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 Indian Journal of Finance and Banking; Vol. 2, No. 1; 2018 

                                                         ISSN 2574-6081  E-ISSN 2574-609X 

 Published by Centre for Research on Islamic Banking & Finance and Business 

 

1 
 

 

Development and Terrorism in Nigeria: Co-Integration and 

Causality Analysis of Macroeconomic Factors 
 

Emmanuel O. Okon1 

 

1Department of Economics, Kogi State University, Anyigba, Kogi State, Nigeria 

Correspondence: Department of Economics, Kogi State University, Anyigba, Kogi State, Nigeria, E-mail: 

tonydom57@yahoo.com. Tel: +2348023275716  

 

Received: January 20, 2018         Accepted: January 20, 2018        Online Published: January 27, 2018  

 

 

Abstract 

This paper is a cointegration and causality analysis of macroeconomic factors and terrorism in Nigeria using 

time series data spanning between 1970 and 2016. The stochastic characteristics of each time series was 

examined using Augmented Dickey Fuller (ADF) test. The result reveals that LOG(GOVX), LOG(INTR), 

POLX, DLOG(GDPC) and DLOG(OPEN) were in line with the apriori expectation. With this development, 

some recommendations were made amongst which are that trade openness rate should be all time kept at peak 

benchmark by adopting tight trade openness while strategic macroeconomic policies should be instituted in 

order to encourage domestic private investment to enhance the growth of the economy. Nigerian political 

system has to be stabilized and the government should step up its intelligence gathering capacity as well as 

training security agents to forcefully combat terrorist group. 

 

Keywords: Terrorism, Economic Deprivation, Cointegration, Causality, Nigeria. 

 

1. Introduction  

Economic development is a broader concept than economic growth. Development reflects social and economic 

progress and requires economic growth. Growth is a vital and necessary condition for development, but it is not a 

sufficient condition as it cannot guarantee development(Economic Online, 2017).One of the most compelling 

definitions of development is that proposed by Amartya Sen. According to Sen (2001), development is about 

creating freedom for people and removing obstacles to greater freedom. Greater freedom enables people to choose 

their own destiny. Obstacles to freedom, and hence to development, include poverty, lack of economic 

opportunities, corruption, poor governance, lack of education and lack of health. 

Economic development in Nigeria has been rocked back and forth by various political, socio-cultural, financial 

and infrastructural setbacks (Nigerian Finder, n.d.). However, since her return to civil rule in 1999, it has faces 

some national security challenges across the six geo-political zones in the country. The spate ofbomb blasts, 

kidnapping, pipeline vandalisation and other forms of criminalities in recent times in various parts of the country 

are emerging trends of domestic terrorism (Abimbola and Adesote, 2012). A number of analysts have variously 

attributed the disturbing trend to political dissatisfaction, ethnic and religious differences, perceived societal 

neglect and pervasive poverty among the people. Nigeria is rich but its people are poor(World Bank, 1996).  

The unfortunate trend of rapidly growing population of poor people is further exacerbated by the worsening of 



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the conditions of living of poor people, i.e., the poor are becoming poorer than they used to be(Manson et al, 

2004). Poverty is caused by both microeconomic and macroeconomic as well as social-cultural factors. The 

conventional wisdom is that poverty creates terrorism but several empirical studies have challenged this view.   

The primarily aim of this paper is to provide some empirical evidence regarding macroeconomic factorsand 

their effects ondomestic terrorism in Nigeria. The paper shall attempt to provide a plausible answer to the 

question: Does economic deprivation lead to terrorism? The paper is structured as follows: Section 2 reviews 

related literature. An overview of the Nigeria‟s is presented in Section 3. The methodology of the study is 

discussed in Section 4. An econometric analysis is presented in Section 5. Section 6 then summarizes and 

concludes. 

2. Review of Related Literature 

Quantitative studies of terrorism have increased dramatically in the past decade. Many articles in this body of 

literature sought to explain terrorism as the result of poor economic development in a country. Factors such as 

poverty, employment, and development are frequently employed as economic variables in empirical terrorism 

research. Based on a sample of  112 countries from 1975 to 1997, Li and Schaub (2004) findings show that the 

economic development of a country and greater trade openness reduce the number of terrorist incidents inside 

the country. Their finding that economic development decreases the likelihood of terrorism is an interesting 

example of an economic indicator‟s effect on terrorism. Revolutionary communiqués frequently justify violence 

based on altruistic motives to rectify grievances on others' behalf (Ehrlich and Liu, 2002; Goldman, 1978; 

Hoffman, 2006; Sageman, 2008) - in this case the impoverished. Public consensus and terrorist rhetoric both 

contend poor economic conditions within a state produce motivating grievances. Although terrorist ideology 

may explain economic deprivation with a global narrative, virtual perceptions do not replace more corporal, 

proximate knowledge and opponents. Violent reactions are posited to occur. Gurr (1970) suggests that collective 

violence emerges as a result of relative deprivation theory. Specifically, he holds that “the greater the intensity 

and scope of relative deprivation, the greater the magnitude of collective violence.” 

Blomberg and Hess (2008) provide a more nuanced empirical analysis of economic development as a 

determinant of terrorism. They find that economic development is positively correlated with transnational 

terrorism, particularly in higher income countries. However, in lower income countries this trend reverses, and 

economic development is negatively related to transnational terrorism. The authors point to the importance of 

considering terrorist groups' political motivations. They say “interestingly, radicalism, separatism, and other 

ideological motivations for terrorism that appear to be intrinsically noneconomic may actually stem from 

underlying economic conditions” (Richardson, 2011).  They make the case that economic factors are important 

in different ways for higher- and lower-income countries. This could be due to a phenomenon similar to relative 

deprivation theory, in which those of different economic brackets view changes in economic factors differently. 

The authors provide two theories for this phenomenon. The “take-off” effect suggests that good policies deter 

terrorism for the most disadvantaged. As countries develop, Blomberg suggests that terrorism becomes a 

“luxury good” enjoyed by dissident groups for political purposes. However, the authors do not look at economic 

changes within a given country (Richardson, 2011). 

Economic recessions can increase the probabilities of internal and external conflicts and visa versa (Elbakidze 

and Jin, 2007). Blomberg, Hess and Weerapana (2004) find that economic recessions, represented by negative 

per capita GDP growth, could increase the probability of terrorist activities in democratic high-income countries. 

They argue that during economic recessions in high-income countries groups that are unhappy with current 

socio-economic status quo, but are unable to influence political and institutional situation, resort to terrorist 



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activities to increase their voice in the economy. Li and Schaub (2004) study the effects of economic 

globalization on the frequency of transnational terrorist incidents within a country‟s borders. They find that trade, 

foreign direct investment, and portfolio of investment of a country have no direct positive effect on the number 

of terrorist events initiated within the country. However, economic development of a country and its trading 

partners has a negative effect on the number of international terrorist incidents within a country. Therefore, if 

trade and foreign direct investment promote economic development, then these variables must indirectly reduce 

transnational terrorism. Li (2005) shows that democratic participation and economic development measured by 

GDP per capita reduces transnational terrorism while government constraints increase the number of terrorist 

incidents. Alesina et al. (1996) find that to some extent low economic growth measured by GDP per capita could 

lead to government turnovers through coups. 

A number of papers examine public opinion surveys in Middle Eastern countries to measure the public support 

for terrorism in light of an individual‟s economic standing (Krueger and Maleckova (2003), Tessler and Robbins 

(2007)). Other studies investigate the economic status and level of educational attainment of terrorists 

themselves to test the hypothesis that poverty and ignorance drive men to violent professions. Berrebi (2003) 

and Krueger and Maleckova (2003) examine biographies of terrorists to assess their educational and economic 

background. 

From the above it is observed that most of the studies focused on the relationship between economic variables 

and terrorism. However, these studies failed to examine the issue ofcausality between the variables. This is 

important because causality enable us to have a comprehensive view of whether the direction of causality runs 

in both directions one direction between the variables. The paper attempt to look at the direction of causation 

between economic factors and terrorism in Nigeria from 1970 to 2015.This paper seeks to provide new evidence 

on this topic in the light of country level economic characteristics and domestic terrorism. 

3. Overview of Terrorism and Economy Performance: Nigeria 

In Nigeria today, many terrorist networks have sprouted in many parts of the country, MEND, Boko Haram and 

MASSOB to mention just but a few, have been unleashing terror to the Nigerian public. The government is 

extremely concern in curtailing the activities of these extremist as well as other crime perpetrators ranging from 

mobile phone theft, cult activities, drug trafficking, gang related offences, fraud, kidnapping for ransom, 

organized crime and others (Okonkwo and Enem, 2011). Table 1 (see Appendix) shows categories of militia 

groups in the Niger Delta where MEND and MASSOB originated. Table 2 and 3 (see Appendix) show attacks 

blamed on two terrorist groups in Nigeria and images of terrorism are shown in Appendix. 

The Economist Intelligence Unit (EIU) 2008 Country Profile on Nigeria states  that the country displays the 

characteristics of a dual economy: an enclave oil sector with few links to the rest of the economy, except via 

government revenue, exists alongside a more typical developing African economy, heavily dependent on 

traditional agricultural, trade and some limited manufacturing. During the colonial era cash crops were 

introduced, harbours, railways and roads were developed, and a market for consumer goods began to emerge. At 

independence in 1960 agriculture accounted for well over half of GDP and was the main source of export 

earnings and public revenue, with the agricultural marketing boards playing a leading role (EIU, 2008). 

However, the rapid development of the oil sector in the 1970s meant that it quickly replaced the agricultural 

sector as the leading engine of growth. According to official Nigerian government estimates, the oil sector 

accounts for 70-80% of federal government revenue (depending on the oil price), around 90% of export earnings 

and about 25% of GDP, measured at constant basic prices(EIU, 2008). Agriculture (including livestock, forestry 

and fishing), which is still the main activity of the majority of Nigerians, constitutes about 40% of GDP(EIU, 



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2008). In recent years it has become clear that the manufacturing sector has also continued to decline, to well 

under 5% of GDP, while the services sector and the retail and wholesale sectors have continued to grow and 

now account for the majority of the remaining 30% of GDP(EIU, 2008). 

The International Crisis Group report „Nigeria: Want in the Midst of Plenty‟, published in July 2006, adds that 

the country has abundant human and natural resources but still struggles with mass impoverishment. 

Agriculture, once its primary hard currency earner, has collapsed, and food imports now account for a sixth of 

the trade bill. Manufacturing is a smaller proportion of the economy – about 6 per cent – than at independence. 

The landscape is dotted with oversized industrial projects of limited utility and capacity. Despite the country‟s 

oil wealth, extreme poverty – defined by the World Bank as living on less than $1 per day – now affects 37 per 

cent of the population. Nine out of ten Nigerians live on less than $2 daily. Corruption, a boom and bust cycle of 

oil prices and failure to diversify the economy have left the country in „a development trap‟ (ICG, 

2006).Nigeria's macroeconomic performance from 1990 to 2012 is illustrated in Table 4. 

Table 4: Nigeria's macroeconomic performance from 1990 to 2012 

Economic 

Indicators 

1990 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 

GDP growth (%) 8.2 5.4 4.6 3.5 9.6 6.6 5.8 5.3 5.7 6.0 6.7 7.67 8.6 7.8 

Oil sector growth 

(%) 

5.6 11.1 5.2 -5.2 23.9 3.3 -1.7 -3.7 -5.9 -6.2 -1.3 - - - 

Non-oil sector 

growth (%) 

8.6 4.4 2.9 4.5 5.2 7.8 8.4 9.5 9.2 9.0 8.3 - - - 

External reserves 

(% of GDP) 

NA NA NA NA 7.7 11.4 24.4 36.5 42.6 52.99 62.48 - - - 

External 

debt/GDP 

106.

5 

64.9 57.3 72.1 61.1 84.5 69.2 7.4 4.0 17.5 9.28 - - - 

Domestic 
debt/GDP 

31.3 32.2 36.6 26.1 28.6 25.3 
 

20.8 18.6 19.2 15.23 12.85 - - - 

Overall 

BOP/GDP 

–2.1 6.9 0.5 -10.

3 

-2.3 5.2 

 

10.5 12.7 1.4 8.02 9.12 - - - 

Inflation rate (%) 7.5 6.9 18.9 12.9 22.2 15 

 

17.9 8.2 5.9 11.6 11.5 13.40 11.20 12.70 

Average official 

exchange rate 

(Naira/US$) 

7.9 101.

7 

111.

9 

121 127.

8 

132.

8 

132.

8 

128.

5 

127.

4 

139.2

7 

142.8

9 

150.3

0 

155.3

0 

155.23 

 

Sources: 

(i) CBN Annual Reports and Statement of Accounts (various years) 

(ii) CBN Statistical Bulletin vol. 17, December 2006. 

(iii) National Bureau of Statistics (NBS), 2005 

(iv)Trading Economics (2013). 

4. Methodology and Data Source 

Granger causality tests and impulse response analysis of vector autoregressive models (VAR) are used to assess 

the relationshipbetween macroeconomic variables and terrorism in Nigeria. The data set consists of time series 



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spanning 1970 through 2016. The choice of the period is due to data availability. The variables under 

consideration areGDP per capita (GDPC), inflation rate (INFL),  trade openness(OPEN), government total 

expenditure (GOVX), interest rate (INTR), macroeconomic policy index (POLX) and terrorism (dummy variable). 

