russia, India, China and South Africa). Nigeria is even now 

grouped among the new emerging powers, the MINT (Mexico, Indonesia, Nigeria and Turkey) countries. The robust 

performance of the Nigerian economy as well as the goal of the Government to propel the economy to become one of 

the top 20 in the world by the year 2020 is also boosting the profile of the economy. To this end, the relationship 

between Nigeria and the BRICS has been of interest to stakeholders. For example, Alao (2011) provided an insight 

into the relationship between Nigeria and the BRICs (excluding South Africa) from a diplomatic, trade, cultural and 

military relations perspectives.  Also, the relationship between Nigeria and South Africa is considered strategic for 

the whole of Africa given the latter’s involvement in the BRICS.     

Studies have dwelt on relationships among the BRICS, for example, Naresh and Alina (2011). However, one of 

the arguments against the BRICS arrangement is that rather than adopt a multilateral strategy, the individual 

countries are pursuing bilateral approach with different countries, including Nigeria. To this end, it is opined that 

there is an implicit struggle by the individual BRICS to penetrate the Nigerian economy. Also, Nigeria is believed to 

be strategic in identifying those markets, including the BRICS, where its bilateral interests are better served. 

Therefore, providing evidence on the trading relationship between Nigeria and the BRICS will shed light on the 

relevance of the BRICS economies to Nigeria.  

Following from the above, the broad objective of this study is to discuss the extent of trade intensity between 

Nigeria and the individual BRICS. Specifically, the study examines how shocks to Nigeria’s economy affect its 

exports to and imports from the BRICS. The rest of the study is organized as follows: Section 2 provides an 

overview of the Nigerian economy while section 3 presents the methodology for estimating the trade intensity and 

shocks. Section 4 presents the data and results while section 5 gives the policy implications of the results.   

 

2. Overview of the Nigerian Economy 
Following the rebasing of the GDP in April 2014, Nigeria is now the largest economy in Sub-Saharan Africa 

(SSA) and 26
th

 in the world with an estimated nominal GDP of $509 billion as shown in Figure 1. Since 1999 when 

The study examined Nigeria’s trading relationship with the individual BRICS (Brazil, 

Russia, India, China and South Africa) by applying a combination of descriptive and 

econometric techniques. The findings show that Nigeria’s trade intensity is highest with 

Brazil followed by trade with India and then South Africa. The outcome of the vector 

autoregressive analysis indicated that Nigeria’s gross domestic product (GDP) reverts faster 

to equilibrium when there is a shock to exports to and imports from Brazil, as against 

Nigeria exports to and imports from the other BRICS countries. A key policy implication of 

the results is that of all the BRICS countries, Brazil appears to have the most potential in 

terms of improving Nigeria’s trade position. 
 

Keywords: Trade intensity, Vector autoregression, Impulse-response, BRICS, MINT, Policy. 

JEL Classification: C32; C51; F14. 

http://creativecommons.org/licenses/by/3.0/


Economy, 2014, 1(2): 37-53 

 

 

 

 

38 

 

series of reforms have been initiated and implemented, average real GDP growth has been robust at over 6% as 

indicted in Figure 2.  

 

 
Fig-1. Nigeria's Real GDP size 1961-2011 (million) 

                                      Source: World Development Indicators 

 

 
Fig-2. Nigeria's Real GDP Growth 1961-2011 (%) 

                              Source: World Development Indicators 
 

With respect to the structure of the economy, Figure 3 shows that between 2002 and 2007, the Nigerian economy 

was substantially agrarian with the agriculture sector contributing approximately 37% to the GDP, the service sector 

contributed 24% while manufacturing sector had the least contribution of 3.1% in the period. The industrial sector 

contribution of 39% is as a result of the inclusion of oil and gas activities in the computation of the sector’s 

contribution to the GDP. However, after the rebasing of the GDP in April 2014, the structure of the Nigerian 

economy has changed has changed with the share of agricultural sector to the GDP declining from 33% to 22% while 

the share of the services sector has increased from 26% to about 51% of GDP.  
 

 
Fig-3. Composition of Nigeria's GDP (%)   

                                       Source: World Development Indicators 



Economy, 2014, 1(2): 37-53 

 

 

 

 

39 

 

 
Fig-4. Growth in GDP per capita and inflation 

                                                 Source: World Development Indicators 

 

In terms of welfare, the purchasing power as shown in Figure 4 has been eroded by rising inflation over the 

years. Between 1961 and 2011, the inflation rate in Nigeria averaged 16% while the growth in GDP per capita was 

1.6%. This erosion in real income was prevalent in the mid-1990s when inflation rate spiked significantly as against 

growth in income that was relatively stable in the period. However, inflation rate has been at single digit in the recent 

times.  

Nigeria’s integration into the global economy has been on the rise since the 1990s with the trade balance 

increasing relative to the GDP. Figure 5 shows that between 1960 and 1989, the country’s trade balance (% of GDP) 

averaged 34.2%.  However, in the period 1990 to 2011, it averaged 76.2%, implying more integration with the global 

economy. With respect to the current account balance, since 2005 Nigeria has maintained a positive balance (% of 

GDP), meaning that inflows into the economy have been higher than the outflows.  

 

 
Fig-5. Nigeria's Trade Balance (% of GDP)   

                                       Source: World Development Indicators 

 

 
Fig-6. Nigeria's Current Account Balance (% of GDP)   

                                        Source: World Development Indicators 



Economy, 2014, 1(2): 37-53 

 

 

 

 

40 

 

3. Methodology   
3.1. Descriptive Analysis 

In line with studies in the literature, e.g., Oehler-Şincai (2011) the first objective of the study is to estimate the 

level of trade intensity between Nigeria and the individual BRICS. The trade intensity between exporter i and 

importer j is defined as: 

                                              Trade Intensity (TI) =     
   

  
 
   

  
     (1) 

             Where 

                                                   = country   exports to country    
                                                  = country   total exports 

                                                  = world exports to country    
                                                 = total world exports.  

An index above one indicates larger exports from country i to country j than would be expected from country j’s 

importance in world trade. 

