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             \author[1]{Hussein Ali  Al-Zeaud}

             \affil[1]{  Al al-Bayt University}

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\date{\small \em Received: 12 February 2015 Accepted: 5 March 2015 Published: 15 March 2015}

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\begin{abstract}
        


This paper is an effort to explore the interaction between fiscal and monetary policies in Jordan through investigating the short-term and long-term relationship between government expenditure as a fiscal policy, money supply as a monetary policy, and inflation as a result and cause of these policies. Using annual data for the period 1990-2011, the study applies cointegration test, Granger-causality, and VECM techniques to inspect the long-term and shortterm relationship and the directions of causality, also the speed of adjustment toward long-term equilibrium between the variables.

\end{abstract}


\keywords{fiscal policy, monetary policy, government expenditure, money supply, inflation, granger-causality, co-integration, jordan}

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\let\tabcellsep& 	 	 		 \par
Introduction ordan has been experiencing economic deformations, which includes chronic deficits in the budget and trade account, high level of unemployment and public debt. Theoretically, there are a number of factors that cause economic deformations, so the government of Jordan has taken a number of fiscal and monetary measures as tools to tackle these problems and to achieve economic stabilization. Monetary policy is controlled by the Central Bank of Jordan, which is responsible to take on monetary measures to provide the appropriate amount of domestic liquidity (Money Supply) in order to encourage growth, and to sustain the stability of the Jordanian dinar. Tools of monetary policy include open market operations, discount policy and reserve requirements. These tools affects both real variables (real GDP, Unemployment ?.) and nominal variables (inflation and the exchange rate ?) in the short-term, but, in the longterm, only nominal variables could be controlled by these tools. On the other hand, Ministry of Finance is responsible for Fiscal policy, which involves deliberate changes in government expenditures and taxes to control economic activity. Government budget is the main tool through which fiscal policy is carried out. Government expenditures are a vital element of aggregate demand. So, it directly affect the level of economic activity; while transfer payments and taxes affect disposable income and consequently influence the other two major elements of aggregate demand, consumption and investment. Therefore, Fiscal policy works through changes in government budget like increasing expenditure to induce economic growth and creation of jobs. This may induce inflation and possible crowding out effect, which in turn affects interstate rate. Likewise, government expenditure is affected by Inflation and interest rate.\par
The interaction between fiscal and monetary policies and the different agencies responsible for implementation of these policies imply there are no exclusive effective fiscal and monetary measurements for dealing with these deformations, given that each policy has its own supporters. So, most countries have been adopting monetary policy, fiscal policy, or a mix of both policies to tackle their economic problems.\par
In the late 1980s, Jordan started to face serious economic problems, seeing that inflows of both official transfers and remittances fell, with substantial unfavorable consequences on budgetary revenues. Initially, the authorities resorted to external borrowing to fill the revenue gap, leading to a sharp increase in external debt. In 1989, Jordan had a 30-35 percent unemployment rate and was struggling with its inability to pay its loans. Structural weaknesses in Jordan's public finances and balance of payments were soon exposed, leading the authorities to request the country's first arrangement with the International Monetary Fund (IMF) in 1989 to facilitate orderly external financial relations and reverse a sharp decline in economic growth.\par
Thus, Economic reforms were initiated as part of an agreement between Jordan and the IMF. The key objectives of the economic reforms in the kingdom focused on the reduction of public debts, reduction of the government expenditures, controlling of inflation, tax reforms, credit policy reforms, investment incentives, privatization and easier trade policies. Nevertheless, a highly growing population and the unstable political situation in the region have been major impediments to immediately reaping the benefits of IMF-introduced economic reforms.\par