The data were obtained from the publication of Central Bank of Nigeria, journals, newspapers and websites. 

4.1 Specification of Model and Analytical Procedure 

The general model of the study hypotheses that terrorism in Nigeria is a function of economic variables such as 

GDP per capita, inflation rate,  trade openness, government total expenditure, interest rate, macroeconomic 

policy. The specification is given by: 

TERR = ƒ(GDPC, OPEN, INFL, GOVX, INTR, POLX)           ………...(1) 

where TERR is a dummy variable which takes the value of 1 if terrorist attack occurs in a year and 0 if otherwise, 

GDPC is per capita GDP, INFL is inflation rate, OPEN stands for degree of openness, GOVX is government 

expenditure, INTR is interest rate, POLX is policy index. The policy index dummy took on the value of unity in 

civilian rule years and zero in military rule years. 

Hereafter, a vector autoregressive (VAR) model is specified to examine the effects of shocks from economic 

variables to terrorism from which variance decomposition and impulse responses are derived to provide 

information on impulse responses of one variable over the other (Adrangi and Allender, 1998; Adebiyi and 

Oladele, 2005;Omojimite,  2012).  

Following Adebiyi (2006), let‟s consider a bivariate autoregression (AR (1)) model. Let yt be a measure of 

economic variables and zt be terrorism. A VAR system can be written as follows: 

 = A0+  A1[L] +          ……….(2) 

 

A0 is s vector of constants, A (L) a 2 x 2 matrix polynomial in the lag operator L, and uitserially 

independent errors for i. Suppose the structural equations can be represented as follows: 

 

yt= b10 - b12zt  + b11yt-1  + b11yt-1  + uyt                  …………(3) 

zt= b20 - b21yt  + b22yt-1  + b23zt-1  + uzt                  …………(4) 

 

which can be rewritten as: 

yt +b12zt = b10 + b11yt-1  + b13zt-1  + uyt                  …………(5) 

zt + b21yt = b20 + b22yt-1  + b23zt-1  + uzt                  …………(6) 

 

and in matrix form:  = +     +     .………(7) 

 

Let B  = ;  Z = ;  V0  =   ;  and V1 =  

This allows for a more compact form of the structural equation as follows: BZt =V0+V1Zt-1+uit 

Assuming that B is invertible, we pre-multiply the equation by B-1to obtain: 



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Zt=A0 +A1Z t-1+ it                ……………(8) 

 where A0 = B-1V0 ; A1= B-1V1 ; and et = B-1uit 

 

Given the aij is the element of the ith row and jth column, we can now write our VAR instandard form: 

 

yt= a10   + a11yt-1  + a12zt-1  +εyt               …………….(9) 

zt= a20   + a21yt-1  + a22zt-1  + εzt              …………….(10) 

 

and the matrix form: =  + +      …………..(11) 

 

Note that the errors are a composite of two errors uytand uzt since εt = B-1uit i.e. 

=  -1 so that: 

 

εyt  =uyt –b12uzt                   ...…………(12) 

   1-b12b21 

 

εzt  =uyt –b12uzt                                        …..……….(13) 

 1-b12b21 

Since the uits are white noise, so are the ets (Adebiyi,2006). 

 

From Equations 12 and 13, we can see that policy errors can be caused by exogenous y and policy disturbances. 

Let ∑u be the 2x2 variance-covariance matrix of uit and ∑e that of eit. Then ∑e = B∑u B1. To determine the 

impact of policy on output, we need tolook at the effect of uzt but unless b21 =0, ezt is not equal to uzt and 

therefore does notprovide a measure of the policy shock. If we estimate our VAR in Equations 6 and 7 as itis, B 

and ∑u will not be identified without further restrictions since estimation of thereduced form in Equations 9 and 

10 will yield less parameters than the structural form inEquations 1and 2. One of the most common restrictions 

is to assume that the structural shocks are uncorrelated so that the off diagonal elements in the covariance matrix 

are zero (Simatele, 2003). Two results obtained from VARs that are useful for analyzing transmission 

mechanisms are impulse response functions and forecast error variance decompositions. The impulse responses 

tell us how growth rate of gross domestic product responds to shocks in real educational expenditure and other 

policy variables, while the variance decompositions show the magnitude of the variations in growth rate in real 

GDP due to real capital educational expenditure and other policy variables. If we assume a stable system (like 

Simatele, 2003), we can iterate Equation 5 backwards and let n approach infinity and solve to obtain: 

Zt= λ + A1
i

 t-1   

Where the λs are the means of yt and zt and use Equation 8 to get 

=  +      ……………… (15) 



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We define the 2x2 matrix as F (i) with elements Fjk(i) such thatF(i) = and we write in 

moving average form as = + or in a more compact formZt=   

+ F(i)ut-i                …..(16) 

Fjk(i) are the impulse response functions. As we vary (i), we get a function describing theresponse of variable j 

to an impulse in variable k (Simatele, 2003).To derive the forecast error variance decompositions, we use 

Equation 12 to make a forecast of zt+1. The one-step-ahead forecast error is Fut+1and in general the 

n-periodforecast error Zt+n- EtZt+nis:    Zt+n- EtZt+n= F(i)ut-1      ..……….(17) 

and the mean square error (MSE)(Zt+n- EtZt+n)
2=

z F(i)            ………………(18) 

where   is the variance of Zt+n. 

To show that the decomposition more explicitly, let us narrow down on yt, (yt+n- Etyt+n)
2=   F(i)2. 

The share of   due to uyt and uzt are:   
y[F11(0)2 + F11(1)2 + ….+ F11(N-1)2]       ….……..(19) 

 (n)
2
 

 

 


z[F11(0)

2 
+ F11(1)

2
 + ….+ F11(N-1)

2
]        ……….(20) 

 (n)
2   

 

 

Since the variance decomposition tells us the share of the total variance attributed to agiven structural shocks, 

for an exogenous sequence y, uzt will not explain any of the forecast error variance of yt. 

 

Granger causality tests are conducted to determine whether the current and lagged values of one variable affect 

another. One implication of Granger representation theorem is that if two variables, say Xt and Yt are 

co-integrated and each is individually 1(1), then either Xt must Granger-cause Ytor Yt must Granger-cause Xt. 

This causality of co-integrated variables is captured in Vector Error Correction model (VEC).  

However, in order to avoid spurious regression results, stationarity of variables and cointegration among them 

are tested prior to estimation of VAR models and Granger causality regressions. The Augmented Dickey-Fuller 

(ADF) test for order of integration was adopted. The ADF test relies on rejecting a null hypothesis of unit root in 

favour of the alternative hypothesis of stationarity. The general form of the ADF is estimated by the following 

regression: 

∆yt = a0 + a1yt-1 + a∆yi+ et       ……………(21) 



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∆yt = a0 + a1yt-1 + a∆yi+ δt + et      ……………(22) 

Where: yt = time series, it is a linear time trend; Δ = first difference operator; a0 = constant; n = optimum 

number of lags in dependent variable; et = random error term. 

5. Empirical Result and Discussion 

Tables 5a and 5b show unit root tests for the variables in levels and in differences. Variables are expressed in 

logarithms form. According to the tests, time series are integrated processes of first order, I(1). The cointegration 

relationship between variables was also established using two likelihood ratio tests, a trace test and maximum 

eigenvalue test. The result of the cointegration test is reported in Table 6. Trace test indicates 4 cointegrating 

equation(s) at the 5% level and 3 cointegrating equation(s) at the 1% level. On the other hand, Max-eigen value 

test indicates 2 cointegrating equation(s) at the 5% level and 1 cointegrating equation(s) at the 1% level. Since 

there is growing evidence in favour of the Trace Statistics compared to the maximum Eigen value statistics 

(Kasa, 1992) as such the trace test result is accepted. The evidence of cointegration among the variables, 

indicate that there is a long-run relationship among the variables. Since the variables are cointegrated the 

equations of the VARs also include lagged values of the variables to capture their long-run relationships. 

Table 7 shows the estimate of an unrestricted VAR. The VAR estimates do not present the p-values for testing 

the corresponding parameters. However, based on each value of the t-statistics, it is easy to conclude whether or 

not a lagged variable has a significant adjusted effect on the corresponding dependent variable, by using a 

critical point of |t0| >2 or 1.96. Corresponding to the exogenous variable TERR(-1) H0: is accepted based on the 

t-statistic of  2.78723. Hence, it has a significant adjusted effect on TERR. In order words, one year previous 

terrorism has a positive significant effect on current year terrorism. This is applicable to LOG(INFL(-1)) and 

LOG(INFL); LOG(GDPC(-1))  and LOG(GDPC). On the other hand, LOG(INFL(-2)) has a negative 

significant effect on LOG(GOVX) and LOG(INTR). Similarly, LOG(GOVX(-1)) has a negative significant 

effect on LOG(INFL) but LOG(GOVX(-2)) has a positive significant effect on LOG(INFL) rather. 

LOG(INTR(-2)) showed also a positively significant relationship with LOG(INFL). A closer examination of the 

VAR results in Table 7, POLX(-1) depicted a positive effect on LOG(GDPC) and negative effects on 

LOG(GOVX) and LOG(OPEN). LOG(OPEN(-1)) showed an inverse effect on LOG(GOVX) and positive effect 

on LOG(OPEN). Still from the results, LOG(OPEN(-2)) showed a positive significant effect on  POLX. The 

remaining endogenous variables in Table 7showed insignificant effects. 

In analyzing the appropriateness of the estimated VAR in Table 7, Figure 1 reports inverse roots of the 

characteristic AR polinominal. VAR model is stationary if all roots have absolute value less than one and lie 

inside the unit circle.  As shown on the graph, all  roots are lying inside the unit circle, so this suggests that 

the model is stable, e. g. the influence of the shock for some variables may decrease over time. Pairwise Granger 

causality tests was carried out to tests if the endogenous variable can be treated as exogenous. According to that 

test all variables in the VAR model may be treated as exogenous.The lag exclusion tests  suggests that jointly 

all two lags of some of the endogenous variables were not statistically significant.  

A major requirement in conducting Johansen (1995) co integration tests and estimation of a VAR system, either 

in its unrestricted or restricted Vector Error Correction (VEC) forms, is the choice of an optimal lag length. In 

this paper, this choice was made by examining the lag structure in an unrestricted VAR originally specified with 

three lags, using a combination of VAR lag order selection criteria. Table 8 presents the evidence based on the 

VAR Lag Order Selection Criteria, while Figure 2 presents the inverse roots of the AR characteristic polynomial 

associated with the lag orders specified by the selection criteria. As shown in Table 3, while the LR, FPE, SC 



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and HQ criteria suggests the use of one lag, the AIC criterion suggests that three lags should be accommodated 

in the VAR. The correct lag length will depend on the criteria or measure we use. This is typical of these tests 

and researchers often use the criterion most convenient for their needs.  The SC criterion is generally more 

conservative in terms of lag length than the AIC criterion.  Here in this paper a lag length of 3 is assumed for 

convenience. 

Figure 2 displays pairwise cross-correlograms for the estimated residuals using 12 lag intervals. The dotted lines 

in the plots of the autocorrelations are the approximate two standard error bounds computed as +2 /(√T). If the 

autocorrelation is within these bounds, it is not significantly different from zero. Note that Figure 2 presents 49 

correlograms, which show that five or six of the corresponding population autocorrelations (or autocorrelation 

parameters) are significant. For example, the first graph shows that one of the autocorrelations is outside the 

interval with two standard error bounds and the second graph shows that two of the autocorrelations are outside 

the interval. 

Table 9 reports the multivariate extensions of the Jarque-Bera residual normality test, which compares the third 

and fourth moments of the residuals to those from the normal distribution. Concerning factorization of the 

residuals that are orthogonal to each other, a Cholesky was chosen. This is the inverse of the lower triangular 

Cholesky factor of the residual covariance matrix. The resulting test statistics depend on the ordering of the 

variables in the VAR. The results show that Halve the components in Table 8 displaced negative skewness while 

the rest showed positive skewness. The skewness of a symmetric distribution, such as the normal distribution, is 

zero. Positive skewness means that the distribution has a long right tail and negative skewness implies that the 

distribution has a long left tail. The kurtosis of a normal distribution is 3.  The result shows that most of the 

components have kurtosis less than 3, that is, the distribution is flat (platykurtic) relative to the normal.Although 

a very few of the component have small probability values,generally, the Jarque-Bera statistic shows that most 

of the component are insignificant meaning that the hypothesis that residuals are normally distributed is 

accepted. 