 

3.2. Estimation Technique 
The estimation approach for the study is the Hjalmarsson and Österholm (2007)

1
 multivariate vector 

autoregressive (VAR) cointegration technique which assumes that all the variables are endogenous. A VAR with p 

lags is stated in the form below; 

                                    tptpttt yAyAyAvy   ...2211    (2)  

                                                       

where ty
 is a K × 1 vector of endogenous variables, v  is K × 1 vector of parameters, pAA 1  are K × K 

matrices of parameters, and t  is K × 1 vector of disturbance terms. The VAR is used when there is no cointegration 

among the variables and it is estimated using time series that have been transformed to their stationary values. 

However, if evidence of cointegration exists, the vector error correction (VECM) is estimated. The number of co-

integrating vectors is determined using the trace test and the maximum-eigenvalue test. Therefore, we estimate the 

following equation;    

 

                                   ),( ,, tttt INFIMFiEXTiGDP       (3)                    

         Where; 

                                   GDPt  = Nigeria’s gross domestic product  

                                   EXTit  = Nigeria’s exports to each of the individual BRICS 

                                   IMFit  = Nigeria’s imports from each of the BRICS 

                                    INFt   = Nigeria’s Africa’s domestic inflation rate 

 

Given that the main limitation of the VAR/VECM model is the lack of a strong theoretical basis for estimated 

coefficients, the study will focus on discussing the impulse response and the variance decomposition analyses. 

However, before estimating equation 3, the Augmented Dickey–Fuller (ADF) test will be used to test the time series 

properties of the selected variables while appropriate lag length will be determined using the relevant criteria such as 

the Akaike Information Criterion [AIC] and the Bayesian Information Criterion [BIC].  

 

3.3. Data Type and Source  
Annual time series data from 1995 to 2011 is used to estimate the trade intensity index between Nigeria and each 

of the BRICS. In order to have sufficient data points for the empirical analysis, quarterly data between 2005Q1 and 

2012Q1 is applied. The sources of the data include UNCTAD – for the exports and imports variables, while the GDP 

and inflation rates were sourced from the Central Bank of Nigeria statistical bulletins.   

 

4. Data Presentation - Trade Flows between Nigeria and the BRICS  
 

The trade flows between Nigeria and the individual BRICS between 1995 and 2011 is depicted in Figures 7 to 

11. Specifically, and as shown in Figure 7, Brazil recorded an average $2,156.9 million trade deficit with Nigeria in 

the period given that its exports to Nigeria averaged $703.4 million while its imports from Nigeria averaged $2, 

860.4 million. Figure 8 shows that Russia maintained trade surplus with Nigeria as its exports averaged $109.5 

million and imports $6.3 million, implying that the country maintained an average trade surplus of $103.2 million 

with Nigeria in the period. The trade flow between India and Nigeria as shown in Figure 9 indicates that apart from 

2004 and 2005 when India recorded positive trade balance with Nigeria, all other years were negative. Overall, 

India’s exports to Nigeria averaged $801.9 million in the review period while imports were $3,939.1 million, 

bringing the trade deficit to an average of $3,137.2 million.   

The trade flow between China and Nigeria as shown in Figure 10 indicates that the Asian country recorded trade 

surplus with Nigeria in the review period. China’s exports to Nigeria and imports from Nigeria averaged $2.6 billion 

and $404.7 million respectively between 1995 and 2011, resulting in a trade surplus of $2.2 billion in the period. 

South Africa’s trade flows with Nigeria as shown in Figure 11 indicates that total exports to Nigeria averaged $390.3 

million while imports were $885.4 million, thereby giving a trade deficit of $495.1 million.  

                                                             
1
 Cited in Hjalmarsson  and Österholm, (2007). 



Economy, 2014, 1(2): 37-53 

 

 

 

 

41 

 

The trade intensity analysis as shown in Figure 12 indicates that between 1995 and 2011, Nigeria’s trade 

intensity was highest with India, followed by trade with Brazil and then with South Africa. The intensity index with 

China and Russia are less than 1 but was lowest with Russia. This implies that among the BRICS, Russia was the 

smallest trading partner with Nigeria in the period 1995 – 2011.  

 

 
Fig-7. Nigeria - Brazil Trade Balance  

                                        Source: UNCTAD and Authors estimations 

 

 
Fig-8. Nigeria - Russia Trade Balance 

                                          Source: UNCTAD and Authors estimations 

 

 

Fig-9. Nigeria - India Trade Balance 
                                         Source: UNCTAD and Authors estimations 

 

 

Fig-10. Nigeria - China Trade Balance 
                                         Source: UNCTAD and Authors estimations 



Economy, 2014, 1(2): 37-53 

 

 

 

 

42 

 

 

Fig-11. Nigeria - S/Africa Trade Balance Fig-12. Nigeria - BRICS Trade Intensity 
             Source: UNCTAD and Authors estimations                                        Source: UNCTAD and Authors estimations 

 

 

5. Empirical Results  
In this section, attempt is made to provide empirical support for the trading relationship between Nigeria and the 

individual BRICS using the traditional VAR technique, although some studies, for example Mustafa and Kabundi 

(2011) used the Global VAR. The analysis focuses on Nigeria and Brazil, Nigeria and China and then Nigeria and 

South Africa, all between 2005Q1 and 2012Q1. However, the unavailability of data for Russia and India means that 

both countries are omitted from the analysis.    
 

5.1. Nigeria and Brazil  

5.1.1. Unit Root, Lag Length and Cointegration  
Table 1 shows the results of the test for time series properties of the variables using the Augmented Dicker Fuller 

(ADF) test. The outcome indicates that all the indicators, gross domestic product (GDP), Nigeria’s exports to Brazil 

(EXTBR), Nigeria’s imports from Brazil (IMFBR) and Nigeria’s inflation rate (INF) are I (1) series as they are 

stationary after first differencing.   