During 2011-2012 Jordan's economy has been suffering from exogenous shocks. Repeated and extensive disruption to the flow of natural gas from Egypt due to the damage of the Sinai Peninsula pipeline, together with high and rising oil prices have required imports of expensive fuel products for electricity generation. At the same time, regional tensions and the global economic downturn have adversely affected tourism, worker remittances, and foreign direct investment. As a result, growth has slowed. Despite improvement in tourism income and remittances in 2012, along with the projected decline in oil prices, the external current account deficit is expected to widen to an estimated 14 percent of GDP in 2012, from 12 percent of GDP in 2011.\par
In response to the negative external shocks, the Jordanian government has asked for financial assistance from the IMF, and adopted another reform program. In this context, it took significant measures to bring back fiscal and energy policies to a sustainable path.\par
Government expenditure may exceed its revenue for a given period. This generates budget deficit, so it is important to find remedy for financing government expenditure to eliminate the negative effects. Government expenditure could be financed by increasing seigniorage revenue. In this case the central bank is assumed to print money for the treasury department without there being any obligation on the part of the government to pay interest or repay the principal. As the government spends this cash it simultaneously increases aggregate demand and the currency in circulation, leading to the growth of the financial wealth of the private sector and of the broad money supply. Broad money demand, private consumption and private investment are affected with a lag of two or three periods. In turn, it increases price level which induces inflation. Likewise, inflation is critical in the determination of government revenue and spending which induces government expenditure.\par
General notions imply that steadily high government expenditures increase inflation and inflation induces government expenditure. However, the results of empirical studies which examines this issue are inconclusive, demonstrating that the link from government expenditures to monetary expansion and then to inflation and vice versa from inflation to government expenditure might be less crucial in determining the track of relationship. Furthermore, lack of monetization in the face of increasing government expenditures presents an additional argument on that point. Nevertheless, even when a central bank does not monetize the budget deficit, adjustments in the private sector to higher deficit policies may lead to inflation.\par
In general, there is little disagreement that in the long-run inflation is primarily a monetary phenomenon. Jordan's experience is not different in that respect as inflation is generally associated with monetary expansion. Therefore, a rise in the general price level can most often be traced to money supply growth. However, the developments in the fiscal sector are also considered to be an important factor in explaining price fluctuations.\par
Following five years of inflation running below 2\%, as measured by the change in the consumer price index, it has started to climb in the last few years, reaching 5.4\% in 2007. However, it is still lower than its record high of 6.3\% recorded the previous year. Inflationary pressures continue in 2008 with inflation hitting 12\% on the back of the full liberalization of oil prices in the domestic market which took place in early February 2008. It will also be affected by the general salary increases in the public sector and the escalation in food and metal prices in the international market. The government also raised the price of water and electricity. Data show that inflation in September of 2008 reached 20\% year on year. Then, it slows down to reach 5.5\% in 2011.\par
Both domestic and foreign factors have had a say in the rise in consumer prices in Jordan over the last five years. Rising international prices of food, oil and metals, as well as the weakening of the US dollar against other major currencies, were major external factors contributing to higher inflation rates, whereas expansionary fiscal and monetary policies were among the leading domestic factors.\par
This paper aims to explore the interaction monetary policy and fiscal policy in Jordan for the period 1990 -2011, through investigation of the short-term and long-term relationship, and direction of causality among between supply of money (M2) as monetary policy, government expenditure (G) as fiscal policy and inflation (INF) as a result and cause of these policies.\par
The rest of the paper is organized as follows. Section 2 provides a brief review of literature. Section 3 shows model specification for the study. The methodology is introduced in Section 4, while Section 5 contains data description and empirical findings. Section 6 concludes the study. 
