The Impacts of Financial Crisis on Pakistan Economy: An Empirical Approach

International Journal of Empirical Finance Vol. 4, No. 5, 2015, 258-269 The Impacts of Financial Crisis on Pakistan Economy: An Empirical Approach Khalid Mughal 1, Irfan Khan 2, Farhat Usman 3 Abstract Financial crisis is an economic situation in which the economy of a country faces some unanticipated downturn or recession, price fluctuations, current account deficits and uncertainty on foreign sector. Main objective of this paper is to measure the long run econometric association between key macroeconomic indicators of financial crisis in Pakistan economy. Secondary data was collected from the annual reports of state bank of Pakistan, economic surveys (2000-2014) and from the IMF database for the period of 2000 to 2014 which latterly transformed into quarterly data for better estimation. Vector Auto-regression (VAR) estimation with the lag length of one with all related applications have been employed in this research to check the long run association among the variables included in the research. Result explains the significant long run associations among the GDP, Inflation, Balance of Trade and Current Account Balance. After applying the augmented Dickey Fuller test it is revealed that the data is facing the problem of non-stationary. To make the data stationary, first difference has been used to make the data stationary and enabling it for further uses. Using the results of VAR estimation system equations also have been generated. In order to check the individual significance of the equations ordinary least square method (OLS) has been used in the research. Result explains that most of the probability values are less than five percent. Further all the R squared values relevant to estimated model are quite high which represent the goodness of fit. The corresponding probability value of F statistic, is less than five percent for all the cases, witnessed the jointly significance of the variables included in the study. Keywords: Financial Crisis, GDP, Inflation, Current Account Balance, Balance of Trade, Pakistan. 1. Introduction After the Great Depression of 1930 an International Financial Crisis of 2008 was the most horrible and dangerous. The consequences of this international financial crisis seriously affected the world economies. Developing countries of the world were further dragged into poverty trap and total output of the world declined up to extreme level. Many steps have been taken across the world to minimize the effects of this crisis and the announcement of bailout package of worth trillion of US dollars by the United State of America (USA) was the prime example in this regard. The foremost reasons behind the international financial crisis of 2008 were rise in prices of asset, collapse of regulatory framework and credit booms. The international financial crisis of 2008 also largely affected the economy of Pakistan which was already facing grate macroeconomic disparities and imbalances. The world crises further pushed Pakistan economy into financial crises. Macroeconomic symptoms of economic growth have shown very poor performance. Growth in Gross Domestic Product (GDP) tremendously declined. GDP which was recorded 5 percent in fiscal year 2007 declined to 0.40 percent in very next year in 2008 (Pakistan Economic Survey, 2007-08). 1 Faculty, Preston University Islamabad, Pakistan 2 M Phil scholar, Preston University Islamabad, Pakistan 3 M Phil scholar, Preston University Islamabad, Pakistan 2015 Research Academy of Social Sciences http://www.rassweb.com 258