An impulse response function traces the effect of a one-time shock to one of the innovations on current and 

future values of the endogenous variables as seen in Tabel 10 (see Appendix). A shock to the i-th variable not 

only directly affects the i-th variable but is also transmitted to all of the other endogenous variables through the 

dynamic (lag) structure of the VAR. Table 10 reveals that past terrorism shocks in the 10 year period has a 

positive relationship with current terrorism. LOG(INFL) shocks has a negative relationship with current 

Terrorism in the early five years, thereafter turns positive. On the other hand, LOG(GDPC) shocks showed a 

positive relationship with terrorism up to the third year. Beyond this period, a one standard shocks from 

LOG(GDPC) attracted significant negative response to terrorism. At first LOG(GOVX) displayed negative 

relationship till the fourth year with terrorism. Beyond the fourth period LOG(GOVX) showed positive 

significant relationship, thereafter, the relationship became insignificant. LOG(OPEN) shocks started with a 

negative significant relationship with terrorism. Along the line it produces a negative insignificance and later 

turned negatively significant in the fourth period.  It displayed positive significant relationship with terrorism 

in the fifth period but the relationship positively insignificant all through till the tenth period. An interesting 

observation in the result is that past LOG(INTR) shocks throughout the periods showed a positive significant 

relationship with terrorism. Although POLX started off in the first four years with a positive significant 

relationship with terrorism, thereafter, it turned negative.See Table 10(in Appendix) for more of the shocks and 

impulse response of other endogenous variables. 

While impulse response functions trace the effects of a shock to one endogenous variable on to the other 



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variables in the VAR, variance decomposition separates the variation in an endogenous variable into the 

component shocks to the VAR. Thus, the variance decomposition provides information about the relative 

importance of each random innovation in affecting the variables in the VAR. Tables 11 shows the results of the 

variance decomposition within a future 10-period horizon. The columns give the percentage of variance in the 

variables that are due to innovations associated with specified variables, with each row adding up to 100. 

TERR own innovation accounted from 100% to 70% of the variation in TERR within the ten year period.  For 

the  later part of the ten year period, especially the eighth period, its own innovation accounted for 71.8% 

variation in TERR, LOG(GDPC) accounts for as much as 17.2%, LOG(INTR) accounted for 7.3%, LOG(OPEN) 

accounted for 2.1%,  LOG(INFL) accounted for 1.4%, LOG(GOVX) accounted for  0.3% and  POLX  

accounted for 0.1% of variation in TERR in the same period. This dominance in variations was also exhibited by 

LOG(INFL), LOG(GDPC)  and LOG(GOVX)due to their own innovations. 

Other interesting features of the results in Table 11 are noted.For example, shocks to LOG(INTR) variable in the 

first year  accounted for 91.4% variation  in LOG(INTR) while 5.1%,  0.1%, 0.8% , 2.1%, 0.4%,  0.0% 

were accounted for by TERR, LOG(INFL), LOG(GDPC), LOG(GOVX), LOG(OPEN) and POLX respectively. 

From the fifth year upward, variation in LOG(INTR) is determined mostly by TERR. This result supports the 

fact that a unidirectional causality runs from TERR to LOG(INTR).Variations in LOG(OPEN) are largely due to 

its own innovations up to the seventh period, thereafter, TERR, LOG(INFL), LOG(GDPC), LOG(GOVX), 

LOG(INTR) and POLX accounted for most of the variation in  LOG(OPEN).Apart from its own innovation 

that accounts for over 53.8% of the variation in POLX in the first year, TERR, LOG(INFL), LOG(GDPC), 

LOG(GOVX), LOG(OPEN) and LOG(OPEN) respectively accounted for  22.8% , 0.0%, 0.1%, 20.1% , 0.1% 

and 3.2% of  variation in POLX in the same period. It is however worthy of note, that most (over 50%) 

variation in TERR from the second year upward were mostly due to variations in LOG(GOVX), LOG(GDPC), 

LOG(INFL) and TERR.  

The results of the Pairwise Granger causality tests alternated between bi-directional, no causality and 

uni-directional between the variables, depending on the lag length allowed. The outcome in respect of two-lag 

length is presented in Table 12 (see Appendix). It reveals that causality runs from LOG(INTR) to LOG(GOVX) 

and there is no evidence of bi-directional causality between these two variables. The probability values and 

F-statistics are given; the low probability values suggested that the nullhypothesis can be rejected. This result 

can be attributed to the fact that interest rate policy in Nigeria is perhaps one of the most controversial of all 

financial policies. The reason for this may not be farfetched because interest rate policy has direct bearing on 

many other economic variables which in turn influence government spending. Interest rates play a crucial role in 

the efficient allocation of resources aimed at facilitating growth and development of an economy and as a 

demand management technique for achieving both internal and external balance. 

Consequently, a unidirectional causality runs from TERR to LOG(GOVX). This is because fighting terrorismhas 

become one of the major concerns in Nigeria and the government isspending more on combating the scourge. 

Government spending has continued to rise due to the huge receipts from production and sales of crude oil, and 

the increased demand for public (utilities) goods like roads, communication, power, education and health. 

Besides, there is increasing need to provide both internal and external security for the people and the nation. The 

war against terror in Nigeria raised military expenditure to a staggering $2.327 billion(N372.3 billion) in 2012 

alone (Naij,2013), ranking Nigeria among countries at war in Africa. 

Causality results between POLX  and  LOG(GOVX) reveals that a bi-directional causality runs from POLX  

to  LOG(GOVX). This finding implies that macroeconomic policies ( government fiscal (expenditure and 



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11 
 

revenue) policies and the monetary policy (inflation management, interest rate policy and foreign exchange 

management) influences government expenditure.Generally, as observed by Sanusi (2002),macroeconomic 

policies in Nigeria have been inconsistent over the long-run as periods of internal and external imbalances were 

more pronounced than periods of strong underlying macroeconomic fundamentals. 

Also, a unidirectional causality was found running from LOG(INTR) to LOG(OPEN). This result aligns with De 

Fiore and Liu (2002) that showed the conditions under which inflation-targeting interest rate rules lead to 

equilibrium uniqueness in an open economy.In an open economy, an increase in the real interest rate is 

transmitted to aggregate demand through an inter-temporal substitution effect and also through terms of trade 

effect. The behaviour of interest rate is important for economic growth of Nigeria in view of the empirical nexus 

between interest rates and investment, and investment and growth. 

Additionally, unidirectional causality was found running from TERR to LOG(INTR) implying that terror 

variable exerts a positive and significant impact on macroeconomic variable like interest. This accords 

Cukierman (2004) that by raising the probability of death; an increase in terror reduces investment, production 

and consumption. In parallel the increase in death raises the interest rate and reduces total wealth. 

However,causality was also seen to runs from LOG(OPEN) to POLX. But interestingly there is was no causality 

found between TERR and  POLX. Also, Granger causality does not run either-way, from POLX to 

LOG(INTR), indicating non-existence causation. Generally, it could be noted that there is existence of dynamic 

relationship existing amongLOG(OPEN), POLX , LOG(INTR), LOG(GOVX). However, worthy of note is that 

Causality ran from TERR to LOG(GOVX) andLOG(INTR). 

Since the results in Table 6 (see Appendix)showed that the variables have a long run relationship, a long 

runstatic regression is then estimated by applying error correction. The results of unit root test shows that the 

error correction term (ECM) is stationary at level 1(0)). Table 13 contains the multivariate regression results of 

the overparameterised model. The results indicate that DLOG(INFL) is statistically insignificant. This 

necessitates the dropping of the variable from the model and hence the results contained in table 14(see 

Appendix), which is the focus of the discussion. The improved results as contained in Table 14 show that with 

the exception of the constant term, all the coefficients are statistically significant. A closer look at the result 

reveals that LOG(GDPC), LOG(OPEN), LOG(INFL), DLOG(GOVX) and DLOG(INTR) went contrary to the 

theoretical expectation. On the other hand, LOG(GOVX), LOG(INTR), POLX, DLOG(GDPC) and 

DLOG(OPEN) were in line with the apriori expectation. The result shows that 1 percent increase in 

LOG(GOVX), reduces the occurrence of terrorism (TER) by 0.18 percent and it is significant at 1% level. On 

the other hand, LOG(INTR) shows a positive relationship with terrorism. Terrorism rises by 0.616936 percent 

given a 1 percent increase in LOG(INTR). Likewise, a 1 percent increase in POLX increase the occurrence of 

terrorism by 0.340240 percent and statistically significant at 1 percent level.  The result from Table 14 also 

revealed an inverse relationship between DLOG(GDPC) and terrorism. Precisely, a 1 percent increase in 

DLOG(GDPC) is associated with a 0.142587 percent decline in terrorism. Similarly, a 1 percent increase in 

DLOG(OPEN) leads to a 0.075312 reduction in the occurrence of terrorism. 

The R2 0.937818 (93.78%) implies that 93.78 percent of total variation in terrorism  explained by the 

regression equation. Coincidentally, the goodness of fit of the regression remained high after adjusting for the 

degrees of freedom as indicated by the adjusted R2 (R 2=0.912088 or 91.21%). The F-statistic 36.44775, which 

is a measure of the joint significance of the explanatory variables, is found to be statistically significant at 1 

percent as indicated by the corresponding probability value (0.000000). The Durbin-Watson statistic of 1.80 

seems to suggest lesser degree of autocorrelation. The results of the error correction models as contained in 



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Tables 14 provides evidence for equilibrium to be restored after short-run disturbances as indicated by the 

statistically significant coefficients of the error correction terms (ECM). But the error correction term happened 

not to be correctly signed.  

6. Conclusion  

This paper is a cointegration and causality analysis of macroeconomic factors and terrorism in Nigeria. The 

econometric investigation was based on a cointegration approach and the  Granger Causality test, using time 

series data from 1970 to 2016. The analysis starts with examining stochastic characteristics of each time series 

by testing their stationarity using Augmented Dickey Fuller (ADF) test. Then, the effects of stochastic shocks to 

one of the innovations on current and future values of the endogenous variables are explored, using VAR models 

and impulse response analysis. Since the results of Johansen cointegration revealed that there is a long-run 

relationship among the stationary variables, a long run static regression was then estimated by applying error 

correction. 

The result reveals that LOG(GDPC), LOG(OPEN), LOG(INFL), DLOG(GOVX) and DLOG(INTR) went 

contrary to the theoretical expectation. On the other hand, LOG(GOVX), LOG(INTR), POLX, DLOG(GDPC) 

and DLOG(OPEN) were in line with the apriori expectation. This implies that LOG(GOVX) has an inverse 

relationship with terrorism. On the other hand, LOG(INTR) shows a positive relationship with terrorism. So also 

is LOG(INTR). Likewise, a 1 percent increase in POLX increase the occurrence of terrorism by 0.340240 

percent and statistically significant at 1 percent level.  The result also revealed an inverse relationship between 

DLOG(GDPC) and terrorism. Similarly, a 1 percent increase in DLOG(OPEN) leads to a 0.075312 reduction in 

the occurrence of terrorism. 

The main limitation of the VAR modeling approach used in this paper is its consumption of degrees of freedom in 

the model estimation. A future extension of the study could be to use the Bayesian VAR (BVAR) approach in 

order to reduce the number of parameters that need to be estimated. However, from a policy perspective, the 

results suggest that government expenditure should be properly managed and directed at more productive sectors 

rather than non-productive ventures. This can bring about employment and foster economic growth which will in 

turn reduce poverty and lead to reduction of the occurrence of terrorism. In addition, a mechanism should be 

provided for Small and Medium Enterprises to have access to loans with long payback period. In this vein, policy 

to promote access to microfinance services can be promoted by making access to microcredit less difficult for the 

poor people by reducing the interest rate charged.  

Also, trade openness rate should be all time kept at peak benchmark by adopting tight trade openness in order to 

ensure economic growth via fiscal sustainability. In addition, strategic macroeconomic policies should be 

instituted in order to encourage domestic private investment to enhance the growth of the economy. Nigerian 

political system has to be stabilized and the government should step up its intelligence gathering capacity as well 

as training security agents to forcefully combat terrorist group. 

References 

Abimbola, J. O., and Adesote, S. A. (2012)Domestic Terrorism and Boko Haram Insurgency in Nigeria, Issues 

and Trends: A Historical Discourse”. Journal of Arts and Contemporary 

Society,4(1),11-29. Retrieved from http://www.cenresinpub.org/pub/Sept%20Edition%202012/JACS/P

age%2011-19_842_.pdf  

Adebiyi, M. A.(2006). Education-Economic Growth Paradox in Nigeria: An Autoregressive Model Retrieved 

from http://ssrn.com/abstract=876244. 

Adebiyi, M.A., and Oladele, O. (2005). Public Education Expenditures and Defence Spending in Nigeria: An 

http://ssrn.com/abstract=876244


www.cribfb.com/journal/index.php/ijfb        Indian Journal of Finance and Banking Vol. 2, No. 1; 2018 

13 
 

Empirical Investigation, http://www.saga.cornell.edu/saga/educonf/ade.Assessed August 12, 2011.  

Adrangi, B., and Allender, M. (1998).Budget Deficits and Stock Prices: International evidence. Journal of 

Economics,22 (2-3),57-66.  

Ajayi, A.I. (2012).Boko Haram and Terrorism in Nigeria: Exploratory and Explanatory Notes.Global Advanced 

Research Journal of History, Political Science and International Relations, 1(5),103-107. 

Alesina, A., Ozler,S., Roubini, N., and Swagel, P. (1996).Political Instability and Economic Growth.Journal of 

Economic Growth, 1(1),189-211. 

Berrebi, C. (2003). Evidence About the Link Between Education, Poverty and Terrorism Among Palestinians, 

Working Papers 856, Princeton University, Department of Economics, Industrial Relations Section. 

Blomberg, S. B., and Hess, G. D. (2008).From (no) Butter to Guns? Understanding the Economic Role in 

Transnational Terrorism. Terrorism, Economic Development, and Political Openness.Ed. Philip Keefer 

and Norman Loayza. New York: Cambridge UP, pp, 83-115. 