 
Table-1. Augmented Dickey Fuller Test 

 P-value at Level            P-value at First Difference 

GDP    0.7576                         0.0000 

EXTBR    0.3715                         0.0000 

IMFBR    0.7594                         0.0000 

INF    0.4866                         0.0158 
           Source: Authors’ estimations 

 

In order to proceed to ascertaining if there are cointegrating vectors in the equation, we first choose the 

appropriate lag length using the Akaike information criterion (AIC), the Schwarz Bayesian criterion (SBIC), and the 

Hannan-Quinn criterion (HQC). Therefore, Table 2 provides that the appropriate lag length is 2 as suggested by the 

AIC and HQC criterion.   

 

 
Table-2. Lag length selection 

Lags   loglik p(LR)    AIC     BIC    HQC  

1 44.0471  -1.9237 -0.9486* -1.6533 

2 67.1352 0.0000 -2.4908* -0.7356 -2.0040* 

          Note: AIC = Akaike criterion, SBIC = Schwarz Bayesian criterion and HQC = Hannan-Quinn criterion.  

 

The result of the Johansen cointegration test as shown in Table 3 indicates that using the eigenvalue and trace 

tests, there exist at least one cointegrating vector in the equation. Therefore, the vector error correction model is 

estimated prior to using the impulse response analysis to ascertain how Nigeria’s GDP responds to shocks in exports 

to and imports from Brazil.   

    
Table-3. Johansen Co-integration Test 

Rank Eigenvalue  Trace test P-value 

0 0.8755 108.7700 0.0000 

1 0.7259   56.6790 0.0000 

2 0.5107   24.3210 0.0014 

3 0.2274     6.4510 0.0111 
          Source: Authors’ estimations 

 

5.1.2. Impulse Response Analysis    
The response of Nigeria’s GDP to a one standard error shock to exports to Brazil is depicted in Figure 13 and the 

GDP responds positively in Q1, moderates afterwards and was negative in Q4. Following from this, the response 



Economy, 2014, 1(2): 37-53 

 

 

 

 

43 

 

gets positive but unstable until the effect gets flat from Q10. On the contrary, the response of GDP to a shock in 

imports from Brazil as shown in Figure 14 indicates that the effect of the response was mixed in the initial quarters. 

While the response was positive and sharp between Q1 and Q2, the response in Q3 was negative before becoming 

positive again in Q4 and then dies out from Q5. When compared with the response to a shock to exports to Brazil, it 

means that the GDP reverts faster to equilibrium when there is a shock to imports from Brazil.  

When emphasis is placed on how Nigeria’s exports to Brazil respond to a one standard error shock to the GDP, 

Figure 15 shows that the response declined in Q1 and eventually dies out from Q10. Similarly, the response of 

Nigeria’s imports from Brazil to a one standard error shock to the GDP as shown in Figure 16 also dies out from Q10 

after declining in Q1 and also negative in Q2. The response of Nigeria’s GDP to a one standard error shock to the 

domestic inflation rate shows that the initial response is sharp and negative between Q1 and Q3 before becoming 

relatively stable, although still negative. This negative response of the GDP to a shock to inflation, however, 

becomes flat from Q10 and remained so throughout the period.  

 

 
Fig-13. Response of GDP to shock in exports to Brazil Fig-14. Response of GDP to shock in imports from Brazil 

 

 
Fig-15. Response of exports to Brazil_to a shock in GDP Fig-16. Response of imports from Brazil_to a shock in GDP 

 

 
Fig-17. Response of GDP to a shock in domestic inflation 

 

5.1.3. Variance Decomposition Analysis    
The objective of the variance decomposition analysis is to provide the extent to which the variation in a 

particular variable is explained by the other variables in the equation. Table 1 in Appendix
2
 A shows that on average 

87% of the variation in Nigeria’s GDP is explained by own effect, followed by imports from Brazil (9.2%), exports 

to Brazil (2.9%), while inflation rate explains the least average variation of approximately 0.7% of the GDP. 

Similarly, Table 2 indicates that own effect explains the highest variation of 89% in Nigeria’s exports to Brazil 

followed by inflation (7%), imports from Brazil (2.3%), while the least variation of 2.1% is explained by the GDP. 

Also, own effect explains the highest average variation of 74% in imports from Brazil while GDP explains 14.8%, 

followed by exports to Brazil (9.1%), while inflation explains the least average variation of 1.8%. With respect to the 

level of variation in the domestic inflation rate, Table 4 explains that own effect is responsible for average 67% while 

exports to Brazil is responsible for 32%, followed by imports from Brazil (1.2%) and GDP (0.09%).  
 

 

 

                                                             
2 It is noteworthy that all the variance decomposition analysis (VDCs) results are housed in the Appendix to the paper in order to conserve space. In other words, 

the VDCs associated with the bilateral trade flows between Nigeria and Brazil, Nigeria and China as well as Nigeria and South Africa are located Tables 1 to 4 in 

Appendix A, B and C respectively.  



Economy, 2014, 1(2): 37-53 

 

 

 

 

44 

 

5.1.4. Diagnostic Tests         
Diagnostic tests are conducted in order to provide validation to the results of the trading relationship between 

Nigeria and Brazil. The results as shown in Table 4 below indicate that the errors are normally distributed while there 

is no evidence of the presence of autocorrelation and heteroskedasticity.      

 

 
Table-4. Post estimation tests 

 

 

 
                           

Source: Authors’ estimates 

 

5.2. Nigeria and China  

5.2.1. Unit Root, Lag Length and Cointegration 
Table 5 shows that in addition to the gross domestic product and inflation rate that are stationary after first 

differencing, Nigeria’s exports to China (EXTCH) and imports from China (IMFCH) are also I (1) series and are 

appropriate to be included in the VAR estimation.  

 
Table-5. Stationarity Test 

 P-value at Level           P-value at First Difference 

GDP    0.7576                         0.0000 

EXTCH    0.6504                         0.0000 

IMFCH    0.5177                         0.0000 

INF    0.4866                         0.0158 
            Source: Authors’ estimates 

 

In addition to testing for the time series properties of the variables, Table 6 shows that the lag length selection of 

2 is the appropriate level as suggested by the Akaike criterion and the Hannan-Quinn criterion.    

 
Table-6. Lag Length Selection 

Lags   loglik p(LR)    AIC  BIC     HQC 

1 30.3923  -0.8314 0.1437* -0.5609 

2 53.9310 0.0000 -1.4345* 0.3207 -0.9476* 
           Note: AIC = Akaike criterion, BIC = Schwarz Bayesian criterion and HQC = Hannan-Quinn criterion.  