International Journal of Empirical Finance Fiscal and current account deficit reached the highest, foreign direct investment came down, trade gap and trade deficit were widened and inflation rose sharply. However in order to achieve macroeconomic stability and to pull the economy again on the track, government of Pakistan has announced a tight monetary policy which properly pursued by the State Bank of Pakistan. Concept and Definition Table 1.1: Previous Year s GDP Growth of Pakistan (%) Years GDP Growth (%) 2006-07 5.50 2007-08 5.00 2008-09 0.40 2009-10 2.60 2010-11 3.62 2011-12 3.84 2012-13 3.70 2013-14 4.14 Source: (Economic Survey, 2006-2014) Financial crisis is a situation where economy faces unanticipated recession experiencing uncertainty, current account deficits and fall in GDP. According to Slaessens and Kose (2011) a financial crisis is normally related with significant transformations in the volume of credit and prices of assets, harsh interruptions in the financial intermediations, provision of outdoor financing to different sectors of the economy, troubled balance sheet issues and large scale support of government in the form of recapitalization and liquidity support. Scholar s view about the Paper Khawaja et al. (2007) argued that economic basics which prevailed before the financial crisis could not authorize an activist fiscal policy to deal with the crisis. The direct impact of the international financial crisis on low income countries like Pakistan has been partial because of absence of integration in the domestic financial sector with the international financial sector (IMF, 2009). According to Daraz (2011) china has faced extreme financial crises and domestic economic problems as compared to Pakistan. A recent world financial crisis has negatively affected the trade growth in Pakistan (Latif et al, 2011). Yakubu and Akerele (2012) conclude that global financial crisis of 2008 has no significant effects on the Nigerian stock exchange. Objectives of the Study The central objective of the present research is to econometrically estimate the long run associations among the key macroeconomic indicators of financial crisis i.e. gross domestic product, inflation, current account balance and balance of trade. 2. Review of Literature Khawja et al. (2007) investigated the impacts of global financial crisis and suggest some policy implications for Pakistan economy. They scrutinized that global financial crisis has emphasized the economic challenges with increasing the current account and fiscal deficits, high inflation and poor economic growth and development. It was recommended that initial conditions before the economic crisis would determine the policy response and political will might play an important role in macroeconomic outcome of the country. Further government could struggle to decrease the public expenditures and might take some steps towards the enhancement of public revenues. Government of Pakistan might think about reductions in 259

K. Mughal et al. subsidies on electricity and gas, develop the competency of public development spending through better project supervising and implementations and better tax reforms. Azam et al. (2010) studied the time series relationship of financial crisis of 2008 and economic growth of Pakistan for the period of 1972 to 2010. The main objective of the research was to expose the relationship among key indicators of financial crisis economic growth and the stability of that relationship. Annual time series data was collected from the sample period economic surveys of Pakistan. Johansen s Co-integration test was incorporated to confirm the long run associations among the variables included in the study. It was found that there was a long run stable equilibrium association among economic growth and all the elements of the financial crisis in Pakistan. It was concluded that only foreign debt and interest rate has co-integration among them and gross domestic product has long run association among all the variables included in the research except foreign debt. Latif et al. (2011) studied the global financial crisis by linking with the growth and development in the agriculture sector of Pakistan economy. Secondary data was collected from the annual statistical reports regarding the agriculture and various economic surveys of Pakistan. Social software EVIEW has been incorporated to empirically analyze the data. Multiple regression analysis has been used in the study to answer the research question. Result explains the negative and inverse impacts of global financial crisis on the growth of trade in Pakistan. Further global financial crisis badly effect exports of the country. Nazir et al. (2012) checked the impacts of global