\section[{II.}]{II.} 
\section[{Literature Review}]{Literature Review}\par
In economic literature, numerous models have been developed to analyze the long-run relationship among government expenditure, money supply and inflation. However, evidence from the empirical literature is mixed:-Year 2015 © 2015 Global Journals Inc. (US) 1 ( B )\par
Sulaiman D Mohammad, S Khurram Arslan Wasti, Irfan Lal and Adnan Hussain (2009) investigate long run relationship among M2, inflation, government expenditure impact and economic growth in case of Pakistan. Using Johnson co integration test to find out long run association and Granger causality test to find out bilateral and unilateral causality, based on annual data from 1977 to 2007. The finding shows that there is unidirectional causality running from public expenditure cause to money supply inflation, and bidirectional causality between money supply and inflation. This study lends support for the hypothesis that government expenditure causes monetary expansion and, therefore, leads to inflation. As well, Ignacio Lozano (2008) analyzed the evidence of the causal long-term relationship between government expenditure, money growth and inflation in Colombia considering the standard (M1), the narrowest (M0-Base) and the broadest (M3) definitions of money supply. Using a vector error correction (VEC) model with quarterly data over the last 25 years, the study found a close relationship between inflation and money growth on the one hand, and between money growth and fiscal deficit, on the other. Likewise, \hyperref[b0]{Agha and Khan (2006)} examine the long-run relationship between inflation and fiscal indicators in Pakistan for the period 1973-2003. The empirical results, using Johansen co-integration analysis, indicate that in the long-run inflation is not only related to fiscal imbalances but also to the sources of fiscal deficit financing. The authors conclude that inflation in Pakistan is strongly affected by government's bank borrowing for budgetary support as well as fiscal deficits and, consequently, that fiscal policy is an important factor in explaining price movements. Also, the study by \hyperref[b2]{Catao and Terrones (2003)} shows that there is a strong positive relationship between government expenditures and inflation among developing countries as well as countries characterized by high inflation, but not among advanced economies with low-inflation. Durevall and Ndung'u (2001), using a dynamic error correction model of inflation for Kenya, find that money supply affects prices only in the shortrun. \hyperref[b20]{Vieira (2000)} investigates the relationship between fiscal deficit and inflation in the case of six major European economies. The results provide little support for the proposition that government expenditure has been an important contributing factor to inflation in these economies over the last 45 years. Similarly, De \hyperref[b4]{Haan and Zelhorst (1990)} analyze the relationship between government expenditure and money growth in developing countries. The overall conclusion of this study does not provide much support for the hypothesis that government expenditure causes monetary expansion and, therefore, leads to inflation. 
\section[{III. Model Specifications for the Study}]{III. Model Specifications for the Study}\par
Using annual data from CBJ's database and IMF's database the present paper examines the relationship between government expenditure, money supply and inflation in Jordan, while our model will be:INF t = ? + ? 1 M2 t + ? 2 G t + U t (1)\par
Where G t is government expenditure, M2 is money supply and INF t is inflation which is measured by consumer price index (CPI t ) while ? and ? are the coefficient to be estimated and the U t is error term.\par
This can be reformulated to examine the link between government expenditure and other variables as follows:-G t = ? + ? 1 M2 t + ? 2 INF t + U t (2)\par
Taking the logarithm form of equation ( 1) will yield equation (3) below with "ln" standing for the natural logarithmlnG t = ? + ? 1 lnM2 t + ? 2 lnINF t + U t\textbf{(3)}\par
IV. 
\section[{Econometric Methodology}]{Econometric Methodology}\par
The objective of this section is to examine the presence of interdependence and the direction of causality between government expenditure, money supply and inflation in Jordan.\par
In order to examine the relationship and the directions of causality between government expenditure, money supply and inflation in Jordan, a two-step procedure is adopted. The first step investigates the existence of a long-run relationship between the variables through a co-integration analysis. The second step explores the causal relationship between the series as follows:a) Long run relationship We perform our investigation of existence of cointegration which clarifies the long run relationship between variables in two steps. First, we test for unit root vs. stationary. Then we test for no co-integration vs. cointegration. 
\section[{b) Unit root test}]{b) Unit root test}\par
The objective of unit root test to empirically examine whether a series contains a unit root. Since many macroeconomic series are non stationary \hyperref[b15]{(Nelson and Plosser 1982)}, unit root test are useful to determine the order of integration of the variables and, therefore, to provide the time-series properties of data. If the series contains a unit root, this means that the series is nonstationary. Otherwise, the series will be categorized as stationary. In order to implement a more rigorous test to verify the presence of a unit root in the series, an Augmented Dickey-Fuller (ADF) is employed. c) Co-integration test Johansen and Juselius procedure is applied to test for the existence of co-integration. The Johansen technique enables us to test for the existence of nonunique co-integration relationships in more than two variables cases. Through Johansen procedure of cointegration two tests statistics are suggested to determine the number of co-integration vectors determined based on a likelihood ratio test (LR): the trace test ( ? trace ) and the maximum eigenvalues test statistics ( ? max ). 