 

From the results of the Johansen cointegration test in Table 7, at least one cointegrating vector is present in the 

equation using the eigenvalue and trace tests. This means that we estimate the VECM with the aim of ascertaining 

the impulse response and error variance decomposition.  

 
Table-7. Johansen Co-integration Test 

Rank Eigenvalue    Trace test P-value    

0 0.8362 89.0660 0.0000 

1 0.5592 43.8410 0.0005 

2 0.4411 23.3640 0.0021 

3 0.2973 8.8198 0.0030 
            Source: Authors’ estimations 

 

5.2.2. Impulse Response Analysis    
The impulse response analysis for Nigeria’s GDP and exports to China is shown in Figure 18. The response of 

the GDP to a shock in exports to China is positive in the initial quarters but by Q4 the response becomes negative. 

Although this improved by Q5, the effect was flat from Q9 and remained so afterwards. When the impulse response 

analysis is reversed, that is, considering the response of Nigeria’s exports to China to a one standard error shock to 

GDP, Figure 19 shows that the unstable response between Q1 and Q4 gave way for stability, with the effect 

remaining flat and positive from Q5.  

The response of the GDP to a one standard error shock in imports from China as shown in Figure 20 depicts that 

the effect dies out from Q6 after the sharp positive response in Q1 and the negative response between Q3 and Q4. 

Again, the reversal of the impulse response analysis as shown in Figure 21 indicates that in the event of a shock to 

the GDP, the response of imports from China is a sharp decline from the positive level in Q1 to a negative response 

in Q2. The response improved between Q3 and Q4 and then dies out from Q6.   

With respect to the response of Nigeria’s GDP to a one standard error shock to the domestic inflation rate, Figure 

22 shows that the response is a sharp negative decline between Q1 and Q5 before becoming flat for the rest of the 

period from Q6.   

 

 Null hypothesis  P-value  

Normality Error is normally distributed 0.6231 

Autocorrelation  Autocorrelation not present 0.8560 

Heteroskedasticity No presence of  heteroskedasticity 0.2156 



Economy, 2014, 1(2): 37-53 

 

 

 

 

45 

 

 
Fig-18. Response of GDP to shock in exports to China Fig-19. Response of GDP to shock in imports from China 

 

 
Fig-20.Response of exports to China_to a shock in GDP Fig-21.Response of imports from China_to a shock in GDP 

 

 
Fig-22.Response of GDP to a shock in domestic inflation 

 

 

5.2.3. Variance Decomposition Analysis    
The results of the variance decomposition analysis for the trading relationship between Nigeria and China are 

provided in Appendix B.  Table 1 show that own effect explains the highest variation of 81% in Nigeria’s GDP while 

inflation rate explains the second highest variation of 9%. Imports from China explain 8.8% in the variation in the 

GDP while exports to China explain the least variation of 1.2% in the GDP. The results of the variance 

decomposition for Nigeria’s exports to China is shown in Table 2 and indicates that own effect explains 81% of the 

variation followed by the inflation rate and then the GDP. Imports from China explain the least variation in Nigeria’s 

exports to China.   

Also, Table 3 shows that the GDP explains the highest variation of 43% in Nigeria’s imports from China 

followed by own effect of 40% and then inflation rate with approximately 11%. Exports to China explain the least 

variation of 6% in Nigeria’s imports from China. The highest variation in the domestic inflation rate of 94% is 

explained by own shock as shown in Table 4, while exports to China is responsible for 3% of the variation in 

domestic inflation. The GDP and imports from China are responsible for 2% and 0.6% of the variation in Nigeria’s 

domestic inflation rate in that order.   

 

5.2.4. Diagnostic Tests     
In order to provide some evidence of validity for the results of the trading relationship between Nigeria and 

China, the combined residual plot shown in Figure 23 indicates that the residuals are stationary. This suggests that 

the results obtained are valid.  



Economy, 2014, 1(2): 37-53 

 

 

 

 

46 

 

 

 
Figure-23. Combined residual plot 

 

In addition to the combined plots, Table 8 shows the results of other diagnostic tests and indicates that the errors 

are normally distributed, while we also fail to reject the null hypotheses of no presence of autocorrelation and 

heteroskedasticity.      

 
Table-8. Post Estimation Tests 

         Source: Authors’ estimations 

 

5.3. Nigeria and South Africa   

5.3.1. Unit Root, Lag Length and Cointegration  
Table 9 shows that Nigeria’s exports to South Africa (EXTSA) and imports from South Africa (IMFSA) have 

unit root at level before becoming stationary after first differencing, making them I (1) series alongside GDP and 

inflation. In addition, Table 10 shows that all the selection lag length selection criteria indicate that 1 is the 

appropriate lag length.    

 
Table-9. Augmented Dickey Fuller Test 

 P-value at Level P-value at First Difference 

GDP       0.7576                  0.0000 

EXTSA       0.1060                  0.0000 

IMFSA       0.7985                  0.0000 

INF       0.4866                  0.0158 
         Source: Authors’ estimations 
 

Table-10. Lag length selection 

Lags loglik p(LR)          AIC      BIC    HQC 

1 27.1746          -0.5739*    0.4011* -0.3035* 

2 36.9062 0.2454         -0.0725    1.6826  0.4143 
            Note: AIC = Akaike criterion, BIC = Schwarz Bayesian criterion and HQC = Hannan-Quinn criterion 

 

The results for the eigenvalue and trace tests as reported in Table 11 indicate that there exists at least one 

cointegrating vector in the equation. This implies that the vector autoregressive model can be estimated with the aim 

of tracing out the response of Nigeria’s GDP to shocks to its exports to South Africa, its imports from South Africa 

as well as the domestic inflation rate. Following from this, the variance decomposition analysis is also carried out.     