Blomberg, B. S., Hess, G. D., and  Weerapana, A. (2004).Economic Conditions and Terrorism.European 

Journal of Political Economy, 20(2),463-478. 

CBN Annual Reports and Statement of Accounts (various years). 

CBN Statistical Bulletin vol. 17, December 2006. 

http://www.cenbank.org/OUT/PUBLICATIONS/STATBULLETIN/RD/2008/STABULL-2006.PDF 

Chinwokwu, E. C. (2012). History and Dynamics of Terrorism in Nigeria: Socio-political 

Dimension.International Journal of Innovative Research and Development,1(11), 419- 446 

Cukierman, A. (2004). Comment on: Macroeconomic Consequences of Terror: Theory and the case of Israel. 

Journal of Monetary Economics, (51), 1003–1006. 

De Fiore, F., and Liu, Z. (2002),Openness and equilibrium determinacy under interest rate rules. European 

Central Bank, Working Paper Series, No. 173.Retrieved 

fromhttp://www.ecb.europa.eu/pub/pdf/scpwps/ecbwp173.pdf 

Economic Online (2017). Economic 

Development.http://www.economicsonline.co.uk/Global_economics/Economic_development.html 

Ehrlich, P.R., and Jianguo, L. (2002).Some Roots of Terrorism.Population and Environment , 24(2), 183-92. 

EIU. (2008). Economist Intelligence Unit  Country Profile on 

Nigeria,.http://www.refworld.org/pdfid/493e3bb12.pdf 

Elbakidze, L., and Jin, Y. (2007). Socio-economic Antecedents of Transnational Terrorism: Exploring the 

Correlation.Available online: 

http://agecon2.tamu.edu/people/faculty/mccarl-bruce/689homeland/documents/terrorpaper.pdf 

Forest, J.J.F. (2012). Confronting the Terrorism of Boko Haram in Nigeria. JSOU Report 12-5. Available online: 

http://cco.dodlive.mil/files/2012/09/Boko_Haram_JSOU-Report-2012.pdf. Accessed on 2nd  , May, 

2016. 

Gambrell, J. (2011). Muslim sect‟s attacks kill 39 in Nigeria: Catholic Church bombed just after Christmas 

Mass. The Columbus Dispatch.Available 

online:http://www.dispatch.com/content/stories/national_world/2011/12/26/muslim-sects-attacks-kill-3

9-in-nigeria.html.Accessed on 2nd  , May, 2016. 

Goldman, E. (1978).The Psychology of Political Violence. In the Terrorism Reader, edited by Walter Laqueur, 

193-98. New York: New American Library. 

Gurr, T. R.(1970).Why Men Rebel. Princeton, NJ: Center of International Studies, Princeton UP. 

http://www.refworld.org/pdfid/493e3bb12.pdf
http://agecon2.tamu.edu/people/faculty/mccarl-bruce/689homeland/documents/terrorpaper.pdf
http://cco.dodlive.mil/files/2012/09/Boko_Haram_JSOU-Report-2012.pdf


www.cribfb.com/journal/index.php/ijfb        Indian Journal of Finance and Banking Vol. 2, No. 1; 2018 

14 
 

Hoffman, B. (2006). Inside Terrorism. Third Ed. New York: Columbia University Press. 

IndianVision (2012). Indian killed in a Nigerian Terror Attack.Available online: 

http://www.indiavision.com/news/article/international/271520/.  Accessed on 2nd  , May, 2016. 

International Crisis Group reports (ICG)(2006). Nigeria: Want in the Midst of Plenty. Africa Report No 

113, Available online:www.crisisgroup.org.Accessed on 2nd  , May, 2016 

Johansen, S. (1995).   Likelihood Based Inferences in Cointegrated Vector Autoregressive Models. Oxford 

University Press. Oxford. 

Kasa, K. (1992). Common Stochastic Trends in International Markets.Journal of Monetary Economics., 29(1), 

95 – 124. 

Krueger, A.B., and Maleckova, J. (2003). Education, Poverty, Political Violence and Terrorism: Is there a Causal 

Connection? Journal of Economic Perspectives, 17(4), 119-144. 

Li, Q. (2005). Does Democracy Promote or Reduce Transnational Terrorist Incidents?Journal of Conflict 

Resolution, 49(2), 278-297. 

Li, Q., and Schaub, D. (2004). Economic Globalization and Transnational Terrorism: A Pooled Time-Series 

Analysis, Journal of Conflict Resolution, 48(2), 230-258. 

Manson, N., Oguijiuba,O., and Adeola, A. (2004).The Impacts of Trade Liberalization on Poverty in Nigeria: 

Microsimulations in a CGE model. A Proposal for the Poverty and Economic Policy(PEP) Research 

Network. 

Naij(2013).Nigeria's Anti-Terror War Gulped N372 Billion in 2012 – Report. Available online: 

http://news.naij.com/34638.html. Accessed on 2nd  , May, 2016. 

National Bureau of Statistics (NBS) (2005). Nigerian Statistics.Available online: 

http://www.nigerianstat.gov.ng/pages/download/71. Accessed on 23rd , March, 2016.  

Nigerian Finder (n.d.).  Economic Development in Nigeria. 

http://nigerianfinder.com/economic-development-in-nigeria/ 

Nigerians Report (2011). Islamic Terrorists Attacking Christians on Christmas Day in Nigeria.Available online: 

http://www.nigeriansreport.com/2011/12/islamic-terrorists-attacking-christians.html#sthash.28cm4rWa.

dpuf. Accessed on 4th , April, 2016.  

Okonkwo, R. O., and Enem, F. O. Combating crime and terrorism using data mining techniques.Nigeria 

Computer Society (NCS): 10th International Conference – july 25-29, 2011. Available online: 

http://www.ncs.org.ng/wp-content/uploads/2011/08/ITePED2011-Paper10.pdf. Accessed on 23rd , 

March, 2016.  

Omojimite, B. U. (2012).Public Education and Defence Spending in Nigeria: Implications for Economic Growth. 

Journal of Educational and Social Research, 2 (1),59-72 

Onuoha, F. C. (2013).   Jama'atuAnsarulMusilimina Fi Biladis Sudan: Nigeria‟s Evolving Terrorist Group. 

Report Aljazeeral Centre for Studies.Available online: 

http://studies.aljazeera.net/ResourceGallery/media/Documents/2013/3/14/2013314103734423734Niger

ias%20Evolving%20Terrorist%20Group.pdf. Accessed on 23rd , March, 2016. 

Richardson,C. (2011).Relative Deprivation Theory in Terrorism: A Study of Higher Education and 

Unemployment as Predictors of 

Terrorism.politics.as.nyu.edu/docs/IO/4600/Clare_Richardson_terrorism.pdf 

Sageman, M. (2008).Leaderless Jihad: Terror Networks in the Twenty-First Century. Philadelphia: University of 

Pennsylvania Press. 

http://www.indiavision.com/news/article/international/271520/
http://www.crisisgroup.org/
http://news.naij.com/34638.html
http://www.nigerianstat.gov.ng/pages/download/71
http://www.nigeriansreport.com/2011/12/islamic-terrorists-attacking-christians.html#sthash.28cm4rWa.dpuf
http://www.nigeriansreport.com/2011/12/islamic-terrorists-attacking-christians.html#sthash.28cm4rWa.dpuf
http://www.ncs.org.ng/wp-content/uploads/2011/08/ITePED2011-Paper10.pdf
http://studies.aljazeera.net/ResourceGallery/media/Documents/2013/3/14/2013314103734423734Nigerias%20Evolving%20Terrorist%20Group.pdf
http://studies.aljazeera.net/ResourceGallery/media/Documents/2013/3/14/2013314103734423734Nigerias%20Evolving%20Terrorist%20Group.pdf


www.cribfb.com/journal/index.php/ijfb        Indian Journal of Finance and Banking Vol. 2, No. 1; 2018 

15 
 

Sanusi, J. O. (2002).Central Bank and the Macroeconomic Environment in Nigeria. Being a lecture delivered to 

participants of the senior executive course no. 24 of the National Institute for Policy and Strategic 

Studies (NIPSS),  Kuru.Available online: 

http://www.cenbank.org/OUT/SPEECHES/2002/GOVADD-19AAUG.PDF. Accessed on 3rd , July, 

2016. 

Sen, A. (2001). Development as Freedom (2nd ed.). Oxford New York: Oxford University Press. 

ISBN 9780192893307. 

Simatele, M.C.H. (2003). Financial Sector Reform and Monetary Policy in Zambia. Ph.D Dissertation. Economic 

Studies, Department of  Economics, School of Economics and Commercial Law, Gotebora University. 

Tessler, M., and Michael, D. H. R. (2007).What Leads Some Ordinary Arab Men and Women to Approve of 

Terrorist Attacks Against the United States?Journal of Conflict Resolution, 51(2), 305-328. 

Trading Economics (2013).Nigeria GDP Growth Rate. 

http://www.tradingeconomics.com/nigeria/gdp-growth 

World Bank(1996).  Nigeria:Poverty in the Midst of Plenty: The Challenge of Growth with 

Inclusion.Available online: http://web.worldbank.org/WBSITE/EXTERNAL/TOPICS/EXTPOVERTY/

EXTPA/0,,contentMDK:20204610~menuPK:435735~pagePK:148956~piPK:216618~theSitePK:43036

7~isCURL:Y,00.html. Accessed on 23rd , March, 2016. 

Appendix 

Table 1: Categories of Militia Groups in the Niger Delta. 

Private Militia Ethnic Militia Pan-Ethnic Militia 

Niger Delta People Volunteer 

Force (NDPVF) 

The MeinbutusArugbo Freedom 

Fighter 

Movement for the Emancipation of 

the Niger Delta (MEND) 

Adaka Marines Iduwini Volunteer Force (IVF) The Coalition for Militant Action 

in the Niger Delta (COMA) 

Martyrs Brigade Egbesu Boys of Africa The Niger Delta People Salvation 

Front 

Niger Delta Volunteers - - 

Niger Delta Militant Force Squad 

(NDMFS) 

- - 

Niger Delta Coastal Guerillas 

(NDCGS) 

- - 

Source: Forest (2012) 

Table 2: Attacks Blamed on the JAMBS 

Date location Target(s) Description remarks  

26 Nov. 2012 Garki, Abuja Headquarters of 

the Special 

Anti-Robbery 
Squad (SARS) 

Attack and 

freeing of some 

inmates in the 
detention 

facility of the 

SARS 

headquarters 

JAMB claimed 

that the attack 

was in 
compliance with 

a Quranic 

injunction that 

urged believers 

to fight for the 

oppressed  

andthe feeble. It 

promised similar 

attacks against 

detention 

centres across 

the country 

 

https://en.wikipedia.org/wiki/International_Standard_Book_Number
https://en.wikipedia.org/wiki/Special:BookSources/9780192893307
http://www.tradingeconomics.com/nigeria/gdp-growth
http://web.worldbank.org/WBSITE/EXTERNAL/TOPICS/EXTPOVERTY/EXTPA/0,,contentMDK:20204610~menuPK:435735~pagePK:148956~piPK:216618~theSitePK:430367~isCURL:Y,00.html
http://web.worldbank.org/WBSITE/EXTERNAL/TOPICS/EXTPOVERTY/EXTPA/0,,contentMDK:20204610~menuPK:435735~pagePK:148956~piPK:216618~theSitePK:430367~isCURL:Y,00.html
http://web.worldbank.org/WBSITE/EXTERNAL/TOPICS/EXTPOVERTY/EXTPA/0,,contentMDK:20204610~menuPK:435735~pagePK:148956~piPK:216618~theSitePK:430367~isCURL:Y,00.html
http://web.worldbank.org/WBSITE/EXTERNAL/TOPICS/EXTPOVERTY/EXTPA/0,,contentMDK:20204610~menuPK:435735~pagePK:148956~piPK:216618~theSitePK:430367~isCURL:Y,00.html


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19 Dec. 2012 Katsina State Francis Colump Kidnapping of 

Francis, a 

French citizen 

working for the 

French company 

Vergnet 

JAMBS claimed 

that the reason 

for kidnapping 

Colump is the 

stance of the 

French 

government and 

the French 

people on Islam, 
specifically 

citing France‟s 

major role in the 

(planned) 

intervention in 

northern Mali 

 

19 Jan. 2013 Okene, Kogi 

State 

Convoy of 

Mali-bound 

Nigerian 

soldiers 

Ambushing of a 

truck conveying 

Mali-bound 

Nigerian 

soldiers, 

resulting in the 

death of two 
soldiers and 

injuring of five 

others 

JAMBS claimed 

it attacked the 

soldiers because 

of Nigeria‟s 

contribution of 

troops to Mali  

 

17 Feb. 2013 Jamaare 

(Bauchi state) 

Seven 

expatriates 

working with a 

Lebanese 

construction 

company, 

Setraco Nig. Ltd 

Those abducted 

were four 

Lebanese, one 

Briton, a Greek 

citizen and an 

Italian 

JAMBS claimed 

responsibility 

for the 

kidnapping, 

citing „the 

transgressions 

and atrocities 

done to the 

religion of Allah 

by the European 
countries 

 

