econometrics final report

institute of business management

Impact of Terrorism on Economy in Pakistan

Term Report Econometrics

Submitted to:

Sir Ejaz Rasheed (Lecturer, IoBM)

By:

Mujtaba Haider (12713)

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Table of ContentsAbstract:......................................................................................................................................................3

Introduction:...............................................................................................................................................4

Methodology:..............................................................................................................................................5

Variables:.....................................................................................................................................................5

Literature Review:.......................................................................................................................................6

Regression Analysis:....................................................................................................................................8

Interpretation:.............................................................................................................................................8

Graphs:........................................................................................................................................................9

Multicolinearity Detection:........................................................................................................................10

Analysis:.....................................................................................................................................................10

Heteroscedacity test:.................................................................................................................................11

Analysis:.....................................................................................................................................................11

Omitted variables test:..............................................................................................................................12

Analysis:.....................................................................................................................................................13

Test of Stationary:.....................................................................................................................................13

Analysis:.....................................................................................................................................................13

Unit Root Tests:.........................................................................................................................................14

Conclusion:................................................................................................................................................19

Sources of Data:........................................................................................................................................20

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Abstract:

In this research paper I have carried out regression analysis taking GDP growth as a dependent variable and deaths due tor terrorism incidents, inflation, percentage of military expenditure to GDP and Karachi Stocks Exchange All index values as an independent variable from year 2003 till 2014. The purpose of this study is to gauge the impact of terrorism on the economy therefore I have taken GDP growth rate as a dependent variable which is the economic indicator, whereas, deaths due to terrorism shows the escalation of terrorist activity in that particular year and inflation shows the general price level due to terrorism and other factors on economic indicator.

Through this study we came to know that terrorism activity had a negative impact on GDP growth during 2003 year till 2014, whereas, Inflation had a positive impact on GDP growth since the inflation is high when there is development going on or there is a high economic activity. As far as military expenditure is concerned it shows the positive impact on GDP growth because increase in military expenditure curtails terrorism activities and enhances economy.

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Introduction:Over the past ten years it is said to be that terrorism incidents in Pakistan has worse off the economic scenario. In order to gauge the impact of terrorism on the economic indicators of Pakistan I have taken Gross domestic product growth as a dependent variable which is to be explained by Total deaths due to terrorists activities which shows the intensity of terrorism activities each year. Greater the deaths in the specific year due to terrorism higher the intensity of terrorism. The second regressor is Inflation which tells the impact of inflation on GDP growth. Third independent variable I took is Military expenditure in terms of percentage to GDP, which tells that increase in military expenditure would reduce terrorism incidents having a positive impact upon gross domestic product growth.

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Methodology:

I have obtain time series data of Gross Domestic Product (GDP) growth rate, Percentage of GDP to defense expenditure, Inflation (in percentage), Total Deaths due to terrorism incidents, Karachi stock exchange All index values from the year 2003 till 2014. Taken log of Gross domestic product growth as an dependent variable, log of percentage of GDP to defense expenditure, log of inflation, total deaths in terrorism incident during specified period and Karachi stock exchange All index value as an independent variables. I have collected these data from various sources such as World Bank and others.

After obtaining the data I have run the regression analysis obtaining coefficients of each variable explaining the dependent variable which is Gross domestic product growth. Following is the regression model:

Loggdpg= B+Loginflation+ tdeaths+ logkseall+ logMilexp

Variables:

Independent Variable:

logInflation tdeaths logkseall logMilexp

Dependent Variable:

Loggdpg

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Literature Review:

In a research report “Linkage between inflation, economic growth & terrorism in Pakistan” written by Mr. Muhammad Shahbaz of COMSAT Institute of Science & Technology Lahore, investigates] linkages between inflation, economic growth and terrorism using annual frequency data over the period of 1971–2010,. The empirical evidence confirms the cointegration between inflation, economic growth and terrorism in Pakistan. An increase in inflation raises terrorist attacks while economic growth is also a major contributor to terrorism. Moreover, bidirectional causality is found between inflation and terrorism as investigated by the VECM Granger-causality approach while variance decomposition approach also supports the findings by the VECM Granger causality analysis. Our results therefore points to benefits of pursuing sustainability of low inflation in reducing terrorism. However, it also implies some difficulties for policy-makers in Pakistan in their pursuit for economic growth as latter would result in an increase in terrorism activities crowding out some of the benefits of economic growth.