\section[{d) Granger-causality test}]{d) Granger-causality test}\par
Pair wise causality relationship between variables should be tested through the implementation Granger causality test; \hyperref[b8]{Granger (1969)}, the concept of "causality" assumes a different meaning with respect to the more common use of the term. The statement(y) Granger causes (x) or vice versa, in fact, does not imply that (y) and (x) is the effect or the result of (y) and (x), but represents how much of the current (y) and (x) can be explained by the past values of (y) and (x) and whether adding lagged values of (y and x) can improve the explanation. For this reason, the causality relationship between (y and x) can be evaluated by estimating the following regressions:i i t i i t i t X Y Y n i m i ? ? ? ? + ? + ? + = ? ? ? ? ? = = 2 1 0 1 1 (4) i i t i i t i t X Y X m i n i ? ? ? ? + ? + ? + = ? ? ? ? ? = = 2 1 0 1 1 (5)\par
Where (m and n) represents the lag length and should set equal to the longest time over which one series could reasonable help to predict the other.\par
Following this approach, the null hypothesis that (x) does not granger cause (y) in regression (4) and that (y) does not Granger cause (y) in regression (5) can be tested through the implementation of a simple F-test for the joint significance of, respectively, the parameters ?1i and ?2i. Following the equations (4) and (5) were estimated using four lags of each variable which should represent and adequate lag-length over which one series could help to predict the other.\par
The results of stationarity and cointegration tests determine how Granger-causality test should be applied, as follows:\par
If the variables (y) and (x) are stationary, the standard Granger-causality test should be carried out by estimating the following regressions:-? ? ? ? + ? ? ? ? = = + + = i t i i t i t X Y Y n i m i 2 1 1 1 (6) i i t i i t i t X Y X m i n i ? ? ? ? + ? ? ? ? = = + + = 2 1 1 1 (7)\par
If the variables (y) and (x) are non-stationary and integrated of order (1), but, they are not cointegrated, the Granger-causality test could be carried out by estimating regression models (4 and 5) using the first difference series of both variables \hyperref[b22]{(Yoo and Kwak, 2004)}. In general, if the origin series of both variables are non-stationary and the variables are not cointegrated, the Granger-causality test could be performed by using the same order of integration for both series, and reforming model (5 and 6) to suit the order of difference series.\par
In model (4 and 6), (Y) is caused by past values of both (Y) and (X). Likewise, in model (5 and 7), (X) is caused by past values of the two variables. According to Granger, (X) causes (Y) in model (4 and 6) if (?2i) is significant from zero, and that (Y) causes (X) in model (5 and 7) if (?1i) is significant from zero. On other hand, (X) does not cause (Y) if (?2i) in model (  {\ref 4 and 6}) is insignificant from zero, and that (Y) does not cause (X) if (?1i) in model (  {\ref 5 and 7}) is insignificant from zero. These hypotheses can be verified depending on the joint significance of the parameters (?1i, ?2i) which can be tested through the implementation of a simple F-test.\par
If the variables (Y) and (X) are non-stationary, integrated of the same order (d), and co-integrated which means that they have a long-run equilibrium relationship, the Granger-causality test should be carried out through estimating Error Correction Model (VECM) which could have the following form:i t i t i i t i t X Y Y n i m i ? ? ? ? ? ? + + ? + ? + = ? ? ? ? ? ? = = 1 3 2 1 1 1 (8) i t i t i i t i t Y X X n i m i ? ? ? ? ? ? + + ? + ? + = ? ? ? ? ? ? = = 1 3 2 1 1 1 (9)\par
Where (1 ? t ? ) is error-correction term. The error correction term ( 1 ? t ? )\par
is the lagged value of the residuals from the OLS regression of Y t on X t in \hyperref[b6]{(8)}, and the lagged value of the residuals from the OLS regression of X t on Y t in \hyperref[b7]{(9)}. In (  {\ref 8}) and (  {\ref 9}), ?Y t , ?X t and ? i are stationary, implying that their right-hand side must also be stationary. It is obvious that (  {\ref 8}) and (  {\ref 9}) compose a bivariate VAR in first differences augmented by the error-correction terms (1 ? t ? ), indicating that\par
ECM model and co-integration are equivalent representations.\par