Source: Onuoha(2013) 

Table 3: Cases of Domestic Terrorism arising from Bomb Explosions in Nigeria 1986-2012 

Date Place State Terrorist 

Group 

Casualty 

19/10/1986 Parcel bomb, Lagos Lagos Nil 1 

31/5/1995 Venue of launching of 

family support Ilorin 

Kwara Nil No record 

18/1/1996 Durbar Hotel Kaduna Kaduna Nil 1 

19/1/1996 Aminu Kano Airport, 

Kano 

Kano Nil No record 

11/4/1996 Ikeja cantonment Lagos Nil No record 

25/4/1996 Airforce base Lagos Nil No record 

14/11/1996 MMIA Lagos Nil 2 

16/12/1996 Col. Marwa convey Lagos Nil No record 

18/12/1996 Lagos state task force on 

environment bus in 

Lagos 

Lagos Nil No record 

7/1/1997 Military bus at 

Ojuelegba, Lagos 

Lagos Nil No record 



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12/2/1997 Military vehicle Fakka 

D608 at Ikorodu road, 

Lagos 

Lagos Nil No record 

7/5/1997 Nigerian army 25 seater 

bus at Yaba, Lagos 

Lagos Nil No record 

12/5/1997 Eleiyele, Ibadan Oyo Nil No record 

16/5/1997 Onitsha Anambra Nil 5 

6/8/1997 Port Harcourt Rivers Nil 1 

2/9/1997 Col. InuaBawa convey, 

Akure 

Ekiti Nil No record 

18/12/1997 Gen. OladipoDiya at 

Abuja airport 

Abuja Nil 1 

22/4/1998 Evan square Lagos Nil 3 

23/4/1998 Ile-Ife Osun Nil 5 

27/1/2002 Lagos Lagos Nil 1000 

31/7/2002 Port Harcourt Rivers Nil 1 

25/11/2006 25/11/2006 PDP 

Secrtariat, Yenagoa 

Bayelsa Nil 1 

5/12/2006 Goodluck Jonathan 

campaign office 

Bayelsa Nil No record 

23/12/2006 Port Harcourt Rivers Nil No record 

12/7/2009 Atlas Cove, Lagos Lagos MEND 5 

2/5/2010 Yenagoa Bayelsa MEND No record 

1/10/2010 Eagle square Abuja MEND 8 

12/11/2010 Alaibe house Opokuma Bayelsa MEND 1 

24/12/2010 Jos Plateau Boko haram 38 

27/12/2010 BarkinLadi Plateau Boko haram No record 

29/12/2010 Yenagoa Bayelsa MEND 1 

31/12/2010 Mugadishu barracks Abuja Boko haram 32 

2/2/2011 Aba Abia Nil 2 

3/3/2011 Suleja Niger Boko haram 16 

16/3/2011 Yenagoa Bayelsa Nil No record 

1/4/2011 Butshen-tanshi Bauchi Boko haram No record 

6/4/2011 kaduna kaduna Boko haram 4 

7/4/2011 UnguwarDoki, 

Maiduguri 

Borno Boko haram 10 

8/4/2011 INEC office suleja Niger Boko haram 14 

8/4/2011 Kaduna Kaduna Boko haram 1 

9/4/2011 Unguwandoki polling 

station 

Kaduna Boko haram 5 

9/4/2011 INEC collating centre Borno Boko haram No record 

22/4/2011 Kaduna Kaduna Boko haram 3 

14/5/2011 London chiki Maiduguri Borno Boko haram 2 

19/5/2011 Lagos road Maiduguri Borno Boko haram No record 

28/5/2011 Lagos park 

Zuba/Mammy market 

Abuja & 

Bauchi 

Boko haram 18 

29/5/2011 Zuba near Abuja Abuja Boko haram 8 

3/6/2011 Maiduguri Borno Boko haram No record 

7/6/2011 Beside St. Patrick church 

Maiduguri 

Borno Boko haram 10 

10/6/2011 Kaduna Kaduna Boko haram No record 

16/6/2011 Police force headquarters Abuja Boko haram 3 

16/6/2011 Damboa Maiduguri Borno Boko haram 3 

26/6/2011 Beer garden Maiduguri Borno Boko haram 25 



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3/7/2011 Beer garden Maiduguri Borno Boko haram 20 

10/7/2011 All christian fellowship 

church Suleja 

Niger Boko haram No record 

26/8/2011 United Nations Office Abuja Boko haram 23 

6/9/2011 Baga road & Ward 

Maiduguri 

Borno Boko haram No record 

17/12/2011 Shuwai Area of 

Maiduguri 

Borno Boko haram 3 

22/12/2011 Pompomari near Emir of 

DamaturuPalaca 

Yobe Boko haram 2 

22/12/2011 Timber shed along Bada 

road Maiduguri 

Borno Boko haram No record 

25/12/2011 St. Theresa Catholic 

Church, Madalla near 

Suleja 

Niger Boko haram 43 

25/12/2011 Near Mountain of Fire 

Ministry, Jos 

Plateau Boko haram 12 

25/12/2011 SSS Office Damaturu Yobe Boko haram 4 

26/12/2011 Near Islamic School in 

Sapele 

Delta Nil No record 

28/12/2011 Near a Hotel in Gombe Gombe Boko haram No record 

6/1/ 2012 Attack on some 

Southerners in Mubi 

Adamawa Boko haram 13 

 21/1/ 2012 Multiple bomb blasts 

rocked Kano city 

Kano Boko haram Over 185 

people killed 

29/1/ 2012 Bombing of a Police 

Station at Naibawa area of 

Yakatabo 

Kano Boko haram No record 

 8/2/ 2012 Bomb blast rocked Army 

Headquarters  

Kaduna Boko haram No record 

15/2/ 2012 Attack on KotonKarfe 

Prison which 119 
prisoners 

were freed 

Kogi  Boko haram 1 Warder 

killed 

19/2/ 2012 Bomb blast near Christ 

Embassy Church, in 

Suleija 

Niger  Boko haram 5 people 

injured 

 26/2/ 2012 Bombing of Church of 

Christ in Nigeria, Jos 

Plateau Boko haram 2 people 

killed and 38 

injured 

11/2/ 2012 Bombing of St. Finbarr‟s 

Catholic Church Rayfield, 

Jos 

Plateau Boko haram 11 people 

killed and 

many injured 

29/2/ 2012 Attack on Bayero 

University 

Kano Boko haram 16 people 

killed and 

many injured 

30/2/ 2012 Bomb explosion in 

Jalingo 

Taraba Boko haram 11 people 

killed and 

several others 

wounded 

Source: Chinwokwu (2012), Ajayi (2012) 

Table 5a: Unit Root Test Results: Levels 

Variable INFL GDPC GOVX OPEN INTR POLX TERR ECM 

Unit root ADF ADF ADF ADF ADF ADF ADF ADF 



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Trend, constant -4.001558* 

 

-2.014036 

 

-3.089160 

 

-2.237038 

 

-1.104306 

 

-3.075318 

 

-1.645215 

 

-4.666189* 

 

Constant -4.131647 

* 

 

-1.993456 

 

-1.495412 

 

-1.479380 

 

-1.506191 

 

-2.282445 

 

-1.840175 

 

-4.650278* 

 

Without trend, 

constant 

-0.747000 

 

0.194820 

 

 

4.693380 

-0.791717 

 

0.658856 

 

-1.380933 

 

0.000000 

 

-4.724776* 

 

Source: Authors‟ Computation from Computer Output. 

 Note:  * Null Hypothesis Rejection at 1%; ** * Null Hypothesis Rejection at 5%; and *** Null Hypothesis 

Rejection at 10% 

Table 5b: Unit Root Test results: First Difference 

Variable INFL GDPC GOVX OPEN INTR POLX TERR ECM 

Unit root ADF ADF ADF ADF ADF ADF ADF ADF 

Trend, 

constant 

-6.302564* 

 

 

-5.861418* 

 

-0.985064 

 

-7.243676* 

 

-10.30788* 

 

-7.597783* 

 

-6.492103* 

 

-10.28363* 

 

Constant -6.361749 

* 

 

-5.898550* 

 

-6.976280* 

 

-7.277774* 

 

-10.25756* 

 

-7.695598* 

 

-6.403124* 

 

-10.15246* 

 

Without 

trend, 

constant 

-6.446588 

 

-5.933521* 

 

-1.133636 

 

-6.969205* 

 

-10.20175* 

 

-7.745967* 

 

 -10.41274* 

 

Source: Authors‟ Computation from Computer Output. 

 Note:  * Null Hypothesis Rejection at 1%; ** * Null Hypothesis Rejection at 5%; and *** Null Hypothesis 

Rejection at 10% 

Table 6: Johansen Cointegration Test 

Unrestricted Cointegration Rank Test 

Hypothesized 

No. of CE(s) 

Eigenvalue Trace 

Statistic 

5 Percent 

Critical Value 

1 Percent 

Critical Value 

None **  0.829696  199.4871 124.24 133.57 

At most 1 **  0.666851  128.6802  94.15 103.18 

At most 2 **  0.542003  84.71358  68.52  76.07 

At most 3 *  0.485237  53.47791  47.21  54.46 

At most 4  0.266711  26.91597  29.68  35.65 

At most 5  0.246280  14.50736  15.41  20.04 

At most 6  0.076837  3.197961   3.76   6.65 

     

 *(**) denotes rejection of the hypothesis at the 5%(1%) level 

 Trace test indicates 4 cointegrating equation(s) at the 5% level 

 Trace test indicates 3 cointegrating equation(s) at the 1% level 

     

Hypothesized 

No. of CE(s) 

Eigenvalue Max-Eigen 

Statistic 

5 Percent 

Critical Value 

1 Percent 

Critical Value 



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None **  0.829696  70.80688  45.28  51.57 

At most 1 *  0.666851  43.96663  39.37  45.10 

At most 2  0.542003  31.23567  33.46  38.77 

At most 3  0.485237  26.56194  27.07  32.24 

At most 4  0.266711  12.40861  20.97  25.52 

At most 5  0.246280  11.30940  14.07  18.63 

At most 6  0.076837  3.197961   3.76   6.65 

     

 *(**) denotes rejection of the hypothesis at the 5%(1%) level 

 Max-eigenvalue test indicates 2 cointegrating equation(s) at the 5% level 

 Max-eigenvalue test indicates 1 cointegrating equation(s) at the 1% level 

Source: Researchers‟ computation, 2013, adapted from regression result using E-view 4.1 

Table 7: Unrestricted VAR 

Standard errors in ( ) & t-statistics in [ ] 

 TERR LOG(INF

L) 

LOG(GDP

C) 

LOG(GOV

X) 

LOG(OPE

N) 

LOG(INT

R) 

POLX 

TERR(-1)  0.648374 -0.876486 -0.083424  0.216857  1.441724  0.234786  0.173192 

  (0.23262)  (0.87056)  (0.44221)  (0.19225)  (1.11221)  (0.26842)  (0.42412) 

 [ 2.78723] [-1.00681] [-0.18865] [ 1.12801] [ 1.29626] [ 0.87468] [ 0.40836] 

        

TERR(-2) -0.085913  0.233596 -0.006201  0.001090 -1.687271  0.394876 -0.595555 

  (0.27010)  (1.01080)  (0.51345)  (0.22322)  (1.29139)  (0.31167)  (0.49245) 

 [-0.31808] [ 0.23110] [-0.01208] [ 0.00488] [-1.30655] [ 1.26698] [-1.20938] 

        

LOG(INFL(-1)) -0.024428  0.537972  0.021890  0.044991  0.033758  0.076149 -0.098971 

  (0.04438)  (0.16607)  (0.08436)  (0.03667)  (0.21217)  (0.05121)  (0.08091) 

 [-0.55046] [ 3.23935] [ 0.25948] [ 1.22676] [ 0.15911] [ 1.48710] [-1.22325] 

        

LOG(INFL(-2)) -0.014808 -0.164937  0.099237 -0.075244  0.033552 -0.120743 -0.131330 

  (0.03966)  (0.14843)  (0.07540)  (0.03278)  (0.18964)  (0.04577)  (0.07231) 

 [-0.37334] [-1.11119] [ 1.31617] [-2.29549] [ 0.17693] [-2.63820] [-1.81611] 

        

LOG(GDPC(-1)

) 

 0.122650  0.018809  0.723194  0.108755 -0.275171  0.025742  0.322924 

  (0.09075)  (0.33963)  (0.17252)  (0.07500)  (0.43391)  (0.10472)  (0.16546) 

 [ 1.35147] [ 0.05538] [ 4.19197] [ 1.45004] [-0.63417] [ 0.24582] [ 1.95167] 

        

LOG(GDPC(-2)

) 

 0.074634  0.186357 -0.179784 -0.007099  0.589393  0.048637  0.038924 

  (0.08964)  (0.33547)  (0.17041)  (0.07408)  (0.42859)  (0.10344)  (0.16343) 



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 [ 0.83259] [ 0.55551] [-1.05503] [-0.09582] [ 1.37519] [ 0.47021] [ 0.23816] 

        

LOG(GOVX(-1

)) 

-0.082239 -2.578577  0.084675  0.709434  0.473362 -0.166581  0.196477 

  (0.26951)  (1.00859)  (0.51233)  (0.22273)  (1.28856)  (0.31098)  (0.49137) 