 
Table-11. Johansen Co-integration Test 

Rank Eigenvalue    Trace test P-value 

0 0.85098 98.7520 0.0000 

1 0.60524 49.2560 0.0000 

2 0.53278 25.0900 0.0010 

3 0.18456 5.3048 0.0213 

          Source: Authors’ estimations 
 

5.3.2. Impulse Response Analysis    
The response of Nigeria’ GDP to a one standard error shock to exports to South Africa is depicted in Figure 24. 

The response between Q1 and Q6 was unstable, fluctuating in the positive and negative regions before moderating 

between Q7 and Q10. The effect of the shock finally dies out from Q11. Figure 25 shows that the response of 

-3

-2

-1

 0

 1

 2

 3

 2005  2006  2007  2008  2009  2010  2011  2012

System residuals

d_l_GDP

d_l_EXTCH

d_l_IMFCH

d_l_Inf

 Null hypothesis  P-value  

Normality Error is normally distributed 0.7524 

Autocorrelation  Autocorrelation not present 0.7450 

Heteroskedasticity No presence of  heteroskedasticity 0.2405 



Economy, 2014, 1(2): 37-53 

 

 

 

 

47 

 

Nigeria’s GDP to a shock in imports from South Africa is negative in Q1. Although the response improved in Q2, it 

stayed negative before becoming flat from Q6.  

However, in the event of a shock to the GDP, Nigeria’s exports to South Africa as shown in Figure 26 indicates a 

sharp decline from a positive level to negative in Q2. The volatility in the response reduced from Q3 before the effect 

finally dies out from Q10.  Also, when a shock to GDP is considered, the response of Nigeria’s imports from South 

Africa as shown in Figure 27 indicates that from a positive state in Q1, the response is negative in Q3 and becomes 

flat from Q4.  With respect to the response of Nigeria’s GDP to a one standard error shock to the domestic inflation 

rate, Figure 28 shows that the response is a sharp negative decline between Q1 and Q2 before becoming flat for the 

rest of the period from Q4.    

 

 
Fig-24. Response of GDP to shock in exports to S/Africa Fig-25. Response of GDP to shock in imports from S/Africa 

 

 
Fig-26. Response of exports to S/Africa_to a shock in GDP Fig-27. Response of imports from S/Africa_to a shock in GDP 

 

 
Fig-28. Response of GDP to a shock in domestic inflation 

 

5.3.3. Variance Decomposition Analysis    
Appendix C provides the results of the variance decomposition analysis for the trading relationship between 

Nigeria and South Africa. From Table 1, own effect explains average 91% of the variation in Nigeria’s GDP while 

import from South Africa is responsible for 3.8%. In addition, exports to South Africa explain 3.7% of the variation 

in Nigeria’s GDP while inflation explains the least variation of average 1.8%. Similarly, the highest variation in 

Nigeria’s exports to South Africa is explained by own shock of 93%, while GDP accounts for 5.9%. Imports from 

South Africa and the domestic inflation rate explain less than 1% of the variation in exports to South Africa. Also, 

own shock explains the highest variation of average 68% in Nigeria’s imports from South Africa while GDP is 

responsible for 17% and then exports to South Africa explains 10%. The domestic inflation rate explains the least 

variation of 4% in Nigeria’s imports from South Africa. With respect to how other variables in the equation explain 

the variation in the domestic inflation rate, Table 4 indicates that own shock accounts for approximately average 86% 



Economy, 2014, 1(2): 37-53 

 

 

 

 

48 

 

of the variation, while imports from South Africa explains 9.3% followed by GDP 1.2% and exports to South Africa 

0.06%.  

 

5.3.4. Diagnostic Tests     
Figure 29 is a combined residual plot for the results of the trading relationship between Nigeria and South Africa. 

Given that the residuals are stationary this implies that the results obtained from the estimated model are valid. 
 

 

 
Figure-29. Combined Residual Plot 

 
 

Table 12 also shows that the results of other diagnostic tests. From the results, while we fail to accept the null 

hypothesis that the errors are normally distributed, the null hypotheses of no presence of autocorrelation and 

heteroskedasticity are not rejected.     

 
Table-12. Post estimation tests 

              

 

 

             

Source: Authors’ estimations 

 

6. Policy Implications of Findings 
The findings in this study have a number of policy implications: 

 Nigeria’s trade intensity is highest with Brazil while on the average, the intensity index with Brazil, India and 

South Africa is above 1, implying that an improved relationship between the BRICS and Nigeria will be 

beneficial. However, the downside and general perception is that the individual BRICS are pursuing a bilateral as 

opposed to a joint approach in their dealings with key countries in Africa, including Nigeria.  

 The finding that Nigeria’s GDP reverts faster to equilibrium when there is a shock to exports to and imports from 

Brazil further confirms the growing bilateral ties between Nigeria and Brazil when compared with other BRICS 

members. However, the fact that the equilibrium adjustment of Nigeria’s exports to Brazil and South Africa is at 

the same period when there is a shock to the GDP also implies the growing relevance of the bilateral relationship 

between Nigeria and South Africa.  

 The relatively strong link between the Nigerian economy and Brazil is explained by the fact that apart from own 

effect, imports from Brazil and exports to Brazil are responsible for the second and third highest variations in 

Nigeria’s GDP. Similarly, the rising bilateral relevance with South Africa explains why import from and exports 

to South Africa are responsible for the second and third highest variation in Nigeria’s GDP when the trading 

relationship between both countries is considered.  

 Given that the GDP explains the second highest variation in Nigeria’s exports to South Africa, it implies that a 

growing Nigerian economy may result in increased exports to South Africa in the future. This scenario may be 

different for Brazil and China as the inflation rate explains the second highest variation in Nigeria’s exports to 

the two countries. In other words, Nigeria may only maintain its competitiveness with increased trading with 

Brazil and China if inflation is low and stable.  

 A growing Nigerian economy may experience more imports from China given that the highest variation in 

Nigeria’s imports from China is explained by the GDP.  

 There is no threat of imported inflation from China into Nigeria given that imports from China explain the least 

variation in Nigeria’s inflation rate. However, this threat is not misplaced in the case of South Africa given that 

Nigeria’s imports from South Africa explain the second highest variation in Nigeria’s inflation rate. 