In a research report titled “Impact of Terrorism, Gas shortage & Political instability on FDI inflows in Pakistan” Mr Zeshan Anwar and Talat Afzal of management sciences department COMSAT institute of Information & Technology Lahore and Sahiwal. The inflows of Foreign Direct Investment (FDI) has been verified as a crucial investment source in developing nations because it assists to decrease unemployment, bridging gap in savings and investment, transferring updated technology and eventually enhancing host countries’ level of economic growth. The objective of this study is to empirically investigate the FDI determinants in Pakistan from the year of 1980 to 2010 through utilizing annual time series dataset. This is the first study to test the impact of terrorism, gas shortage and political instability (together with control variables which include inflation, GDP, trade openness, exchange rate and investors’ incentives) on inflows of FDI in Pakistan through utilizing ARMA research model and OLS regression technique. As anticipated, the findings confirmed that political instability and terrorism have

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negative influence whereas gas generation has positive affect on Pakistan’s FDI inflows. The control variables of GDP, incentives offered to investors and trade oppeness have positively affected FDI inflows whereas exchange rate and inflation rate have negative association with FDI inflows.

“ Acts Of Terrorism And Their Impacts On Stock Index Returns And Volatility: The Cases Of The Karachi And Tehran Stock Exchanges” written by Anh Phuong Nguyen, New Mexico State University and Carl E. Enomoto, New Mexico State University Published in International Business & Economics Research JournalVolume 8, Number 12. This research paper investigates Terrorist attacks throughout the world have disrupted the flow of financial capital between nations and affected incomes, company profits and stock prices. This paper uses a GARCH(1,1) model to determine how these attacks have affected two specific stock markets: one in Pakistan and the other in Iran. It was found that significant, but different, stock index return shifts and changes in volatility occurred in the two markets. These effects on stock returns have important implications for the economies involved and provide information about investor reaction to terrorism.

Research paper “Terrorism & Stock market “ written by G. Andrew Karolyi and Rodolfo Martell examines the stock price impact of terrorist attacks. Using an official list of terrorism-related incidents compiled by the Counter terrorism Office of the U.S. Department of State, they identified 75 attacks between 1995 and 2002 in which publicly traded firms are targets. An event-study analysis around the day of the attacks uncovers evidence of a statistically significant negative stock price reaction of -0.83% which corresponds to an average loss per firm per attack of $401 million in firm market capitalization. Across sectional analysis of the abnormal returns indicates that the impact of terrorist attacks differs according to the home country of the target firm and the country in which the incident occurred. Attacks in countries that are wealthier and more democratic are associated with larger negative share price reactions. Most interestingly,author found that human capital losses, such as kidnappings of company executives, are associated with larger negative stock price reactions than physical losses, such as bombings of facilities or buildings. We discuss the implications of these findings for existing research on terrorism and for current policy debates regarding terrorism re-insurance programs.

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Regression Analysis:I have run Regression Analysis taking dependent variable of log of Gross domestic product growth and independent variables total deaths, log of KSE All index, log of inflation and log of military expenditure in terms of GDP.

Date: 12/25/15 Time: 12:37Sample (adjusted): 2005 2014Included observations: 10 after adjustments

Variable Coefficient Std. Error t-Statistic Prob.

C -12.65464 12.31304 -1.027744 0.3512TDEATHS -5.23E-05 0.000267 -0.196113 0.8522

LOGKSEALL 0.644268 0.544909 1.182341 0.2902LOGINFLATION 0.245886 0.581341 0.422963 0.6899

LOGMILEXP 6.098189 5.537931 1.101167 0.3210

R-squared 0.546543 Mean dependent var 1.291777Adjusted R-squared 0.183777 S.D. dependent var 0.527016S.E. of regression 0.476133 Akaike info criterion 1.660614Sum squared resid 1.133513 Schwarz criterion 1.811906Log likelihood -3.303069 Hannan-Quinn criter. 1.494646F-statistic 1.506599 Durbin-Watson stat 3.103718Prob(F-statistic) 0.327658