 [-0.30515] [-2.55662] [ 0.16528] [ 3.18518] [ 0.36736] [-0.53565] [ 0.39986] 

        

LOG(GOVX(-2

)) 

-0.018075  2.458224  0.085219  0.328262 -0.032975  0.150961 -0.327225 

  (0.29360)  (1.09875)  (0.55812)  (0.24264)  (1.40375)  (0.33878)  (0.53529) 

 [-0.06156] [ 2.23730] [ 0.15269] [ 1.35288] [-0.02349] [ 0.44560] [-0.61130] 

        

LOG(OPEN(-1)

) 

 0.030028  0.260626 -0.165487 -0.073313  0.495004 -0.042773 -0.029550 

  (0.04005)  (0.14990)  (0.07614)  (0.03310)  (0.19151)  (0.04622)  (0.07303) 

 [ 0.74967] [ 1.73870] [-2.17339] [-2.21475] [ 2.58478] [-0.92545] [-0.40464] 

        

LOG(OPEN(-2)

) 

 0.030169 -0.313288  0.057226  0.036928  0.005242  0.076727  0.194024 

  (0.04406)  (0.16489)  (0.08376)  (0.03641)  (0.21066)  (0.05084)  (0.08033) 

 [ 0.68472] [-1.89996] [ 0.68322] [ 1.01413] [ 0.02488] [ 1.50913] [ 2.41526] 

        

LOG(INTR(-1))  0.180906  0.373271 -0.478087  0.065513  0.367369  0.273585  0.247065 

  (0.15685)  (0.58697)  (0.29816)  (0.12962)  (0.74991)  (0.18098)  (0.28596) 

 [ 1.15340] [ 0.63593] [-1.60346] [ 0.50542] [ 0.48989] [ 1.51165] [ 0.86398] 

        

LOG(INTR(-2))  0.140221  1.225664  0.294857  0.173627  1.008236  0.248524 -0.026945 

  (0.13828)  (0.51750)  (0.26287)  (0.11428)  (0.66115)  (0.15956)  (0.25212) 

 [ 1.01402] [ 2.36844] [ 1.12168] [ 1.51930] [ 1.52497] [ 1.55752] [-0.10687] 

        

POLX(-1) -0.027296  1.031574  0.278190 -0.391291 -1.433729 -0.135993  0.227890 

  (0.13009)  (0.48686)  (0.24731)  (0.10751)  (0.62201)  (0.15012)  (0.23719) 

 [-0.20981] [ 2.11884] [ 1.12488] [-3.63943] [-2.30501] [-0.90591] [ 0.96080] 

        

POLX(-2)  0.063025 -1.156955 -0.209922 -0.065763  0.325293 -0.191429  0.334786 

  (0.18562)  (0.69464)  (0.35285)  (0.15340)  (0.88746)  (0.21418)  (0.33842) 

 [ 0.33954] [-1.66555] [-0.59493] [-0.42870] [ 0.36654] [-0.89376] [ 0.98927] 

        

C -0.392792 -0.853405  1.143827 -1.230586 -8.831539  0.628751 -0.264580 

  (0.66329)  (2.48225)  (1.26090)  (0.54816)  (3.17130)  (0.76537)  (1.20931) 



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 [-0.59219] [-0.34380] [ 0.90715] [-2.24493] [-2.78483] [ 0.82150] [-0.21879] 

 R-squared  0.888515  0.547731  0.819996  0.997768  0.945602  0.912387  0.765947 

 Adj. R-squared  0.828485  0.304201  0.723070  0.996566  0.916311  0.865211  0.639919 

 Sum sq. resids  0.717852  10.05363  2.594115  0.490287  16.40990  0.955814  2.386196 

 S.E. equation  0.166162  0.621834  0.315870  0.137321  0.794450  0.191734  0.302947 

 F-statistic  14.80115  2.249132  8.460071  830.1494  32.28278  19.34008  6.077576 

 Log likelihood  24.74733 -29.36089 -1.589779  32.56343 -39.40489  18.87819  0.122885 

Akaike AIC -0.475480  2.163946  0.809258 -0.856753  2.653897 -0.189180  0.725713 

 Schwarz SC  0.151437  2.790862  1.436174 -0.229836  3.280814  0.437737  1.352630 

 Mean 

dependent 

 0.804878  2.724202  6.101589  11.69422  1.451717  2.326163  0.463415 

 S.D. dependent  0.401218  0.745474  0.600238  2.343343  2.746197  0.522244  0.504854 

 Determinant Residual 

Covariance 

 1.57E-08      

 Log Likelihood (d.f. adjusted) -38.85718      

Akaike Information Criteria  7.017423      

 Schwarz Criteria  11.40584      

Source: Researchers‟ computation, 2013, adapted from regression result using E-view 4.1 

 

Table 8: Results of VAR lag order selection criteria. 

 Lag LogL LR FPE AIC SC HQ 

0 -233.0032 NA   0.000384  12.00016  12.29571  12.10702 

1 -17.89726   344.1694*   9.86E-08*  3.694863   6.059294*   4.549767* 

2  24.46435  52.95201  1.73E-07  4.026783  8.460091  5.629727 

3  86.07665  55.45107  1.83E-07   3.396167*  9.898353  5.747153 

Source: Researchers‟ computation, 2013, adapted from regression result using E-view 4.1 

* indicates lag order selected by the criterion 

 LR: sequential modified LR test statistic (each test at 5% level) 

 FPE: Final prediction error 

 AIC: Akaike information criterion 

 SC: Schwarz information criterion 

 HQ: Hannan-Quinn information criterion 

    

 

Table 9: VAR Residual Normality Tests 

Orthogonalization: Cholesky (Lutkepohl) 

     

Component Skewness Chi-sq df Prob. 

1  1.900810  24.68936 1  0.0000 

2  0.060026  0.024621 1  0.8753 



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3 -0.302136  0.623788 1  0.4296 

4  0.163287  0.182194 1  0.6695 

5 -0.061474  0.025824 1  0.8723 

6 -0.302005  0.623247 1  0.4298 

7 -0.198038  0.267996 1  0.6047 

Joint   26.43703 7  0.0004 

     

Component Kurtosis Chi-sq df Prob. 

1  8.607204  53.71126 1  0.0000 

2  1.124734  6.007561 1  0.0142 

3  1.719795  2.799830 1  0.0943 

4  1.525991  3.711702 1  0.0540 

5  1.372508  4.524916 1  0.0334 

6  2.142370  1.256530 1  0.2623 

7  1.748928  2.673851 1  0.1020 

Joint   74.68565 7  0.0000 

     

Component Jarque-Bera df Prob.  

     

1  78.40062 2  0.0000  

2  6.032182 2  0.0490  

3  3.423618 2  0.1805  

4  3.893896 2  0.1427  

5  4.550740 2  0.1028  

6  1.879776 2  0.3907  

7  2.941846 2  0.2297  

     

Joint  101.1227 14  0.0000  

     

     

Source: Own Computations using E-view 4.1 

Note: Variables are as defined in equation 2 

 

Table 10: Impulse Response 

 

   Response of 

TERR: 

    

 Period TERR LOG(INFL) LOG(GDPC) LOG(GOVX) LOG(OPEN) LOG(INTR) POLX 

 1  0.166162  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000 

 2  0.126029 -0.018968  0.028931 -0.005092  0.020525  0.031695 -0.006066 



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 3  0.102592 -0.024122  0.055941  0.003605  0.024617  0.044336  0.002790 

 4  0.085424 -0.015267  0.068273 -0.003002  0.014174  0.034688 -0.003369 

 5  0.068695 -0.006630  0.061605 -0.005506  0.017739  0.030102  0.000835 

 6  0.054544  0.001900  0.049156 -0.006652  0.014151  0.027564 -0.001585 

 7  0.040870  0.008096  0.038236 -0.008960  0.013367  0.027399 -0.000888 

 8  0.032848  0.008929  0.029206 -0.009668  0.011324  0.027005 -0.001115 

 9  0.027275  0.007712  0.022966 -0.010127  0.008641  0.025329 -0.002735 

 10  0.024195  0.006140  0.018273 -0.010257  0.006939  0.022357 -0.003693 

   Response of 

LOG(INFL): 

    

 Period TERR LOG(INFL) LOG(GDPC) LOG(GOVX) LOG(OPEN) LOG(INTR) POLX 

 1 -0.148760  0.603779  0.000000  0.000000  0.000000  0.000000  0.000000 

 2 -0.007438  0.329946 -0.044796 -0.157136  0.181346  0.123998  0.229260 

 3  0.001847 -0.030133 -0.012030  0.030640  0.030094  0.296118  0.054848 

 4 -0.097648 -0.008060  0.101258 -0.044233 -0.092023  0.118495 -0.051405 

 5 -0.001751 -0.037987  0.039894 -0.041258  0.006248  0.014694 -0.000359 

 6  0.042238 -0.023995 -0.025055 -0.021557 -0.015133 -0.024945 -0.064057 

 7  0.024141  0.024594 -0.036773 -0.030234  0.017519 -0.013131 -0.033526 

 8  0.045206  0.024472 -0.030720 -0.022399  0.023174  0.013294 -0.007036 

 9  0.046134  0.010538 -0.004130 -0.017838  0.011899  0.033379 -0.002104 

 10  0.044438 -0.002056  0.018607 -0.014835  0.008066  0.033514  0.005844 

    Response of 

LOG(GDPC): 

    

 Period TERR LOG(INFL) LOG(GDPC) LOG(GOVX) LOG(OPEN) LOG(INTR) POLX 

 1  0.082616 -0.037369  0.302575  0.000000  0.000000  0.000000  0.000000 

 2  0.067582 -0.020141  0.255855  0.010231 -0.120873 -0.072661  0.061826 

 3  0.002302  0.015403  0.165251  0.050263 -0.102575 -0.013325  0.077488 

 4 -0.048731  0.043408  0.086279  0.034868 -0.079764 -0.039731  0.080645 

 5 -0.063092  0.001040  0.021461  0.045654 -0.022203 -0.019878  0.068881 

 6 -0.086869 -0.014257 -0.008357  0.043689 -0.023701 -0.032904  0.020735 

 7 -0.098118 -0.010816 -0.025904  0.033185 -0.009091 -0.042981  0.004095 

 8 -0.090709 -0.002963 -0.041704  0.026852 -0.011624 -0.048628 -0.010959 

 9 -0.084251  0.007662 -0.047899  0.019889 -0.015624 -0.043164 -0.017256 

 10 -0.072286  0.012772 -0.047184  0.015254 -0.020546 -0.034574 -0.017069 

    Response of 

LOG(GOVX): 

    

 Period TERR LOG(INFL) LOG(GDPC) LOG(GOVX) LOG(OPEN) LOG(INTR) POLX 

 1 -0.024546  0.004700 -0.006364  0.134878  0.000000  0.000000  0.000000 

 2 -0.029482  0.020033  0.045199  0.033115 -0.052215 -0.009067 -0.086962 

 3 -0.027846  0.007354  0.026643  0.039570 -0.030826  0.007409 -0.059023 



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 4 -0.008551  0.028622  0.018458  0.024778 -0.081486  0.005581 -0.107233 

 5  0.004265  0.048190  0.010693  0.004804 -0.070433  0.012838 -0.093220 

 6  0.043495  0.041261  0.004275  0.003096 -0.073631  0.029153 -0.096814 

 7  0.067518  0.036838  0.014497 -0.003541 -0.075964  0.042553 -0.102075 

 8  0.096122  0.029478  0.027664 -0.007588 -0.070221  0.051683 -0.099310 

 9  0.123269  0.022822  0.041131 -0.010217 -0.066358  0.058793 -0.100836 

 10  0.143827  0.020642  0.054337 -0.013421 -0.059687  0.065333 -0.099350 

   Response of 

LOG(OPEN)

: 

    

 Period TERR LOG(INFL) LOG(GDPC) LOG(GOVX) LOG(OPEN) LOG(INTR) POLX 

 1 -0.045176  0.063459 -0.192661  0.154316  0.751099  0.000000  0.000000 

 2 -0.013464  0.057147 -0.174053 -0.044014  0.380632 -0.009881 -0.318636 

 3 -0.151163  0.140510 -0.070392 -0.053173  0.247581  0.196247 -0.228333 

 4 -0.074011  0.232530  0.003951 -0.122192 -0.021738  0.161599 -0.215346 

 5  0.029897  0.186480  0.014645 -0.132110 -0.063159  0.193406 -0.140353 

 6  0.130999  0.116849  0.019134 -0.108032 -0.121304  0.179284 -0.146326 

 7  0.199771  0.065525  0.040467 -0.096523 -0.104726  0.160976 -0.135382 

 8  0.267563  0.024076  0.058923 -0.078489 -0.076049  0.139186 -0.125935 

 9  0.305960  0.004643  0.084020 -0.065717 -0.047670  0.131053 -0.119569 

 10  0.322252  0.001896  0.110546 -0.058151 -0.022487  0.130262 -0.103788 

   Response of 

LOG(INTR)

: 

    

