Moreover in equation 1 through 4 four short run

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Moreover, in equation (1) through (4), four short-run Granger causality tests (a Wald F-test for short-run non causality) are performed by setting the co-efficient of all order-lagged differences of each of the variables on the right-hand side equal to zero. In equation (1) for instance, a test for short-run non-causality from money supply ( ) to Economic Growth (GDP) is conducted by testing whether the coefficients of the lagged differences of the (MS) are all equal to zero. The same thing is done regarding the short-run causality from other variables such as INF and RINTR on GDP. V. Data Presentation And Discussion Of Results 5.1. Data Presentation The analysis and interpretation of results commence with investigation of the properties of the data used. This begins with unit root test using both PP and ADF unit root tests. The table below presents the result of the unit root test carried out on each of the series. Table (1) indicates that all the variables are stationary at first difference I(1). Therefore, since our series are stationary at I(1) value, the application of VAR has theoretical support.
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Impact of Money Supply and Inflation on Economic Growth in Nigeria (1973-2013) DOI: 10.9790/5933-0803042637 www.iosrjournals.org 31 | Page Table 1 : Unit Root Test Results Variables ADF Test results PP Test Results Order of integration Constant Trend Constant Trend GDPDR -5.3167* -5.8672* -5.3774* -5.8481* I(1) BMGDP -3.0821* -3.2142* -2.9503* -3.2795* I(1) INFD -6.3243* -6.2311* -6.3261* -6.2306* I(1) INTR -6.3301* -6.9005* -6.3327* -7.0218* I(1) -8.3508* -8.2199* -16.9770* -17.7960* I(1) -4.4946* -4.4894* -5.5763* -5.5557* I(1) -7.7103* -7.6068* -13.6466* -13.4134* I(1) -7.8929* -7.7796* -14.4241* -14.1972* I(1) Note: For ADF and PP, the null hypothesis is that the variable has a unit root (i.e non stationary), (*), (**), and (***) represent significant at 10%, 5% and 1% respectively while denotes difference operator and order of integration. Source: A uthor’s computation using E -VIEW 7.1 Having confirming the stationarity of the series, the paper proceeded by testing for the optimum lag. The test for optimum lag was carried out and the result is presented in table (2). From table (2), it can be seen that all the criteria selected an optimum lag of 5 with the exception of LR t-statistics and Schwarzs information criteria (SC) that suggested 3 and 1 respectively. Table 2: Optimal Lag Test Lag LogL LR FPE AIC SC HQ 0 -517.7520 NA 45570491 28.98622 29.16217 29.04763 1 -485.1875 56.08332 18286828 28.06597 28.94570 * 28.37302 2 -469.6029 23.37687 19401182 28.08905 29.67257 28.64174 3 -444.2753 32.36309 * 12682779 27.57085 29.85815 28.36918 4 -432.8049 12.10760 19709690 27.82250 30.81359 28.86647 5 -402.4887 25.26353 12597116* 27.02715* 30.72203 28.31676* * indicates lag order selected by the criterion LR: sequential modified LR test statistic (each test 5%level) FPE: Final prediction error AIC: Akaike information criterion SC: Schwarz information criterion HQ: Hannan-Quinn information criterion Source: Author’s computation using E -view 7.1 Consequently, a co-integration test was conducted using Johanson Co-integration test using maximum lag of 5, and the result of which is presented in the table (3). Table 3 : Johansen Co-integration Test Results Hypothesis Eigen Value Max 5%Critical value Prob. Trace 5%Critical value Prob.
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