Interpretation:Increase in the death due to terrorism would decrease GDP growth by 5.23%, increase in KSE All index by 1% would lead to increase in GDP growth rate by 0.644%, increase in 1% of inflation would increase gross domestic product growth with 0.245% and 1% increase in military expenditure would lead 6.09% to GDP growth rate. Increase in Military expenditure would decrease terrorism consequently and hence GDP growth will increase. R square of this model is 0.546 which is 54.6% means that the model is able to explain only 54.6% of variation rest is define by other variables. All the variables are statistically insignificant because the pvalue is greater than 0.05 might be because of limited data or the variables are not enough to explain regressand.

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Graphs:

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Multicolinearity Detection:

Date: 12/25/15 Time: 12:37Sample (adjusted): 2005 2014Included observations: 10 after adjustments


C -12.65464 12.31304 -1.027744 0.3512TDEATHS -5.23E-05 0.000267 -0.196113 0.8522

LOGKSEALL 0.644268 0.544909 1.182341 0.2902LOGINFLATION 0.245886 0.581341 0.422963 0.6899

LOGMILEXP 6.098189 5.537931 1.101167 0.3210


Analysis:Since the R square is 54.65 % which is not very high or not very low, on the contrary none of the variable is significant explains that there is no sign of multicolinearity in the model.

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Heteroscedacity test:

Heteroskedasticity Test: Breusch-Pagan-Godfrey

F-statistic 0.234149 Prob. F(4,5) 0.9078Obs*R-squared 1.577663 Prob. Chi-Square(4) 0.8128Scaled explained SS 0.401112 Prob. Chi-Square(4) 0.9824

Test Equation:Dependent Variable: RESID^2Method: Least SquaresDate: 12/25/15 Time: 12:42Sample: 2005 2014Included observations: 10


C 3.251826 5.425823 0.599324 0.5751TDEATHS -3.37E-05 0.000118 -0.286377 0.7861

LOGKSEALL -0.141200 0.240118 -0.588047 0.5821LOGINFLATION -0.033043 0.256172 -0.128989 0.9024

LOGMILEXP -1.350390 2.440327 -0.553364 0.6038

R-squared 0.157766 Mean dependent var 0.113351Adjusted R-squared -0.516021 S.D. dependent var 0.170403S.E. of regression 0.209811 Akaike info criterion 0.021636Sum squared resid 0.220104 Schwarz criterion 0.172929Log likelihood 4.891820 Hannan-Quinn criter. -0.144332F-statistic 0.234149 Durbin-Watson stat 2.448612Prob(F-statistic) 0.907766

Analysis:Since the F statistics of the Breusch-Pagan-Godfrey test which is used to test the heteroskedacity in the model is small and greater than pvalue 0.05 which means the test is statistically insignificant so there is no sign of heteroskecedasity in the model.

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Omitted variables test:H0= Restricted model is not appropriate

H1= Restricted Model is appropriate

Ramsey RESET TestEquation: EQ01Specification: LOGGDPG C TDEATHS LOGKSEALL LOGINFLATION LOGMILEXPOmitted Variables: Squares of fitted values

Value df Probabilityt-statistic 0.875779 4 0.4306F-statistic 0.766988 (1, 4) 0.4306Likelihood ratio 1.754204 1 0.1853

F-test summary:

Sum of Sq. dfMean

SquaresTest SSR 0.182378 1 0.182378Restricted SSR 1.133513 5 0.226703Unrestricted SSR 0.951136 4 0.237784Unrestricted SSR 0.951136 4 0.237784

LR test summary:Value df

Restricted LogL -3.303069 5Unrestricted LogL -2.425968 4

Unrestricted Test Equation:Dependent Variable: LOGGDPGMethod: Least SquaresDate: 12/25/15 Time: 12:47Sample: 2005 2014Included observations: 10


C 22.99411 42.61380 0.539593 0.6181TDEATHS 7.15E-05 0.000308 0.232347 0.8277

LOGKSEALL -0.900134 1.849658 -0.486649 0.6520LOGINFLATION -0.454298 0.996833 -0.455741 0.6722

LOGMILEXP -11.58490 20.97273 -0.552379 0.6101FITTED^2 1.047991 1.196640 0.875779 0.4306


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Analysis:Since the P value of the F statistics is greater than 0.05 we reject Ho and accept H1 which means that the restricted model is appropriate there is no need of any additional variable.