 Period TERR LOG(INFL) LOG(GDPC) LOG(GOVX) LOG(OPEN) LOG(INTR) POLX 

 1  0.043168 -0.005858 -0.017607  0.028029 -0.012630  0.183328  0.000000 

 2  0.027994  0.039441  0.013606 -0.039853 -0.034304  0.042830 -0.030223 

 3  0.092404 -0.036117 -0.002917 -0.022373  0.058573  0.056085 -0.011959 

 4  0.089367 -0.025967  0.007814 -0.016397  0.007141  0.029150 -0.067815 

 5  0.077161  0.007044  0.023857 -0.029319  0.027355  0.031164 -0.036444 

 6  0.092422  0.012453  0.026060 -0.024726  0.018174  0.037435 -0.023341 

 7  0.085064  0.013551  0.036595 -0.023798  0.011151  0.049375 -0.016579 

 8  0.080385  0.009570  0.043933 -0.021969  0.009035  0.050361 -0.007120 

 9  0.077499  0.002755  0.045774 -0.019160  0.008004  0.047096 -0.005713 

 10  0.071136  0.000315  0.043846 -0.017446  0.009191  0.040706 -0.005477 

    Response of 

POLX: 

    

 Period TERR LOG(INFL) LOG(GDPC) LOG(GOVX) LOG(OPEN) LOG(INTR) POLX 

 1  0.144480  0.003490 -0.009804  0.135691 -0.009399  0.053867  0.222243 

 2  0.110283 -0.073428  0.095567  0.059788 -0.027457  0.057570  0.050647 



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 3  0.041309 -0.115834  0.104902  0.060037  0.053259  0.005059  0.067032 

 4  0.021320 -0.088181  0.076422  0.057105  0.023322 -0.034085 -0.012137 

 5 -0.033962 -0.009615  0.040039  0.025782  0.029286 -0.052772 -0.011752 

 6 -0.043804  0.025223 -0.000161  0.018049  0.027346 -0.030615 -0.000459 

 7 -0.057441  0.037561 -0.014018  0.009586  0.003746 -0.006494 -0.005258 

 8 -0.059090  0.031428 -0.015357  0.003847 -0.008716  0.006055 -0.002934 

 9 -0.046889  0.017171 -0.015842  0.002371 -0.020513  0.005060 -0.010343 

 10 -0.033927  0.008350 -0.016712  0.001054 -0.024231 -0.001346 -0.017452 

 

Table 11: Variance Decomposition 

     Variance  

decompositio

n of TERR 

 

   

 

Period 

S.E. TERR LOG(INFL) LOG(GDPC) LOG(GOVX) LOG(OPEN) LOG(INTR) POLX 

 1  0.166162  100.0000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000 

 2  0.214892  94.18479  0.779127  1.812577  0.056141  0.912307  2.175370  0.079691 

 3  0.251013  85.73325  1.494544  6.295081  0.061774  1.630443  4.714151  0.070759 

 4  0.276809  80.02196  1.533165  11.25963  0.062559  1.602894  5.446794  0.072999 

 5  0.293996  76.39933  1.410018  14.37262  0.090536  1.785024  5.876945  0.065521 

 6  0.304689  74.33559  1.316672  15.98435  0.131956  1.877628  6.290090  0.063708 

 7  0.311518  72.83324  1.327111  16.79772  0.208958  1.980334  6.790886  0.061757 

 8  0.316240  71.75356  1.367506  17.15283  0.296232  2.049872  7.318827  0.061170 

 9  0.319632  70.96672  1.396846  17.30694  0.390356  2.079681  7.792259  0.067198 

 10  0.322162  70.42042  1.411319  17.35789  0.485619  2.093529  8.151942  0.079284 

     Variance    

decomposition of 

LOG(INFL) 

 

 

Period 

S.E. TERR LOG(INFL) LOG(GDPC) LOG(GOVX) LOG(OPEN) LOG(INTR) POLX 

 1  0.621834  5.722961  94.27704  0.000000  0.000000  0.000000  0.000000  0.000000 

 2  0.789379  3.560278  75.97479  0.322038  3.962627  5.277727  2.467524  8.435018 

 3  0.846589  3.095823  66.18004  0.300176  3.576138  4.714878  14.37971  7.753232 

 4  0.873885  4.154039  62.11884  1.624332  3.612429  5.533818  15.33406  7.622473 

 5  0.876738  4.127444  61.90292  1.820828  3.810407  5.502936  15.26251  7.572957 

 6  0.881519  4.312372  61.30731  1.881912  3.828987  5.472874  15.17747  8.019074 

 7  0.884383  4.358998  60.98823  2.042638  3.921103  5.476726  15.10137  8.110934 

 8  0.887122  4.591796  60.68838  2.149961  3.960685  5.511208  15.03074  8.067227 

 9  0.889281  4.838664  60.40813  2.141692  3.981713  5.502385  15.09874  8.028667 

 10  0.891396  5.064248  60.12223  2.175111  3.990530  5.484483  15.16851  7.994896 



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Variance    

decompositio

n  

ofLOG(GDP

C) 

   

 

Period 

S.E. TERR LOG(INFL) LOG(GDPC) LOG(GOVX) LOG(OPEN) LOG(INTR) POLX 

 1  0.315870  6.840920  1.399605  91.75948  0.000000  0.000000  0.000000  0.000000 

 2  0.440483  5.871758  0.928791  80.92419  0.053948  7.530124  2.721128  1.970058 

 3  0.490720  4.733273  0.846883  76.54360  1.092589  10.43661  2.266233  4.080813 

 4  0.517851  5.135812  1.463116  71.50919  1.434458  11.74417  2.623623  6.089638 

 5  0.529461  6.333029  1.400040  68.57180  2.115744  11.41062  2.650773  7.517994 

 6  0.540491  8.660353  1.413054  65.82533  2.683633  11.14192  2.914281  7.361431 

 7  0.552806  11.42912  1.389084  63.14491  2.925764  10.67810  3.390419  7.042605 

 8  0.564722  13.53191  1.333832  61.05350  3.029684  10.27457  3.990326  6.786180 

 9  0.575468  15.17470  1.302213  59.48754  3.037048  9.968160  4.405312  6.625028 

 10  0.583883  16.27313  1.312798  58.43819  3.018388  9.806719  4.629877  6.520895 

    Variance              

decomposition      

of LOG(GOVX) 

  

 

Period 

S.E. TERR LOG(INFL) LOG(GDPC) LOG(GOVX) LOG(OPEN) LOG(INTR) POLX 

 1  0.137321  3.195072  0.117152  0.214806  96.47297  0.000000  0.000000  0.000000 

 2  0.183407  4.375080  1.258778  6.193622  57.34155  8.105212  0.244404  22.48135 

 3  0.203057  5.449831  1.158091  6.774436  50.57811  8.917062  0.332533  26.78994 

 4  0.247486  3.788144  2.117130  5.116740  35.05096  16.84362  0.274718  36.80869 

 5  0.278465  3.015631  4.667092  4.189053  27.71579  19.70186  0.429541  40.28103 

 6  0.311141  4.369663  5.496826  3.374253  22.20985  21.38106  1.221953  41.94639 

 7  0.347776  7.266713  5.521740  2.874582  17.78753  21.88492  2.475187  42.18933 

 8  0.386450  12.07173  5.053697  2.840462  14.44401  21.02556  3.793129  40.77141 

 9  0.429981  17.96993  4.363945  3.209460  11.72391  19.36555  4.933598  38.43361 

 10  0.476267  23.76656  3.744788  3.917577  9.635297  17.35498  5.903021  35.67778 

      

Variance  

decomposition      

    of 

LOG(OPEN) 

   

 

Period 

S.E. TERR LOG(INFL) LOG(GDPC) LOG(GOVX) LOG(OPEN) LOG(INTR) POLX 



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 1  0.794450  0.323352  0.638043  5.881080  3.773039  89.38449  0.000000  0.000000 

 2  0.955686  0.243298  0.798479  7.380956  2.819421  77.63088  0.010690  11.11627 

 3  1.056247  2.247314  2.423302  6.486579  2.561550  69.04697  3.460804  13.77348 

 4  1.123882  2.418625  6.421106  5.730576  3.444587  61.02389  5.124242  15.83698 

 5  1.173687  2.282600  8.412129  5.270116  4.425428  56.24431  7.413980  15.95144 

 6  1.220107  3.264980  8.701391  4.901325  4.879087  53.03444  9.019735  16.19904 

 7  1.264526  5.535424  8.369327  4.665444  5.124979  50.05988  10.01775  16.22719 

 8  1.312191  9.298321  7.806016  4.534302  5.117202  46.82504  10.42832  15.99080 

 9  1.364038  13.63616  7.225037  4.575563  4.967696  43.45517  10.57370  15.56667 

 10  1.417144  17.80416  6.693861  4.847553  4.770732  40.28450  10.64098  14.95821 

     Variance           

decomposition      

of LOG(INTR) 

   

 

Period 

S.E. TERR LOG(INFL) LOG(GDPC) LOG(GOVX) LOG(OPEN) LOG(INTR) POLX 

 1  0.191734  5.069115  0.093336  0.843320  2.137088  0.433950  91.42319  0.000000 

 2  0.211659  5.908988  3.548942  1.105242  5.299003  2.982903  79.11594  2.038981 

 3  0.248738  18.07903  4.678071  0.814036  4.645956  7.704977  62.37037  1.707559 

 4  0.276335  25.10721  4.673352  0.739515  4.116426  6.309653  51.64771  7.406133 

 5  0.294688  28.93336  4.166515  1.305645  4.609526  6.409893  46.53327  8.041792 

 6  0.314809  33.97194  3.807408  1.829335  4.656027  5.949970  42.18895  7.596376 

 7  0.333567  36.76179  3.556263  2.832935  4.656101  5.411355  39.76848  7.013077 

 8  0.350573  38.53934  3.294128  4.135177  4.608018  4.965505  38.06740  6.390431 

 9  0.365640  39.92120  3.033924  5.368615  4.510674  4.612640  36.65390  5.899048 

 10  0.377824  40.93274  2.841466  6.374638  4.437656  4.379111  35.48867  5.545720 

     Variance           

decomposition      

of LOG(POLX) 

   

 

Period 

S.E. TERR LOG(INFL) LOG(GDPC) LOG(GOVX) LOG(OPEN) LOG(INTR) POLX 

 1  0.302947  22.74493  0.013270  0.104728  20.06183  0.096249  3.161594  53.81739 

 2  0.358708  25.67533  4.199664  7.172667  17.08747  0.654554  4.830798  40.37951 

 3  0.407136  20.96002  11.35452  12.20652  15.43874  2.219352  3.765362  34.05548 

 4  0.430052  19.03155  14.38117  14.09819  15.60044  2.283232  4.002937  30.60248 

 5  0.438451  18.90940  13.88357  14.39717  15.35427  2.642755  5.299702  29.51313 

 6  0.443627  19.44565  13.88473  14.06316  15.16357  2.961408  5.652990  28.82850 

 7  0.449319  20.59040  14.23399  13.80645  14.82734  2.893806  5.531567  28.11645 

 8  0.454685  21.79614  14.37775  13.59655  14.48657  2.862646  5.419500  27.46085 

 9  0.458304  22.50008  14.29200  13.50219  14.26141  3.017961  5.346458  27.07989 

 10  0.460909  22.78828  14.16371  13.48145  14.10118  3.260337  5.287042  26.91800 

Source: Own Computations using E-view 4.1 



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Note: Variables are as defined in equation 2 

 

Table 12: Pairwise Granger Causality Tests 

  Null Hypothesis: Obs F-Statistic Probability 

  LOG(GDPC) does not Granger Cause 

LOG(INFL) 

41  0.59631  0.55619 

  LOG(INFL) does not Granger Cause LOG(GDPC)  0.08914  0.91492 

  LOG(GOVX) does not Granger Cause 

LOG(INFL) 

41  0.23011  0.79561 

  LOG(INFL) does not Granger Cause LOG(GOVX)  0.63140  0.53763 

  LOG(OPEN) does not Granger Cause LOG(INFL) 41  0.64984  0.52814 

  LOG(INFL) does not Granger Cause LOG(OPEN)  0.20646  0.81442 

  LOG(INTR) does not Granger Cause LOG(INFL) 41  1.13996  0.33110 

  LOG(INFL) does not Granger Cause LOG(INTR)  1.49031  0.23887 

  POLX does not Granger Cause LOG(INFL) 41  0.43285  0.65199 

  LOG(INFL) does not Granger Cause POLX  0.77673  0.46746 

  TERR does not Granger Cause LOG(INFL) 41  0.29372  0.74726 

  LOG(INFL) does not Granger Cause TERR  0.13166  0.87706 

  LOG(GOVX) does not Granger Cause 

LOG(GDPC) 

41  1.21548  0.30845 

  LOG(GDPC) does not Granger Cause LOG(GOVX)  2.03077  0.14600 

  LOG(OPEN) does not Granger Cause 

LOG(GDPC) 

41  2.13279  0.13324 

  LOG(GDPC) does not Granger Cause LOG(OPEN)  0.11118  0.89509 

  LOG(INTR) does not Granger Cause 

LOG(GDPC) 

41  1.81976  0.17666 

  LOG(GDPC) does not Granger Cause LOG(INTR)  0.33510  0.71747 

  POLX does not Granger Cause LOG(GDPC) 41  0.50060  0.61032 

  LOG(GDPC) does not Granger Cause POLX  1.81650  0.17718 

  TERR does not Granger Cause LOG(GDPC) 41  0.75834  0.47578 

  LOG(GDPC) does not Granger Cause TERR  0.44572  0.64385 

  LOG(OPEN) does not Granger Cause 

LOG(GOVX) 