 

References 
Alao, A., 2011. Nigeria and the BRICs: Diplomatic, trade, cultural and military relations. SAIIA Occasional Paper, No. 101. 
Hjalmarsson, E. and P. Österholm, 2007. Testing for cointegration using the Johansen methodology when variables are near-integrated. 

International Monetary Fund Working Paper No. 07/141. 

Mustafa, Y. and A. Kabundi, 2011. Trade shocks from BRIC to South Africa. A global VAR analysis. Available from 

http://ideas.repec.org/p/rza/wpaper/250.html [Accessed August 12th 2012]. 

-5

-4

-3

-2

-1

 0

 1

 2

 3

 2005  2006  2007  2008  2009  2010  2011  2012

System residuals

d_l_GDP

d_l_EXTSA

d_l_IMFSA

d_l_Inf

 Null hypotheses  P-value  

Normality Error is normally distributed 0.0005 

Autocorrelation  Autocorrelation not present 0.6410 

Heteroskedasticity No presence of  heteroskedasticity 0.2640 

http://ideas.repec.org/p/rza/wpaper/250.html


Economy, 2014, 1(2): 37-53 

 

 

 

 

49 

 

Naresh, K. and F. Alina, 2011. Perspective on economic growth of BRIC countries. A case of Brazil and India. Available from 
http://ssrn.com/abstract=1342255 [Accessed August 12th 2012]. 

Oehler-Şincai, I.M., 2011. Trends in trade and investment flows between the EU and the BRIC countries. Theoretical and Applied Economics, 

6(559): 73-112. 

  
 

 

Appendix-A. Nigeria and Brazil 

 
Table-1. Decomposition of Variance for Nigeria’s GDP 

Period GDP EXTBR IMFBR INF 

1 100.0000          0.0000 0.0000 0.0000 

2 89.4179      2.7213      7.8572      0.0036 

3 87.6937      2.6976      9.4993      0.1094 

4 87.2655      2.7096      9.8340      0.1909 

5 86.9457      2.9344      9.8650      0.2549 

6 86.8476      2.9311      9.8847      0.3366 

7 86.7060      3.0155      9.8722      0.4063 

8 86.6200      3.0294      9.8678      0.4828 

9 86.5124      3.0770      9.8556      0.5551 

10 86.4207      3.1030      9.8468      0.6296 

11 86.3213      3.1405      9.8357      0.7025 

12 86.2270           3.1711 9.8258      0.7761 

13 86.1302      3.2055      9.8152      0.8491 

14 86.0353      3.2375      9.8051      0.9221 

15 85.9396      3.2708      9.7947      0.9949 

16 85.8447      3.3032      9.7845      1.0676 

17 85.7497      3.3360      9.7742      1.1401 

18 85.6550      3.3685      9.7641      1.2124 

19 85.5605      3.4011      9.7539      1.2846 

20 85.4663      3.4335      9.7437      1.3566 

Ave. 87.1680 2.9543 9.2140 0.6638 
          Source: Authors’ estimates 

 
Table-2. Decomposition of Variance for Nigeria’s exports to Brazil 

Period GDP EXTBR IMFBR INF 

1 2.5209     97.4791      0.0000 0.0000 

2 2.3072     94.6609      1.5630      1.4688 

3 2.2222     93.5693      2.2025      2.0060 

4 2.2197     92.3060      2.6082      2.8661 

5 2.1917     91.6461      2.6261      3.5361 

6 2.1726     90.8580      2.6722      4.2971 

7 2.1462     90.2341      2.6386      4.9811 

8 2.1237     89.5594      2.6301      5.6869 

9 2.0994     88.9435      2.6013      6.3557 

10 2.0768     88.3178      2.5827      7.0226 

11 2.0541     87.7199      2.5584      7.6676 

12 2.0322     87.1268      2.5379      8.3031 

13 2.0105     86.5511      2.5158      8.9226 

14 1.9894     85.9847      2.4953      9.5305 

15 1.9687     85.4317      2.4747     10.1249 

16 1.9485     84.8893      2.4548     10.7075 

17 1.9286     84.3584      2.4350     11.2780 

18 1.9092     83.8379      2.4159     11.8371 

19 1.8901     83.3280      2.3970     12.3849 

20 1.8715     82.8281      2.3785     12.9219 

Ave. 2.0842 88.4815 2.3394 7.0949 
           Source: Authors’ estimates 

 

Table-3. Decomposition of Variance for Nigeria’s imports from Brazil 

Period GDP EXTBR IMFBR INF 

1 15.2295      4.2953     80.4752      0.0000 

2 15.5809      6.6001     77.6241      0.1949 

3 15.3391      7.5675     76.6307      0.4626 

4 15.0705      8.9279     75.4137      0.5880 

5 15.0111      8.9405     75.2391      0.8093 

6 14.9279      9.2697     74.8307      0.9718 

7 14.8927      9.2617     74.6782      1.1674 

8 14.8420      9.3942     74.4212      1.3426 

9 14.8042      9.4292     74.2384      1.5282 

10 14.7606      9.5140     74.0191      1.7063 

11 14.7211      9.5680     73.8238      1.8872 

    Continue 

http://ssrn.com/abstract=1342255


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50 

 