Test of Stationary:Date: 12/25/15 Time: 14:06Sample: 2003 2014Included observations: 10

Autocorrelation Partial Correlation AC PAC Q-Stat Prob

.****| . | .****| . | 1 -0.555 -0.555 4.1054 0.043 . |** . | . *| . | 2 0.245 -0.092 5.0022 0.082 . | . | . | . | 3 -0.055 0.061 5.0542 0.168 . *| . | . **| . | 4 -0.197 -0.267 5.8280 0.212 . | . | . ***| . | 5 0.007 -0.370 5.8293 0.323 . | . | . **| . | 6 -0.003 -0.206 5.8296 0.443 . | . | . | . | 7 0.039 -0.017 5.8893 0.553 . | . | . *| . | 8 0.018 -0.068 5.9088 0.657 . | . | . **| . | 9 0.001 -0.211 5.9090 0.749

Analysis:Since the data is not following regular pattern which means that the data is stationary.

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Unit Root Tests:

Null Hypothesis: LOGGDPG has a unit rootExogenous: ConstantLag Length: 2 (Automatic - based on SIC, maxlag=2)

t-Statistic Prob.*

Augmented Dickey-Fuller test statistic -2.539125 0.1382Test critical values: 1% level -4.420595

5% level -3.25980810% level -2.771129

*MacKinnon (1996) one-sided p-values.Warning: Probabilities and critical values calculated for 20 observations and may not be accurate for a sample size of 9

Augmented Dickey-Fuller Test EquationDependent Variable: D(LOGGDPG)Method: Least SquaresDate: 12/25/15 Time: 15:01Sample (adjusted): 2006 2014Included observations: 9 after adjustments


LOGGDPG(-1) -0.760273 0.299423 -2.539125 0.0519D(LOGGDPG(-1)) 0.189799 0.318170 0.596534 0.5768D(LOGGDPG(-2)) 0.598319 0.292119 2.048206 0.0959

C 0.942209 0.408213 2.308130 0.0691

R-squared 0.644500 Mean dependent var -0.038709Adjusted R-squared 0.431201 S.D. dependent var 0.525115S.E. of regression 0.396035 Akaike info criterion 1.286474Sum squared resid 0.784219 Schwarz criterion 1.374130Log likelihood -1.789135 Hannan-Quinn criter. 1.097314F-statistic 3.021570 Durbin-Watson stat 3.072489Prob(F-statistic) 0.132427

Interpretation:

Since the P value of Fstatistics is more than 0.05 this means that the data follows unit root process.

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Null Hypothesis: LOGINFLATION has a unit rootExogenous: ConstantLag Length: 0 (Automatic - based on SIC, maxlag=2)

t-Statistic Prob.*


5% level -3.17535210% level -2.728985


Augmented Dickey-Fuller Test EquationDependent Variable: D(LOGINFLATION)Method: Least SquaresDate: 12/25/15 Time: 15:03Sample (adjusted): 2004 2014Included observations: 11 after adjustments


LOGINFLATION(-1) -1.000407 0.298213 -3.354671 0.0085C 2.333821 0.699049 3.338563 0.0087


Interpretation:

Since the P value of F statistics is less than 0.05 this means that the log inflation does not follows unit root process.