41  2.21918  0.12336 

  LOG(GOVX) does not Granger Cause LOG(OPEN)  0.57034  0.57036 

  LOG(INTR) does not Granger Cause 

LOG(GOVX) 

41  7.17436  0.00239 

  LOG(GOVX) does not Granger Cause LOG(INTR)  0.45856  0.63583 

  POLX does not Granger Cause LOG(GOVX) 41  9.80739  0.00040 

  LOG(GOVX) does not Granger Cause POLX  3.83616  0.03088 

  TERR does not Granger Cause LOG(GOVX) 41  3.20184  0.05250 



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  LOG(GOVX) does not Granger Cause TERR  0.03978  0.96104 

  LOG(INTR) does not Granger Cause 

LOG(OPEN) 

41  3.13488  0.05558 

  LOG(OPEN) does not Granger Cause LOG(INTR)  0.71094  0.49795 

  POLX does not Granger Cause LOG(OPEN) 41  1.94552  0.15765 

  LOG(OPEN) does not Granger Cause POLX  2.90440  0.06771 

  TERR does not Granger Cause LOG(OPEN) 41  0.55851  0.57694 

  LOG(OPEN) does not Granger Cause TERR  0.08092  0.92244 

  POLX does not Granger Cause LOG(INTR) 41  0.43424  0.65110 

  LOG(INTR) does not Granger Cause POLX  0.02879  0.97164 

  TERR does not Granger Cause LOG(INTR) 41  3.70864  0.03432 

  LOG(INTR) does not Granger Cause TERR  0.15309  0.85860 

  TERR does not Granger Cause POLX 41  0.19446  0.82413 

  POLX does not Granger Cause TERR  0.00000  1.00000 

Source: Own Computations using E-view 4.1 

Note: Variables are as defined in equation 2 

 

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

-1.5 -1.0 -0.5 0.0 0.5 1.0 1.5

Inverse Roots of AR Characteristic Polynomial

 

Figure 1: Inverse Roots of AR characteristic Polynomial 

 



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-.6

-.4

-.2

.0

.2

.4

.6

2 4 6 8 10 12

Cor(TERR,TERR(-i))

-.6

-.4

-.2

.0

.2

.4

.6

2 4 6 8 10 12

Cor(TERR,LOG(INFL)(-i) )

-.6

-.4

-.2

.0

.2

.4

.6

2 4 6 8 10 12

Cor(TERR,LOG(GDPC)(-i))

-.6

-.4

-.2

.0

.2

.4

.6

2 4 6 8 10 12

Cor(TERR,LOG(GOVX)(-i))

-.6

-.4

-.2

.0

.2

.4

.6

2 4 6 8 10 12

Cor(TERR,LOG(OPEN)(-i))

-.6

-.4

-.2

.0

.2

.4

.6

2 4 6 8 10 12

Cor(TERR,LOG(INTR)(-i))

-.6

-.4

-.2

.0

.2

.4

.6

2 4 6 8 10 12

Cor(TERR,POLX(-i))

-.6

-.4

-.2

.0

.2

.4

.6

2 4 6 8 10 12

Cor(LOG(INFL),TERR(-i))

-.6

-.4

-.2

.0

.2

.4

.6

2 4 6 8 10 12

Cor(LOG(INFL),LOG(INFL)(-i))

-.6

-.4

-.2

.0

.2

.4

.6

2 4 6 8 10 12

Cor(LOG(INFL),LOG(GDPC)(-i))

-.6

-.4

-.2

.0

.2

.4

.6

2 4 6 8 10 12

Cor(LOG(INFL),LOG(GOVX)(-i))

-.6

-.4

-.2

.0

.2

.4

.6

2 4 6 8 10 12

Cor(LOG(INFL),LOG(OPEN)(-i))

-.6

-.4

-.2

.0

.2

.4

.6

2 4 6 8 10 12

Cor(LOG(INFL),LOG(INTR)(-i))

-.6

-.4

-.2

.0

.2

.4

.6

2 4 6 8 10 12

Cor(LOG(INFL),POLX(-i))

-.6

-.4

-.2

.0

.2

.4

.6

2 4 6 8 10 12

Cor(LOG(GDPC),TERR(-i))

-.6

-.4

-.2

.0

.2

.4

.6

2 4 6 8 10 12

Cor(LOG(GDPC),LOG(INFL)(-i))

-.6

-.4

-.2

.0

.2

.4

.6

2 4 6 8 10 12

Cor(LOG(GDPC),LOG(GDPC)(-i))

-.6

-.4

-.2

.0

.2

.4

.6

2 4 6 8 10 12

Cor(LOG(GDPC),LOG(GOVX)(-i))

-.6

-.4

-.2

.0

.2

.4

.6

2 4 6 8 10 12

Cor(LOG(GDPC),LOG(OPEN)(-i))

-.6

-.4

-.2

.0

.2

.4

.6

2 4 6 8 10 12

Cor(LOG(GDPC),LOG(INTR)(-i) )

-.6

-.4

-.2

.0

.2

.4

.6

2 4 6 8 10 12

Cor(LOG(GDPC),POLX(-i))

-.6

-.4

-.2

.0

.2

.4

.6

2 4 6 8 10 12

Cor(LOG(GOVX),TERR(-i))

-.6

-.4

-.2

.0

.2

.4

.6

2 4 6 8 10 12

Cor(LOG(GOVX),LOG(INFL)(-i))

-.6

-.4

-.2

.0

.2

.4

.6

2 4 6 8 10 12

Cor(LOG(GOVX),LOG(GDPC)(-i))

-.6

-.4

-.2

.0

.2

.4

.6

2 4 6 8 10 12

Cor(LOG(GOVX),LOG(GOVX)(-i))

-.6

-.4

-.2

.0

.2

.4

.6

2 4 6 8 10 12

Cor(LOG(GOVX),LOG(OPEN)(-i))

-.6

-.4

-.2

.0

.2

.4

.6

2 4 6 8 10 12

Cor(LOG(GOVX),LOG(INTR)(-i) )

-.6

-.4

-.2

.0

.2

.4

.6

2 4 6 8 10 12

Cor(LOG(GOVX),POLX(-i))

-.6

-.4

-.2

.0

.2

.4

.6

2 4 6 8 10 12

Cor(LOG(OPEN),TERR(-i))

-.6

-.4

-.2

.0

.2

.4

.6

2 4 6 8 10 12

Cor(LOG(OPEN),LOG(INFL)(-i))

-.6

-.4

-.2

.0

.2

.4

.6

2 4 6 8 10 12

Cor(LOG(OPEN),LOG(GDPC)(-i))

-.6

-.4

-.2

.0

.2

.4

.6

2 4 6 8 10 12

Cor(LOG(OPEN),LOG(GOVX)(-i))

-.6

-.4

-.2

.0

.2

.4

.6

2 4 6 8 10 12

Cor(LOG(OPEN),LOG(OPEN)(-i))

-.6

-.4

-.2

.0

.2

.4

.6

2 4 6 8 10 12

Cor(LOG(OPEN),LOG(INTR)(-i))

-.6

-.4

-.2

.0

.2

.4

.6

2 4 6 8 10 12

Cor(LOG(OPEN),POLX(-i))

-.6

-.4

-.2

.0

.2

.4

.6

2 4 6 8 10 12

Cor(LOG(INTR),TERR(-i))

-.6

-.4

-.2

.0

.2

.4

.6

2 4 6 8 10 12

Cor(LOG(INTR),LOG(INFL)(-i))

-.6

-.4

-.2

.0

.2

.4

.6

2 4 6 8 10 12

Cor(LOG(INTR),LOG(GDPC)(-i))

-.6

-.4

-.2

.0

.2

.4

.6

2 4 6 8 10 12

Cor(LOG(INTR),LOG(GOVX)(-i))

-.6

-.4

-.2

.0

.2

.4

.6

2 4 6 8 10 12

Cor(LOG(INTR),LOG(OPEN)(-i))

-.6

-.4

-.2

.0

.2

.4

.6

2 4 6 8 10 12

Cor(LOG(INTR),LOG(INTR)(-i))

-.6

-.4

-.2

.0

.2

.4

.6

2 4 6 8 10 12

Cor(LOG(INTR),POLX(-i))

-.6

-.4

-.2

.0

.2

.4

.6

2 4 6 8 10 12

Cor(POLX,TERR(-i))

-.6

-.4

-.2

.0

.2

.4

.6

2 4 6 8 10 12

Cor(POLX,LOG(INFL)(-i) )

-.6

-.4

-.2

.0

.2

.4

.6

2 4 6 8 10 12

Cor(POLX,LOG(GDPC)(-i))

-.6

-.4

-.2

.0

.2

.4

.6

2 4 6 8 10 12

Cor(POLX,LOG(GOVX)(-i))

-.6

-.4

-.2

.0

.2

.4

.6

2 4 6 8 10 12

Cor(POLX,LOG(OPEN)(-i))

-.6

-.4

-.2

.0

.2

.4

.6

2 4 6 8 10 12

Cor(POLX,LOG(INTR)(-i))

-.6

-.4

-.2

.0

.2

.4

.6

2 4 6 8 10 12

Cor(POLX,POLX(-i))

Autocorrelations with 2 Std.Err. Bounds

 

Figure 2: Pairwise  Cross-Correlograms for the Estimated Residuals 

 

Table 13: Over parameterized Regression Estimates  

Variable Coefficien

t 

Std. Error t-Statistic Prob.   

C -0.051816 0.470048 -0.110236 0.9130 

LOG(GDPC) 0.232026 0.056988 4.071458 0.0003 

LOG(OPEN) 0.121070 0.026233 4.615207 0.0001 

LOG(INFL) -0.079971 0.034086 -2.346150 0.0263 

LOG(GOVX) -0.184890 0.031325 -5.902341 0.0000 

LOG(INTR) 0.628458 0.082824 7.587871 0.0000 

POLX 0.328669 0.077225 4.255988 0.0002 

ECM(-1) 0.464029 0.120551 3.849235 0.0006 

DLOG(GDPC) -0.144426 0.061097 -2.363885 0.0253 

DLOG(OPEN) -0.079500 0.029229 -2.719929 0.0111 



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DLOG(GOVX) 0.399108 0.172224 2.317379 0.0280 

DLOG(INTR) -0.412218 0.104604 -3.940756 0.0005 

D(POLX) -0.417677 0.096888 -4.310939 0.0002 

DLOG(INFL) 0.024306 0.029174 0.833152 0.4118 

R-squared 0.939322     Mean dependent var 0.785714 

Adjusted R-squared 0.911150     S.D. dependent var 0.415300 

S.E. of regression 0.123791     Akaike info criterion -1.07924

2 

Sum squared resid 0.429078     Schwarz criterion -0.50001

9 

Log likelihood 36.66408     F-statistic 33.34263 

Durbin-Watson stat 1.778757     Prob(F-statistic) 0.000000 

 

Table 14: Error Correction Model Estimates  

Variable Coefficien

t 

Std. Error t-Statistic Prob.   

C -0.051268 0.467562 -0.109649 0.9134 

LOG(GDPC) 0.217044 0.053791 4.034945 0.0004 

LOG(OPEN) 0.115505 0.025234 4.577340 0.0001 

LOG(INFL) -0.064628 0.028532 -2.265142 0.0312 

LOG(GOVX) -0.178076 0.030079 -5.920365 0.0000 

LOG(INTR) 0.616936 0.081230 7.594969 0.0000 

POLX 0.340240 0.075564 4.502661 0.0001 

ECM(-1) 0.470324 0.119678 3.929922 0.0005 

DLOG(GDPC) -0.142587 0.060734 -2.347727 0.0259 

DLOG(OPEN) -0.075312 0.028641 -2.629527 0.0135 

DLOG(GOVX) 0.393111 0.171164 2.296700 0.0290 

DLOG(INTR) -0.411254 0.104044 -3.952685 0.0005 

D(POLX) -0.417237 0.096374 -4.329353 0.0002 

R-squared 0.937818     Mean dependent var 0.785714 

Adjusted R-squared 0.912088     S.D. dependent var 0.415300 

S.E. of regression 0.123137     Akaike info criterion -1.10237

2 

Sum squared resid 0.439715     Schwarz criterion -0.56452

2 

Log likelihood 36.14982     F-statistic 36.44775 

Durbin-Watson stat 1.802824     Prob(F-statistic) 0.000000 

 

Images of Terrorism in Nigeria 



www.cribfb.com/journal/index.php/ijfb        Indian Journal of Finance and Banking Vol. 2, No. 1; 2018 

33 
 

 

A terror attack in Nigeria's northern           A car burns after a bombing that        Rescuers helping one of 

the victims of 

city of Kano                                                 killed 35 worshippers outside            

the Christmas Day terrorist attacks on 

                                                                    St. Theresa Catholic 

Church inChristian churches in Jos, Damaturu, 

                                                                     Madalla, Nigeria. 

Potiskum and other areas in the Middle                            

                                                                                                                                  

Belt and Northern Nigeria. 

 

Source: IndianVision (2012),Gambrell (2011), Nigerians Report (2011). 

 

 

 

 

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