Contents 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 www.cribfb.com/journal/index.php/ijfb Indian Journal of Finance and Banking Vol. 2, No. 1; 2018 2 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 www.cribfb.com/journal/index.php/ijfb Indian Journal of Finance and Banking Vol. 2, No. 1; 2018 3 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, www.cribfb.com/journal/index.php/ijfb Indian Journal of Finance and Banking Vol. 2, No. 1; 2018 4 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 www.cribfb.com/journal/index.php/ijfb Indian Journal of Finance and Banking Vol. 2, No. 1; 2018 5 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: www.cribfb.com/journal/index.php/ijfb Indian Journal of Finance and Banking Vol. 2, No. 1; 2018 6 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) www.cribfb.com/journal/index.php/ijfb Indian Journal of Finance and Banking Vol. 2, No. 1; 2018 7 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) www.cribfb.com/journal/index.php/ijfb Indian Journal of Finance and Banking Vol. 2, No. 1; 2018 8 ∆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 www.cribfb.com/journal/index.php/ijfb Indian Journal of Finance and Banking Vol. 2, No. 1; 2018 9 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 www.cribfb.com/journal/index.php/ijfb Indian Journal of Finance and Banking Vol. 2, No. 1; 2018 10 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 www.cribfb.com/journal/index.php/ijfb Indian Journal of Finance and Banking Vol. 2, No. 1; 2018 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 www.cribfb.com/journal/index.php/ijfb Indian Journal of Finance and Banking Vol. 2, No. 1; 2018 12 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. 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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 www.cribfb.com/journal/index.php/ijfb Indian Journal of Finance and Banking Vol. 2, No. 1; 2018 16 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 www.cribfb.com/journal/index.php/ijfb Indian Journal of Finance and Banking Vol. 2, No. 1; 2018 17 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 www.cribfb.com/journal/index.php/ijfb Indian Journal of Finance and Banking Vol. 2, No. 1; 2018 18 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 www.cribfb.com/journal/index.php/ijfb Indian Journal of Finance and Banking Vol. 2, No. 1; 2018 19 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 www.cribfb.com/journal/index.php/ijfb Indian Journal of Finance and Banking Vol. 2, No. 1; 2018 20 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) www.cribfb.com/journal/index.php/ijfb Indian Journal of Finance and Banking Vol. 2, No. 1; 2018 21 [ 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) www.cribfb.com/journal/index.php/ijfb Indian Journal of Finance and Banking Vol. 2, No. 1; 2018 22 [-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 www.cribfb.com/journal/index.php/ijfb Indian Journal of Finance and Banking Vol. 2, No. 1; 2018 23 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 www.cribfb.com/journal/index.php/ijfb Indian Journal of Finance and Banking Vol. 2, No. 1; 2018 24 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 www.cribfb.com/journal/index.php/ijfb Indian Journal of Finance and Banking Vol. 2, No. 1; 2018 25 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 www.cribfb.com/journal/index.php/ijfb Indian Journal of Finance and Banking Vol. 2, No. 1; 2018 26 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 www.cribfb.com/journal/index.php/ijfb Indian Journal of Finance and Banking Vol. 2, No. 1; 2018 27 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 www.cribfb.com/journal/index.php/ijfb Indian Journal of Finance and Banking Vol. 2, No. 1; 2018 28 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 www.cribfb.com/journal/index.php/ijfb Indian Journal of Finance and Banking Vol. 2, No. 1; 2018 29 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 www.cribfb.com/journal/index.php/ijfb Indian Journal of Finance and Banking Vol. 2, No. 1; 2018 30 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 www.cribfb.com/journal/index.php/ijfb Indian Journal of Finance and Banking Vol. 2, No. 1; 2018 31 -.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 www.cribfb.com/journal/index.php/ijfb Indian Journal of Finance and Banking Vol. 2, No. 1; 2018 32 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). Copyrights Copyright for this article is retained by the author(s), with first publication rights granted to the journal. This is an open-access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/4.0/).