12 14.6797      9.6384     73.6170      2.0649 

13 14.6398      9.6987     73.4186      2.2429 

14 14.5994      9.7642     73.2172      2.4192 

15 14.5596      9.8261     73.0193      2.5950 

16 14.5198      9.8895     72.8211      2.7695 

17 14.4804      9.9515     72.6248      2.9433 

18 14.4411     10.0138     72.4291      3.1160 

19 14.4021     10.0754     72.2347      3.2878 

20 14.3632     10.1368     72.0412      3.4587 

Ave. 14.7932 9.0881 74.3409 1.7778 
          Source: Authors’ estimates 

 
Table-4. Decomposition of Variance for Nigeria’s inflation 

Period GDP EXTBR IMFBR    INF 

1 0.4019 24.7439 4.4182 70.4360 

2 0.2561 31.0384 2.1466 66.5590 

3 0.1805 30.4113 1.9760 67.4322 

4 0.1407 31.6554 1.5213 66.6827 

5 0.1129 31.5874 1.3758 66.9238 

6 0.0952 31.9444 1.2136 66.7469 

7 0.0819 31.9981 1.1273 66.7927 

8 0.0722 32.1392 1.0474 66.7413 

9 0.0645 32.1974 0.9928 66.7454 

10 0.0584 32.2705 0.9451 66.7260 

11 0.0533 32.3165 0.9082 66.7219 

12 0.0492 32.3621 0.8764 66.7123 

13 0.0456 32.3968 0.8500 66.7075 

14 0.0426 32.4287 0.8271 66.7016 

15 0.0400 32.4552 0.8074 66.6975 

16 0.0377 32.4789 0.7900 66.6933 

17 0.0357 32.4996 0.7748 66.6900 

18 0.0339 32.5181 0.7612 66.6868 

19 0.0323 32.5346 0.7491 66.6841 

20 0.0308 32.5495 0.7382 66.6816 

Ave. 0.0933 31.7263 1.2423 66.9381 
           Source: Authors’ estimates 

 

Appendix-B. Nigeria and China 

 
Table-1. Decomposition of Variance for Nigeria’s GDP 

Period GDP EXTCH IMFCH INF 

1 100.0000 0.0000 0.0000 0.0000 

2 88.2973 0.2383 10.6027 0.8616 

3 86.8884 0.7321 10.4724 1.9072 

4 85.2235 1.2732 10.3763 3.1270 

5 84.3862 1.2640 10.2663 4.0836 

6 83.4597 1.2872 10.1575 5.0956 

7 82.5803 1.2980 10.0475 6.0742 

8 81.7039 1.3205 9.9382 7.0374 

9 80.8569 1.3357 9.8327 7.9747 

10 80.0239 1.3527 9.7291 8.8944 

11 79.2096 1.3687 9.6276 9.7940 

12 78.4118 1.3847 9.5283 10.6752 

13 77.6308 1.4001 9.4311 11.5381 

14 76.8655 1.4154 9.3358 12.3834 

15 76.1157 1.4302 9.2424 13.2116 

16 75.3809 1.4448 9.1509 14.0233 

17 74.6607 1.4592 9.0612 14.8189 

18 73.9546 1.4732 8.9733 15.5989 

19 73.2622 1.4869 8.8871 16.3638 

20 72.5831 1.5004 1.5004 17.1139 

Ave.  80.5748 1.2233 8.8080 9.0288 
         Source: Authors’ estimates 

 
Table-2. Decomposition of Variance for Nigeria’s exports to China 

Period GDP EXTCH IMFCH INF 

1 4.3091 95.6909 0.0000 0.0000 

2 6.1423 90.0867 0.4633 3.3077 

3 7.2798 88.2793 0.4497 3.9913 

4 7.1405 86.9989 0.4757 5.3849 

5 7.1287 85.9134 0.4698 6.4881 

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Economy, 2014, 1(2): 37-53 

 

 

 

 

51 

 

6 7.0459 84.8081 0.4640 7.6821 

7 7.0015 83.7495 0.4592 8.7898 

8 6.9407 82.7125 0.4546 9.8922 

9 6.8879 81.7042 0.4500 10.9579 

10 6.8339 80.7199 0.4455 12.0007 

11 6.7823 79.7604 0.4411 13.0161 

12 6.7315 78.8241 0.4369 14.0075 

13 6.6821 77.9104 0.4327 14.9748 

14 6.6338 77.0184 0.4287 15.9192 

15 6.5867 76.1473 0.4247 16.8413 

16 6.5406 75.2965 0.4208 17.7420 

17 6.4956 74.4652 0.4171 18.6221 

18 6.4516 73.6528 0.4134 19.4822 

19 6.4087 72.8587 0.4097 20.3229 

20 6.3666 72.0821 0.4062 21.1450 

Ave.  6.6195 80.9340 0.4182 12.0284 
        Source: Authors’ estimates 
  

Table-3. Decomposition of Variance for Nigeria’s imports from China 

Period   GDP EXTCH     IMFCH   INF 

1 50.4434  3.6157         45.9409      0.0000 

2 47.4678  6.7550         44.6818      1.0954 

3 46.2972   6.8699         44.1737     2.6592 

4 45.7294  6.9525         43.4617      3.8563 

5 45.2079     6.8782        42.9319      4.9820 

6 44.6604    6.8203       42.3943      6.1250 

7 44.1273    6.7742         41.8591      7.2395 

8 43.6126   6.7268         41.3393     8.3212 

9 43.1103    6.6792         40.8338      9.3766 

10 42.6194    6.6333         40.3401     10.4072 

11 42.1405   6.5885         39.8581     11.4129 

12 41.6729   6.5448         39.3876     12.3947 

13 41.2162   6.5020         38.9281     13.3537 

14 40.7701    6.4603         38.4792     14.2904 

15 40.3342    6.4195     38.0405    15.2058 

16 39.9082    6.3796         37.6118     16.1004 

17 39.4916   6.3407         37.1926     16.9751 

18 39.0843    6.3025         36.7827    17.8304 

19 38.6859    6.2653         36.3818     18.6671 

20 38.2960    6.2288         35.9895    19.4856 

Ave.  42.7438 6.4369     40.3304 10.4889 
       Source: Authors’ estimates 
 

Table-4. Decomposition of Variance for domestic inflation 

Period GDP EXTCH IMFCH    INF 

1 0.0000 0.8554 2.9450 96.1996 

2 0.4040 3.4839 1.6270 94.4851 

3 1.5604 3.3688 1.0945 93.9763 

4 1.8444 3.2318 0.8708 94.0530 

5 1.9818 3.1352 0.7161 94.1668 

6 2.0831 3.1066 0.6105 94.1998 

7 2.1636 3.0745 0.5366 94.2254 

8 2.2204 3.0517 0.4812 94.2467 

9 2.2648 3.0335 0.4379 94.2637 

10 2.3004 3.0194 0.4033 94.2768 

11 2.3297 3.0077 0.3750 94.2877 

12 2.3540 2.9980 0.3514 94.2967 

13 2.3746 2.9897 0.3314 94.3043 

14 2.3922 2.9826 0.3143 94.3109 

15 2.4075 2.9765 0.2994 94.3165 

16 2.4209 2.9711 0.2864 94.3215 

17 2.4327 2.9664 0.2750 94.3259 

18 2.4432 2.9622 0.2648 94.3298 

19 2.4526 2.9584 0.2557 94.3333 

20 2.4611 2.9551 0.2475 94.3364 

Ave. 2.0446 2.9564 0.6362 94.3628 
         Source: Authors’ estimates 

 