According to \hyperref[b8]{Granger (1969;}\hyperref[b10]{1988)}, in a cointegrated system of two series expressed by ECM representation causality must run in at least one way. Within the ECM equation (  {\ref 8}), (X t ) does not Granger cause (Y t ) if all ?2i = 0 and ?3 = 0. Equivalently, in equation (  {\ref 9}) (Y t ) does not Granger cause (X t ) if all ?2i = 0 and ?3 = 0. Also, (?3s) the parameters of the error correction term indicate the speed of adjustment of any short-run disequilibrium towards a long-run equilibrium between both variables. The Granger-causality could be claimed if the parameters (?2i and ?3) in (  {\ref 8}) and, or (?2i and ?3) in (  {\ref 9}) are jointly significant from zero which can be tested by a simple F-test. Similarly, Long-run causality could be claimed if (?3) the parameter of the error correction term in (8 or 9) is statistically significant which can be tested by t-test.\par
What have been mentioned above clarifies that testing of stationary then co-integration are an essential requirements which determine how we do Grangercausality test.\par
Finally, as co-integration, causality tests and VECM model are sensitive to lag length (m) the choice of the number of lag actually employed was assigned to LR: sequential modified LR test statistic (each test at 5\% level).\par
V. 
\section[{Estimation and Interpretation of Results}]{Estimation and Interpretation of Results}\par
This study uses annual observations for the period 1990-2011 for three variables: government expenditure (G), money supply (M2) and inflation (consumer price index (CPI)) in order to analyze the possibility of co-integration and causality relationship among them. 
\section[{a) Unit root test}]{a) Unit root test}\par
The first step in analysis is to test the unit roots in each variable. Consequently, we apply augmented Dickey-Fuller (ADF) unit root test on logarithms of G, M2 and INF (LG, LM2 and LINF). From the results of the ADF test presented in Table \hyperref[tab_0]{1}. \par
-Note: * test critical values which denotes significant at 5\% level.\par
-The number in parenthesis is the [t] statistic value. As a sum up, (LG, LM2 and LINF) are stationaries in the first difference. This implies that all the series are integrated of order one I(1). This making cointegration tests relevant. 
\section[{b) Testing Co-integration and Error Correction mechanism}]{b) Testing Co-integration and Error Correction mechanism}\par
Since the first difference series are stationary, Let us examine the existence of co-integration between variables on bivariate bases. Johansen-Juselius procedure is used to test for co-integration between variables. Tables 3-1,3-2,3-3 reports the results obtained from the co-integration tests and presents the result of the trace test ( ? trace) and maximum eigenvalues test ( ? max) statistics for the existence of long run equilibrium between the variables: The null hypothesis of no Cointegration (r=0) based on both the trace test and the maximum eigenvalues test between government expenditure (LG) and money supply (LM2) could not be rejected at (5\%) level of significance. The estimated tests indicate that there is no Cointegration vector between the two variables. The null hypothesis of no Cointegration (r=0) based on both the trace test and the maximum eigenvalues test between government expenditure (LG) and inflation (LINF) is rejected at (5\%) level of significance. However, the null hypothesis that (r ? 1)\par
could not be rejected. The estimated tests indicate that there is only one Cointegration vector between the two variables. The lagged error term coefficient (1 ? t ? ) in (LG)\par
equation is negative and statistically insignificant.\par
Likewise, the lagged error term coefficient (1 ? t ? ) in\par
(LINF) equation is positive and statistically significant. These results indicate that there is no long-run equilibrium relationship between the variables government expenditure and inflation Furthermore, the estimates of the VECM does not support the existence of causation relationship in the short run between them.\par
Then, the existence of Granger-causality between government expenditure (LG) and money supply (LM2) are investigated by estimating regression models (4 and 5) using the first difference series of each variable. Since, they are non-stationary at level and integrated of order (1), but, they are not co-integrated. Table \hyperref[tab_4]{4}-2 shows the results of estimation: Similarly, the existence of Granger-causality between money supply (LM2) and inflation (LINF) are investigated by estimating regression models (4 and 5) using the first difference series of each variable. Since, they are non-stationary at level and integrated of order (1), but, they are not co-integrated. The results of Granger-causality test in Table \hyperref[tab_4]{4}-3 reveal that there is no causation between money supply (LM2) and inflation (LINF).\par
The findings of Granger-causality test and VECM reveal that there is no significant direct interaction between government expenditure as fiscal policy and money supply as monetary policy or indirect interaction through inflation among them. 