financial crisis of 2008 on the financial performance of the banking sector of Pakistan. Main objective of the study was to analyze the different determinants of the financial performance of the banks in Pakistan. Secondary data was collected from the several economic surveys and annual performance reviews of state bank of Pakistan. To empirically analyze the data stepwise multiple regression analysis has been employed in the research. It was found that quality of assets was most significant determinant of return over assets followed by the size of bank and solvency. The assessment of pre and post crisis has exerted major impacts on the virtual capability of the financial performance determinants to elucidate the fluctuations in return over assets. It was concluded that low asset quality and deposits have inversely affected the financial performance of the banking sector of Pakistan. On the other hand size, solvency, advances, liquidity and the investments have positive effects of the financial performance of the banking sector of Pakistan. Yakubu and Akerele (2012) analyzed the impacts of global financial crisis on the stock exchange of Nigeria for the period of 2008 to 2011. They have used market capitalization as a proxy of stock exchange of Nigeria. And at the same time capital inflow and foreign exchange rate has been used as a proxy for global financial crisis. Secondary data was collected from the central bank of Nigeria. Multiple regression analysis using ordinary least square methodology has been employed in the study. Result explains the insignificant effects of global financial crisis on stock exchange of Nigeria. It was suggested to the government of Nigeria to create such measures so that confidence of investors could be to better off and economic activities in the economy may be improved in order to contribute into the economy of Nigeria. 3. Theoretical Framework Term fiscal policy has been used in this study as a policy tool of macroeconomic strategies in an economy for public spending, taxation or other facilities provided by the government to private sector. While tight fiscal policy refers to limit the effective demand and easy fiscal policy refers to strategies of cutting taxes, raising government expenditures without taking care of budget deficit. Term monetary policy is used as a tool in this research to control the supply of money in order to influence the economy. Similarly tight monetary policy refers to limit the effective demand of money by raising the mark up rate and easy monetary policy refers to provision of loans on low interest rates. Current account deficit refers to the surplus of government and private spending over revenues or receipts. Balance of payment measures the monetary deals with rest of the world for some specific period. 260

4. Research Methodology International Journal of Empirical Finance The present research is secondary and applied in nature. Annual time series data have been collected from the state bank of Pakistan, Pakistan economic surveys (2000-2014) and from the IMF World Economic database for the period of 2000 to 2014. In order to generate better results annual time series data has been converted into quarterly time series data using Eviews 6.0. Social software Eviews 6.0 has been incorporated in the research to analyze the data. Vector Auto-regression (VAR) estimation with the lag length of two (as advised by the lag order selection criteria) with all related application has been employed in this research to check the long run association among the variables included in the model. After applying the augmented Dickey Fuller test it is revealed that the data has facing the problem of non-stationary. To make the data stationary, first difference has been carried out to make the data stationary and enabling the data for further uses. Using the results of VAR estimation system equations also has been generated to check the individual significance of the equations using OLS. 5. Statistical Estimations Table 5.1: Summary Results of ADF Test Variables At At 1 st Difference Intercept Trend and None Intercept Trend and None Intercept Intercept GDP -2.047-2.107-0.808-7.484-7.423-7.549 Critical Value at 1 % -3.546-4.121-2.605-3.548-4.112-2.605 Critical Value at 5 % -2.912-3.488-1.946-2.913-3.489-1.947 Critical Value at 10 % -2.594-3.172-1.632-2.594-3.173-1.613 Inflation -1.889-1.913-0.667-7.484-7.455-7.549 Critical Value at 1 % -3.546-4.121-2.605-3.548-4.124-2.605 Critical Value at 5 % -2.912-3.488-1.946-2.913-3.489-1.947 Critical Value at 10 % -2.594-3.172-1.613-2.594-3.173-1.613 CAB -1.347-1.233-1.079-7.486-7.456-7.550 Critical Value at 1 % -3.546-4.121-2.605-3.548-4.124-2.605 Critical Value at 5 % -2.912-3.488-1.946-2.913-3.489-1.947 Critical Value at 10 % -2.594-3.172-1.613-2.594-3.173-1.613 BOT -2.395-2.464-2.190-7.484-7.431-7.550 Critical Value at 1 % -3.546-4.121-2.605-3.548-4.124-2.605 Critical Value at 5 % -2.912-3.488-1.946-2.913-3.489-1.947 Critical Value at 10 % 2.594-3.172-1.613-2.594-3.173-1.613 261

K. Mughal et al. Table 5.2: Lag Order Selection Criteria using VAR VAR Lag Order Selection Criteria Endogenous variables: GDP INFLATION BOT CAB Exogenous variables: C Date: 02/25/15 Time: 00:18 Sample: 2000Q1 2014Q4 Included observations: 56 Lag LogL LR FPE AIC SC HQ 0-675.1962 NA 402525.3 24.25701 24.40167 24.31309 1-513.8556 293.8703* 2245.474* 19.06627* 19.78961* 19.34671* 2-509.4922 7.324345 3434.410 19.48186 20.78388 19.98665 3-501.8700 11.70552 4741.201 19.78107 21.66176 20.51021 4-483.3682 25.77039 4533.422 19.69172 22.15108 20.64521 * 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 5.3: Summary Results of Unrestricted VAR Model Vector Auto-regression Estimates Date: 02/25/15 Time: 00:55 Sample (adjusted): 2000Q2 2014Q4 Included observations: 59 after adjustments Standard errors in ( ) & t-statistics in [ ] GDP INFLATION BOT CAB GDP(-1) 0.707646 0.250845 7.046696-0.452369 (0.11026) (0.28651) (5.15416) (0.21242) [ 6.41814] [ 0.87553] [ 1.36719] [-2.12961] INFLATION(-1) 0.029302 0.739786 2.551848 0.024010 (0.05056) (0.13139) (2.36374) (0.09742) [ 0.57949] [ 5.63026] [ 1.07958] [ 0.24646] BOT(-1) 0.003029-0.001562 0.741646 5.73E-05 (0.00199) (0.00517) (0.09302) (0.00383) [ 1.52211] [-0.30207] [ 7.97325] [ 0.01494] CAB(-1) 0.087883-0.233613 2.096245 1.058359 (0.04402) (0.11438) (2.05774) (0.08481) [ 1.99648] [-2.04234] [ 1.01871] [ 12.4798] 262

International Journal of Empirical Finance C 1.089392 0.715308-38.55053 1.777572 (0.71019) (1.84546) (33.1991) (1.36824) [ 1.53394] [ 0.38760] [-1.16119] [ 1.29917] R-squared 0.777281 0.813715 0.687717 0.904456 Adj. R-squared 0.760784 0.799916 0.664585 0.897379 Sum sq. resids 31.42877 212.2193 68679.85 116.6541 S.E. equation 0.762899 1.982420 35.66299 1.469783 F-statistic 47.11456 58.96967 29.73003 127.7962 Log likelihood -65.13787-121.4798-291.9777-103.8268 Akaike AIC 2.377555 4.287451 10.06704 3.689045 Schwarz SC 2.553617 4.463514 10.24310 3.865107 Mean dependent 4.091525 8.918644 28.05797-2.833508 S.D. dependent 1.559809 4.431897 61.57814 4.588117 Determinant resid covariance (dof adj.) 1278.470 Determinant resid covariance 897.1344 Log likelihood -535.4461 Akaike information criterion 18.82868 Schwarz criterion 19.53293 Table 5.4: Summary Results of System Equations using VAR System: UNTITLED Estimation Method: Least Squares Date: 02/25/15 Time: 00:56 Sample: 2000Q2 2014Q4 Included observations: 59 Total system (balanced) observations 236 Coefficient Std. Error t-statistic Prob. C(1) 0.707646 0.110257 6.418140 0.0000 C(2) 0.029302 0.050565 0.579494 0.5629 C(3) 0.003029 0.001990 1.522105 0.1294 C(4) 0.087883 0.044019 1.996484 0.0471 C(5) 1.089392 0.710192 1.533940 0.1265 C(6) 0.250845 0.286507 0.875528 0.3823 C(7) 0.739786 0.131395 5.630258 0.0000 C(8) -0.001562 0.005171-0.302072 0.7629 C(9) -0.233613 0.114385-2.042341 0.0423 C(10) 0.715308 1.845460 0.387604 0.6987 C(11) 7.046696 5.154160 1.367186 0.1730 C(12) 2.551848 2.363740 1.079580 0.2815 C(13) 0.741646 0.093017 7.973250 0.0000 C(14) 2.096245 2.057743 1.018711 0.3095 C(15) -38.55053 33.19915-1.161190 0.2468 C(16) -0.452369 0.212419-2.129606 0.0343 C(17) 0.024010 0.097417 0.246464 0.8056 C(18) 5.73E-05 0.003834 0.014940 0.9881 263