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Null Hypothesis: LOGKSEALL has a unit rootExogenous: ConstantLag Length: 0 (Automatic - based on SIC, maxlag=1)

t-Statistic Prob.*


5% level -3.25980810% level -2.771129


Augmented Dickey-Fuller Test EquationDependent Variable: D(LOGKSEALL)Method: Least SquaresDate: 12/25/15 Time: 15:03Sample (adjusted): 2006 2014Included observations: 9 after adjustments


LOGKSEALL(-1) -0.118423 0.333920 -0.354645 0.7333C 1.230505 3.000104 0.410154 0.6940

R-squared 0.017650 Mean dependent var 0.167257Adjusted R-squared -0.122685 S.D. dependent var 0.313144S.E. of regression 0.331798 Akaike info criterion 0.824549Sum squared resid 0.770629 Schwarz criterion 0.868376Log likelihood -1.710470 Hannan-Quinn criter. 0.729969F-statistic 0.125773 Durbin-Watson stat 1.440317Prob(F-statistic) 0.733300

Interpretation:

Since the P value of Fstatistics is greater than 0.05 this means that the data follows unit root process.

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Null Hypothesis: LOGMILEXP has a unit rootExogenous: ConstantLag Length: 0 (Automatic - based on SIC, maxlag=2)

t-Statistic Prob.*


5% level -3.17535210% level -2.728985


Augmented Dickey-Fuller Test EquationDependent Variable: D(LOGMILEXP)Method: Least SquaresDate: 12/25/15 Time: 15:04Sample (adjusted): 2004 2014Included observations: 11 after adjustments


LOGMILEXP(-1) -0.275408 0.134829 -2.042650 0.0715C 0.333108 0.175234 1.900929 0.0898

R-squared 0.316754 Mean dependent var -0.023709Adjusted R-squared 0.240838 S.D. dependent var 0.052864S.E. of regression 0.046061 Akaike info criterion -3.154749Sum squared resid 0.019094 Schwarz criterion -3.082404Log likelihood 19.35112 Hannan-Quinn criter. -3.200352F-statistic 4.172421 Durbin-Watson stat 2.515558Prob(F-statistic) 0.071458

Interpretation:

Since the Pvalue of Fstatistics is greater than 0.05 this means that the data follows unit root process.

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Null Hypothesis: TDEATHS has a unit rootExogenous: ConstantLag Length: 0 (Automatic - based on SIC, maxlag=2)

t-Statistic Prob.*


5% level -3.17535210% level -2.728985


Augmented Dickey-Fuller Test EquationDependent Variable: D(TDEATHS)Method: Least SquaresDate: 12/25/15 Time: 15:07Sample (adjusted): 2004 2014Included observations: 11 after adjustments


TDEATHS(-1) -0.290440 0.172411 -1.684577 0.1264C 820.0066 434.3486 1.887900 0.0916

R-squared 0.239723 Mean dependent var 195.4545Adjusted R-squared 0.155248 S.D. dependent var 816.5617S.E. of regression 750.5049 Akaike info criterion 16.24234Sum squared resid 5069319. Schwarz criterion 16.31468Log likelihood -87.33284 Hannan-Quinn criter. 16.19673F-statistic 2.837798 Durbin-Watson stat 1.847679Prob(F-statistic) 0.126355

Interpretation:

Since the Pvalue of F statistics is greater than 0.05 this means that the data follows unit root process.

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Conclusion:

Hence it is concluded that terrorism have an adverse relationship with gross domestic product growth since deaths due to terrorism incidents have a negative impact on GDP growth whereas inflation, Karachi stock exchange all index have a positive impact on gross domestic product growth and military expenditure also have a positive impact on GDP growth because it will curtail the terrorism incidents and so it would have a positive effect on economy but all the independent variable were statistically insignificant might be because of limited data or missing variable; however; regression coefficients were according to the theory.

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Sources of Data:

http://www.ciitlahore.edu.pk/Papers/Abstracts/430-8588307547197119558.pdf http://journalistsresource.org/studies/international/conflicts/relationship-between-economic-

growth-terrorism-new-research http://www.sciencedirect.com/science/article/pii/S0264999313000643

Data:

World Bank http://www.satp.org/satporgtp/countries/pakistan/database/casualties.htm

http://www.satp.org/satporgtp/countries/pakistan/database/casualties.htm

http://www.sciencedirect.com/science/article/pii/S0264999313000643

http://journalistsresource.org/studies/international/conflicts/relationship-between-economic-growth-terrorism-new-research

http://journalistsresource.org/studies/international/conflicts/relationship-between-economic-growth-terrorism-new-research

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