\section[{VI.}]{VI.} 
\section[{Conclusion}]{Conclusion}\par
In the last three decades Jordan has been suffering from serious Economic deformations, which includes chronic deficits in the budget and trade account, high level of unemployment and public debt. This highlights the need of efficient fiscal and monetary policies to tackle these problems and to achieve economic stabilization. The interaction between fiscal and monetary policies and the different agencies responsible for implementation of these policies imply there are no exclusive effective fiscal and monetary measurements for dealing with these deformations, given that each policy has its own supporters. So, most countries have been adopting monetary policy, fiscal policy, or a mix of both policies to tackle their economic problems.\par
In Jordan, notions imply that expansionary fiscal and monetary policies were crucial domestic factors in rise in consumer prices in the country over the last two decades .whereas, raising international prices of food, oil and metals, as well as the weakening of the US dollar against other major currencies, were major external factors contributing to higher inflation rates in Jordan.\par
Based on annual data covering the period 1990-2011for Jordan. This study has used Grangercausality, cointegration and VECM techniques to investigate short-run and long-run relationships also the directions of causality between government expenditure as fiscal policy, money supply which belongs to monetary policy and inflation which is mainly induced by money supply and affects government expenditure and vice versa.\par
The estimation of cointegration, Grangercausality and VECM tests reveal that there is no significant direct interaction between government expenditure as fiscal policy and money supply as monetary policy or indirect interaction through inflation among them. This study does not provide much support for the hypothesis that government expenditure causes monetary expansion and, therefore, leads to inflation or vice versa. Which confirm the findings of De \hyperref[b4]{Haan and Zelhorst (1990)} and \hyperref[b20]{Vieira (2000)}.\par
The study suggests that policies makers should considered these findings while designing the fiscal and monetary policies to deal with the economic problem, and pay attention to others factors which induce inflation especially the external factors mainly international prices of food and oil.\begin{figure}[htbp]
\noindent\textbf{1} \par 
\begin{longtable}{P{0.09654788418708239\textwidth}P{0.2631403118040089\textwidth}P{0.28017817371937637\textwidth}P{0.1514476614699332\textwidth}P{0.0586859688195991\textwidth}}
Series\tabcellsep With intercept\tabcellsep With intercept\tabcellsep Decision\tabcellsep Order of\\
\tabcellsep \tabcellsep and trend\tabcellsep \tabcellsep integration\\
Levels\tabcellsep ADF\tabcellsep ADF\tabcellsep \tabcellsep \\
LG\tabcellsep -1.418137\tabcellsep -1.100418\tabcellsep Not stationary\tabcellsep -\\
\tabcellsep {}[-3.012363]\tabcellsep {}[-3.644963]\tabcellsep \tabcellsep \\
LM2\tabcellsep 0.722843\tabcellsep -0.773098\tabcellsep Not stationary\tabcellsep -\\
\tabcellsep {}[-3.012363]\tabcellsep {}[-3.644963]\tabcellsep \tabcellsep \\
LINF\tabcellsep -0.030729\tabcellsep -1.361985\tabcellsep Not stationary\tabcellsep -\\
\tabcellsep {}[-3.012363]\tabcellsep {}[-3.644963]\tabcellsep \tabcellsep \\
First difference\tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