K. Mughal et al. C(19) 1.058359 0.084806 12.47976 0.0000 C(20) 1.777572 1.368240 1.299167 0.1953 Determinant residual covariance 897.1344 Equation: GDP = C(1)*GDP(-1) + C(2)*INFLATION(-1) + C(3)*BOT(-1) + C(4) *CAB(-1) + C(5) Observations: 59 R-squared 0.777281 Mean dependent var 4.091525 Adjusted R-squared 0.760784 S.D. dependent var 1.559809 S.E. of regression 0.762899 Sum squared resid 31.42877 Durbin-Watson stat 1.938802 Equation: INFLATION = C(6)*GDP(-1) + C(7)*INFLATION(-1) + C(8)*BOT(-1) + C(9)*CAB(-1) + C(10) Observations: 59 R-squared 0.813715 Mean dependent var 8.918644 Adjusted R-squared 0.799916 S.D. dependent var 4.431897 S.E. of regression 1.982420 Sum squared resid 212.2193 Durbin-Watson stat 1.904352 Equation: BOT = C(11)*GDP(-1) + C(12)*INFLATION(-1) + C(13)*BOT(-1) + C(14)*CAB(-1) + C(15) Observations: 59 R-squared 0.687717 Mean dependent var 28.05797 Adjusted R-squared 0.664585 S.D. dependent var 61.57814 S.E. of regression 35.66299 Sum squared resid 68679.85 Durbin-Watson stat 1.848841 Equation: CAB = C(16)*GDP(-1) + C(17)*INFLATION(-1) + C(18)*BOT(-1) + C(19)*CAB(-1) + C(20) Observations: 59 R-squared 0.904456 Mean dependent var -2.833508 Adjusted R-squared 0.897379 S.D. dependent var 4.588117 S.E. of regression 1.469783 Sum squared resid 116.6541 Durbin-Watson stat 2.133519 264

International Journal of Empirical Finance Table 5.5: Individually Significance of GDP as Dependent Variable using VAR Dependent Variable: GDP Method: Least Squares Date: 02/25/15 Time: 01:10 Sample (adjusted): 2000Q2 2014Q4 Included observations: 59 after adjustments GDP = C(1)*GDP(-1) + C(2)*INFLATION(-1) + C(3)*BOT(-1) + C(4) *CAB(-1) + C(5) Coefficient Std. Error t-statistic Prob. C(1) 0.707646 0.110257 6.418140 0.0000 C(2) 0.029302 0.050565 0.579494 0.5647 C(3) 0.003029 0.001990 1.522105 0.1338 C(4) 0.087883 0.044019 1.996484 0.0509 C(5) 1.089392 0.710192 1.533940 0.1309 R-squared 0.777281 Mean dependent var 4.091525 Adjusted R-squared 0.760784 S.D. dependent var 1.559809 S.E. of regression 0.762899 Akaike info criterion 2.377555 Sum squared resid 31.42877 Schwarz criterion 2.553617 Log likelihood -65.13787 Hannan-Quinn criter. 2.446283 F-statistic 47.11456 Durbin-Watson stat 1.938802 Prob(F-statistic) 0.000000 Table 5.6: Individually Significance of Inflation as Dependent Variable using VAR Dependent Variable: INFLATION Method: Least Squares Date: 02/25/15 Time: 01:14 Sample (adjusted): 2000Q2 2014Q4 Included observations: 59 after adjustments INFLATION = C(6)*GDP(-1) + C(7)*INFLATION(-1) + C(8)*BOT(-1)+ C(9) *CAB(-1) + C(10) Coefficient Std. Error t-statistic Prob. C(6) 0.250845 0.286507 0.875528 0.3852 C(7) 0.739786 0.131395 5.630258 0.0000 C(8) -0.001562 0.005171-0.302072 0.7638 C(9) -0.233613 0.114385-2.042341 0.0460 C(10) 0.715308 1.845460 0.387604 0.6998 R-squared 0.813715 Mean dependent var 8.918644 Adjusted R-squared 0.799916 S.D. dependent var 4.431897 S.E. of regression 1.982420 Akaike info criterion 4.287451 Sum squared resid 212.2193 Schwarz criterion 4.463514 Log likelihood -121.4798 Hannan-Quinn criter. 4.356179 F-statistic 58.96967 Durbin-Watson stat 1.904352 Prob(F-statistic) 0.000000 265

K. Mughal et al. Table 5.7: Individually Significance of BOT as Dependent Variable using VAR Dependent Variable: BOT Method: Least Squares Date: 02/25/15 Time: 01:16 Sample (adjusted): 2000Q2 2014Q4 Included observations: 59 after adjustments BOT = C(11)*GDP(-1) + C(12)*INFLATION(-1) + C(13)*BOT(-1) +C(14) *CAB(-1) + C(15) Coefficient Std. Error t-statistic Prob. C(11) 7.046696 5.154160 1.367186 0.1772 C(12) 2.551848 2.363740 1.079580 0.2851 C(13) 0.741646 0.093017 7.973250 0.0000 C(14) 2.096245 2.057743 1.018711 0.3129 C(15) -38.55053 33.19915-1.161190 0.2507 R-squared 0.687717 Mean dependent var 28.05797 Adjusted R-squared 0.664585 S.D. dependent var 61.57814 S.E. of regression 35.66299 Akaike info criterion 10.06704 Sum squared resid 68679.85 Schwarz criterion 10.24310 Log likelihood -291.9777 Hannan-Quinn criter. 10.13577 F-statistic 29.73003 Durbin-Watson stat 1.848841 Prob(F-statistic) 0.000000 Table 5.8: Individually Significance of CAB as Dependent Variable using VAR Dependent Variable: CAB Method: Least Squares Date: 02/25/15 Time: 01:18 Sample (adjusted): 2000Q2 2014Q4 Included observations: 59 after adjustments CAB = C(16)*GDP(-1) + C(17)*INFLATION(-1) + C(18)*BOT(-1) +C(19) *CAB(-1) + C(20) Coefficient Std. Error t-statistic Prob. C(16) -0.452369 0.212419-2.129606 0.0378 C(17) 0.024010 0.097417 0.246464 0.8063 C(18) 5.73E-05 0.003834 0.014940 0.9881 C(19) 1.058359 0.084806 12.47976 0.0000 C(20) 1.777572 1.368240 1.299167 0.1994 R-squared 0.904456 Mean dependent var -2.833508 266