 

 

 

 



Economy, 2014, 1(2): 37-53 

 

 

 

 

52 

 

Appendix-C. Nigeria and South Africa 

 
Table-1. Decomposition of Variance for Nigeria’s GDP 

Period GDP EXTSA IMFSA INF 

1 100.0000 0.0000 0.0000 0.0000 

2 93.9798 1.5137 4.1207 0.3857 

3 92.2790 3.1688 3.1688 0.5295 

4 91.4777 3.8171 3.9874 0.7177 

5 91.0844 4.0233 4.0089 0.8834 

6 90.8203 4.0997 4.0130 1.0670 

7 90.6131 4.1212 4.0273 1.2385 

8 90.4200 4.1263 4.0372 1.4165 

9 90.2377 4.1220 4.0507 1.5896 

10 90.0571 4.1160 4.0622 1.7647 

11 89.8793 4.1083 4.0750 1.9375 

12 89.7020 4.1005 4.0869 2.1106 

13 89.5260 4.0924 4.0993 2.2823 

14 89.3504 4.0844 4.1113 2.4538 

15 89.1757 4.0764 4.1235 2.6244 

16 89.0017 4.0684 4.1355 2.7944 

17 88.8283 4.0604 4.1476 2.9637 

18 88.6557 4.0524 4.1595 3.1324 

19 88.4837 4.0445 4.1714 3.3004 

20 88.3124 4.0366 4.0366 3.4677 

Ave. 90.5942 3.6916 3.8311 1.8330 
            Source: Authors’ estimates 

 
Table-2. Decomposition of Variance for Nigeria’s exports to South Africa 

Period GDP EXTSA IMFSA INF 

1 7.7717 92.2283 0.0000 0.0000 

2 6.3879 93.3158 0.1711 0.1251 

3 5.9681 93.6439 0.2557 0.1323 

4 5.8135 93.7390 0.2489 0.1986 

5 5.7691 93.7367 0.2654 0.2288 

6 5.7437 93.7103 0.2648 0.2812 

7 5.7369 93.6685 0.2741 0.3205 

8 5.7301 93.6249 0.2767 0.3682 

9 5.7278 93.5781 0.2833 0.4108 

10 5.7243 93.5317 0.2874 0.4566 

11 5.7222 93.4844 0.2930 0.5003 

12 5.7196 93.4375 0.2977 0.5453 

13 5.7173 93.3903 0.3029 0.5895 

14 5.7149 93.3433 0.3078 0.6341 

15 5.7126 93.2963 0.3129 0.6783 

16 5.7102 93.2493 0.3178 0.7227 

17 5.7079 93.2024 0.3228 0.7669 

18 5.7055 93.1555 0.3278 0.8112 

19 5.7032 93.1087 0.3328 0.8554 

20 5.7008 93.0619 0.3377 0.8995 

Ave. 5.8744 93.3753 0.2740 0.4763 
            Source: Authors’ estimates 

  
Table-3. Decomposition of Variance for Nigeria’s imports from South Africa 

Period GDP EXTSA IMFSA INF 

1 14.5471 10.6457 74.8071 0.0000 

2 18.0150 10.3120 71.0826 0.5904 

3 18.1251 10.6796 70.1096 1.0858 

4 18.0178 10.7389 69.6792 1.5641 

5 17.9199 10.7093 69.3457 2.0251 

6 17.8241 10.6659 69.0150 2.4951 

7 17.7356 10.6128 68.6996 2.9521 

8 17.6456 10.5592 68.3863 3.4089 

9 17.5584 10.5038 68.0796 3.8581 

10 17.4713 10.4493 67.7750 4.3044 

11 17.3857 10.3947 67.4747 4.7449 

12 17.3006 10.3409 67.1769 5.1816 

13 17.2167 10.2874 66.8826 5.6133 

14 17.1334 10.2346 66.5911 6.0408 

15 17.0511 10.1823 66.3028 6.4638 

16 16.9696 10.1306 66.0173 6.8825 

17 16.8890 10.0793 65.7347 7.2970 

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18 16.8091 10.0286 65.4549 7.7073 

19 16.7301 9.9784 65.1780 8.1135 

20 16.6518 9.9287 64.9039 8.5157 

Ave. 17.2499 10.3731 67.9348 4.4422 
             Source: Authors’ estimates 

 
Table-4. Decomposition of Variance for Nigeria’s inflation 

Period GDP EXTSA IMFSA    INF 

1 2.5177 0.2430 4.2599 92.9795 

2 1.4792 0.1319 7.4757 90.9133 

3 1.3379 0.0917 8.5252 90.0452 

4 1.2731 0.0711 8.9443 89.7116 

5 1.2252 0.0602 9.2017 89.5129 

6 1.1951 0.0522 9.3761 89.3766 

7 1.1735 0.0469 9.4989 89.2808 

8 1.1574 0.0427 9.5911 89.2088 

9 1.1449 0.0395 9.6625 89.6625 

10 1.1349 0.0370 9.7197 89.1084 

11 1.1267 0.0349 9.7664 89.0720 

12 1.1199 0.0332 9.8053 89.0417 

13 1.1141 0.0317 9.8381 89.0160 

14 1.1092 0.0305 9.8663 88.9941 

15 1.1049 0.0294 9.8906 88.9750 

16 1.1012 0.0284 9.9120 88.9584 

17 1.0979 0.0276 9.9308 88.9437 

18 1.0950 0.0269 9.9475 88.9307 

19 1.0924 0.0262 9.9624 88.9190 

20 1.0900 0.0256 9.9759 88.9085 

Ave. 1.2345 0.0555 9.2575 85.4779 
           Source: Authors’ estimates 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 
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