?LG\tabcellsep -4.140659*\tabcellsep -4.865945*\tabcellsep stationary\tabcellsep I(1)\\
\tabcellsep {}[-3.020686]\tabcellsep {}[-3.658446]\tabcellsep \tabcellsep \\
?LM2\tabcellsep -3.190892*\tabcellsep -3.622737\tabcellsep stationary\tabcellsep I(1)\\
\tabcellsep {}[-3.020686]\tabcellsep {}[-3.658446]\tabcellsep \tabcellsep \\
Î?"LINF\tabcellsep -4.834829*\tabcellsep -4.862575*\tabcellsep stationary\tabcellsep I\\
\tabcellsep {}[-3.020686]\tabcellsep {}[-3.658446]\tabcellsep \tabcellsep \end{longtable} \par
 
\caption{\label{tab_0}Table 1 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{3} \par 
\begin{longtable}{P{0.21340425531914892\textwidth}P{0.1374468085106383\textwidth}P{0.018085106382978722\textwidth}P{0.1374468085106383\textwidth}P{0.010851063829787233\textwidth}P{0.33276595744680854\textwidth}}
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep Year 2015\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep Volume XV Issue II Version I\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep ( B )\\
\multicolumn{5}{l}{1 : Co-integration test: (LGâ??"LM2)}\tabcellsep Global Journal of Management and Business Research\\
Null Hypothesis\tabcellsep ?\tabcellsep trace\tabcellsep ?\tabcellsep max\\
r=0\tabcellsep \multicolumn{2}{l}{11.49574}\tabcellsep \multicolumn{2}{l}{10.38150}\\
\tabcellsep \multicolumn{2}{l}{[15.49471]}\tabcellsep \multicolumn{2}{l}{[14.26460]}\\
r ? 1\tabcellsep \multicolumn{2}{l}{1.114242 [3.841466]}\tabcellsep \multicolumn{2}{l}{1.114242 [3.841466]}\end{longtable} \par
  {\small\itshape [Note: -*terms in [ ] indicates 5\% level critical value.]} 
\caption{\label{tab_1}Table 3 -}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{3} \par 
\begin{longtable}{P{0.39522292993630576\textwidth}P{0.20573248407643313\textwidth}P{0.027070063694267513\textwidth}P{0.20573248407643313\textwidth}P{0.01624203821656051\textwidth}}
Null Hypothesis\tabcellsep ?\tabcellsep trace\tabcellsep ?\tabcellsep max\\
r=0\tabcellsep \multicolumn{2}{l}{16.87893}\tabcellsep \multicolumn{2}{l}{14.40104}\\
\tabcellsep \multicolumn{2}{l}{[15.49471]}\tabcellsep \multicolumn{2}{l}{[14.26460]}\\
r ? 1\tabcellsep \multicolumn{2}{l}{2.477893 [3.841466]}\tabcellsep \multicolumn{2}{l}{2.477893 [3.841466]}\\
\multicolumn{3}{l}{-*terms in [ ] indicates 5\% level critical value.}\tabcellsep \tabcellsep \end{longtable} \par
 
\caption{\label{tab_2}Table 3 -}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{3} \par 
\begin{longtable}{P{0.42276021080368903\textwidth}P{0.043115942028985506\textwidth}P{0.30517127799736493\textwidth}P{0.0744729907773386\textwidth}P{0.004479578392621871\textwidth}}
\multicolumn{5}{l}{3 : Co-integration test: (LM2â??"LINF)}\\
Null Hypothesis\tabcellsep ?\tabcellsep trace\tabcellsep ?\tabcellsep max\\
r=0\tabcellsep \multicolumn{2}{l}{10.12480}\tabcellsep \multicolumn{2}{l}{8.948506}\\
\tabcellsep \multicolumn{2}{l}{[15.49471]}\tabcellsep \multicolumn{2}{l}{[14.26460]}\\
r ? 1\tabcellsep \multicolumn{2}{l}{1.176294 [3.841466]}\tabcellsep \multicolumn{2}{l}{1.176294 [3.841466]}\\
\multicolumn{3}{l}{-*terms in [ ] indicates 5\% level critical value.}\tabcellsep \tabcellsep \\
\multicolumn{2}{l}{The null hypothesis of no Cointegration (r=0)}\tabcellsep \multicolumn{3}{l}{Does money supply cause inflation, or does inflation}\\
\multicolumn{2}{l}{based on both the trace test and the maximum}\tabcellsep \multicolumn{3}{l}{cause money supply? And (c) does government}\\
\multicolumn{2}{l}{eigenvalues test between money supply (LM2) and}\tabcellsep \multicolumn{3}{l}{expenditure cause inflation, or does inflation cause}\\