International Journal of Empirical Finance Adjusted R-squared 0.897379 S.D. dependent var 4.588117 S.E. of regression 1.469783 Akaike info criterion 3.689045 Sum squared resid 116.6541 Schwarz criterion 3.865107 Log likelihood -103.8268 Hannan-Quinn criter. 3.757772 F-statistic 127.7962 Durbin-Watson stat 2.133519 Prob(F-statistic) 0.000000 5. Results and Discussion Table 5.1 possesses the summary results of ADF test statistic. Literature on the time series stated that a time series may only be stationary when the value of ADF test statistics greater than the critical values of all the three (1%, 5%, 10%) levels. The results of ADF test statistic stated that all the variables are non stationary at level and stationary at first difference as all the values of ADF test statistics are less than their respective critical values. Table 5.2 represents the information regarding lag order selection criteria which has been carried in order to be advised by the different criterions that how much the lag length may be taken for further estimations. All the criterions like sequential modified LR test statistic, final prediction error, Akaike information criterion, Schwarz information criterion and Hannan-Quinn information criterion has advised the researcher to select the one lags and hence one lag length has been chosen in the entire statistical estimations. Table 5.3 possesses the results of VAR model regarding all the variables included in the model. Here in VAR model it is assumed as per advised by the literature on VAR that the entire four variables Gross Domestic Product (GDP), Inflation, Balance of Trade (BOT), and Current Account Balance (CAB) all are dependent variables. In other words four different models have been developed after employing VAR model. Result explained that there are four different models named GDP, Inflation, BOT and CAB. When the GDP is dependent variable GDP lag one (GDP(-1), Inflation lag one, BOT lag one, CAB lag one and constant (C) are the independent variables of this model. Similarly when Inflation is the dependent variable GDP lag one, Inflation lag one, BOT lag one, CAB lag one and constant are the independent variables of the model. When BOT is the dependent variable GDP lag one, Inflation lag one, BOT lag one, CAB lag one and constant are the independent variables of this model. Similarly when CAB is dependent variable GDP lag one, Inflation lag one, BOT lag one, CAB lag one and constant are the independent variables of the model with coefficient values and respective t-values. Every model has five coefficients and in this way the whole VAR system possesses twenty coefficients. In order to know whether these independent variables can influence the dependent variables or not? In other words whether these independent variables are significant variables to explain the dependent variables, probability value can give the answer? The system equation model has represented in table 5.4 which possesses the probability values. Literature on time series stated that less than five percent corresponding probability value of a coefficient for a particular variable is significant to explain the dependent variable. So here are twenty coefficients C(1) to C(20) have been estimated at a time. Here in system equation it can be seen that the corresponding probability values of most of the coefficients are very low and are less than five percent. So most of the variables are individually significant included in the research and in this way individual significance of all the variables can be checked. Table no 5.4 also contained four models at below where GDP, Inflation, BOT and CAB are the dependent variables respectively. To check statistically how are these models or whether these models statistically efficient, diagnostic checking has been made after estimating these models separately. Results of these estimated models have been shown from table 5.5 to 5.8. Result explained that most of the probability values are less then fiver percent. Further all the R squared values respected to relevant estimated model are quite high. Higher R squared values of all the models represents the goodness of fit. The corresponding 267