\multicolumn{2}{l}{inflation (LINF) could not be rejected at (5\%) level of}\tabcellsep \multicolumn{3}{l}{government expenditure?}\\
\multicolumn{2}{l}{significance. The estimated tests indicate that there is}\tabcellsep \multicolumn{3}{l}{As the variables (LG) and (LINF) are non-}\\
no Cointegration vector between the two variables.\tabcellsep \tabcellsep \multicolumn{3}{l}{stationary at level, integrated of the same order (d), and}\\
c) Causality test\tabcellsep \tabcellsep \multicolumn{3}{l}{co-integrated, the Granger-causality test is carried out}\\
\multicolumn{2}{l}{Now we can turn our attention to the question of}\tabcellsep \multicolumn{3}{l}{through estimating Vector Error Correction Model}\\
\multicolumn{2}{l}{direction of causality. It contains three elements: (a)}\tabcellsep \multicolumn{3}{l}{(VECM). Table 4-1 shows the findings of VECM for both}\\
\multicolumn{2}{l}{does government expenditure cause money supply, or}\tabcellsep \multicolumn{3}{l}{variables government expenditure (LG) and inflation}\\
\multicolumn{2}{l}{does money supply cause government expenditure? (b)}\tabcellsep (LINF):-\tabcellsep \tabcellsep \\
\multicolumn{5}{l}{Table 4-1 : Vector Error Correction Model.(LGâ??"LINF)}\\
Regression\tabcellsep \tabcellsep ?LG\tabcellsep \tabcellsep ?LINF\\
CONSTANT\tabcellsep \multicolumn{2}{l}{0.066760 [ 1.94392]}\tabcellsep \multicolumn{2}{l}{0.048762 [ 4.34105]}\\
Error Correction Term ( 1 ? t ? )\tabcellsep \multicolumn{2}{l}{-0.173714 [-0.74059]}\tabcellsep \multicolumn{2}{l}{-0.225798 [-2.94314]}\\
?LG -1\tabcellsep \tabcellsep 0.006863 [0.02267]\tabcellsep \multicolumn{2}{l}{-0.023081 [-0.23311]}\\
?LINF -1\tabcellsep \tabcellsep 0.390044 [0.62228]\tabcellsep \multicolumn{2}{l}{-0.225798 [-1.52933]}\\
R 2\tabcellsep \tabcellsep 0.045572\tabcellsep \multicolumn{2}{l}{0.469093}\\
S.E\tabcellsep \tabcellsep 0.075045\tabcellsep \multicolumn{2}{l}{0.024546}\end{longtable} \par
  {\small\itshape [Note: -*terms in [ ] are t -statistics.]} 
\caption{\label{tab_3}Table 3 -}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{4} \par 
\begin{longtable}{P{0.6998756218905472\textwidth}P{0.010572139303482588\textwidth}P{0.05497512437810945\textwidth}P{0.04017412935323383\textwidth}P{0.044402985074626866\textwidth}}
\multicolumn{3}{l}{2 : Granger-causality test (LGâ??"LM2)}\tabcellsep \tabcellsep \\
Regression\tabcellsep Lag\tabcellsep F-statistics\tabcellsep P-Value\tabcellsep Granger-causality\\
Î?"LG on Î?"LM2\tabcellsep 1\tabcellsep 0.63976\tabcellsep 0.4348\tabcellsep NO\\
Null hypothesis: Î?"LM2 does not granger cause Î?"LG\tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
Î?"LM2 on Î?"LG\tabcellsep 1\tabcellsep 1.40816\tabcellsep 0.2517\tabcellsep NO\\
Null hypothesis: Î?"LG does not granger cause Î?"LM2\tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
The results of Granger-causality test in Table 4-\tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
2 reveal that there is no causation between government\tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
expenditure (Î?"LG) and money supply (Î?"LM2).\tabcellsep \tabcellsep \tabcellsep \tabcellsep \end{longtable} \par
 
\caption{\label{tab_4}Table 4 -}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{4} \par 
\begin{longtable}{P{0.85\textwidth}}
-3 shows the\end{longtable} \par
 
\caption{\label{tab_5}Table 4}\end{figure}
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