K. Mughal et al. probability value of F statistic, which is less than five percent for all the cases, witnessed the jointly significance of the variables included in the study. 6. Conclusion & Recommendations Pakistan s failing macroeconomic situations after the international financial crisis resultantly affect the GDP. The growth rate of GDP turn down a lot and reached to 0.40 2008-09 and in fiscal year 2009 it again rise and reached to 2.60 percent. The economy of Pakistan was already suffering the financial imbalances due to rise in oil prices and reducing foreign exchange reserves further enlarged the trade gap, increase the budget deficit, current account deficit and high inflation carried more financial challenges for the economy of Pakistan. The economy of Pakistan has no potential to accept the discretionary fiscal policy because here public debt and shrinking in taxes are already on top. In this situation government is not capable to finance the fiscal deficit and resultantly tax to GDP ratio is very low. On the basis of results of system equations and VAR model it can easily be concluded that GDP is one of the important measures of financial and macroeconomic stability. The results of system equations and VAR model have made it clear that Current Account Balance, Trade Deficit and even Inflation had an impact on GDP. Result explained that most of the probability values are less then fiver percent. Further all the R squared values respected to relevant estimated model are quite high. Higher R squared values of all the models represents the goodness of fit. The corresponding probability value of F statistic, which is less than five percent for all the cases, witnessed the jointly significance of the variables included in the study. The international financial crisis has revealed many ducks in the financial system of Pakistan economy. The crisis has furnished a lesson to so called sustained financial sector of Pakistan that it is an essential need of the time to improve the present financial mechanism and to make the regulatory authority more consistent. The international financial crisis badly damaged the macroeconomic stipulations of Pakistan economy. It is recommended to enhance and refurnish the action plans to deal with such crisis by reducing the fiscal and current account, trade deficits and to increase taxes to GDP. Acknowledgement Our deep sense of gratitude to Young Dr. Atta Ullah Khan, Assistant Professor (Faculty department of Economics, Preston University Kohat, Islamabad Campus) for their valuable guidance in the statistical analysis of data, interpretation of results and other encouragements in finalizing this paper. References Azam, R.I., Batool, I., Imran, R., Chani, M.I., Hunjra, A.I. & Jasra, J.M. (2010). Financial Crises and Economic Growth in Pakistan, Middle East Journal of Scientific Research: Volume 9(3): 425-430. Daraz, M.U. (2011). Impacts of Financial Crises on Pakistan and China: A Comparative Study of Six Decades, Journal of Global Business and Economics: Volume 3(1): 174-186. Govt. of Pakistan, (2000-2014). Economic Surveys of Pakistan, Ministry of Finance, Economic Wing, Islamabad Advisor s Khawja, I., Din, M.U. & Ghani, E. (2007). Global Financial Crisis: Policy Implications for Pakistan, NUML Journal of Management & Technology: Volume 9(1): 20-31. Latif, A., Nazir, M.S., Shah, M.Z. & Shaikh, F.M (2011). Global Financial Crisis: Macroeconomic Linkage to Pakistan s Agriculture, Asian Social Science Research Journal: Volume 7(7): 90-93. Nazir, M.S., Safdar, R. & Akram, M.I. (2012). Impact of Global Financial Crisis on Bank s Financial Performance in Pakistan, American Journal of Scientific Research: ISSN 2301-2005 (78):101-111. 268

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