this study the Augmented Dickey Fuller ADF and Phillip Perron test PP are used

This study the augmented dickey fuller adf and

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this study, the Augmented Dickey-Fuller (ADF) and Phillip-Perron test (PP) are used for unit root test to check the stationarity of the variables. Both tests test the null hypothesis of a unit root and the null hypothesis of a unit root is rejected in favour of the stationary alternative in each case if the test statistic is more negative than the critical value. A rejection of the null hypothesis means that the series do not have a unit root. Table 1 presents results of the unit root tests. Results of unit root tests reported in Table 1 below show that when considering the ADF test, the variables GDP and MS are stationary at level series whilst the variables EXC, CPI and REPO are not stationary at level series but however become stationary after first difference. When using the PP test, all variables are stationary after first differencing except for the variable CPI which is stationary at level series. Considering results of the much stricter PP test, variables are thus integrated of order 0 and 1. Table 1: Unit root tests 2000Q1- 20010Q4 at levels and first differences ( ǻ ) GDP MS EXC CPI REPO ADF Level -3.228029* -4.178733** -2.260465 -2.403507 -2182617 First Difference -3.174193** -5.548346*** -3.369273** PP Level -2.090200 -1.574085 -1.576060 -2.606956* -1.393391 First Difference -3.197683** -5.816582*** -3.284369** -3.107152** ***, ** and * represents stationary at 1%, 5% and 10% level of significance respectively Co integration Analysis Result and Interpretation 5.2 After establishing stationarity, the next step is to test for cointegration. Cointegration exists if two variables have a long- term, or equilibrium, relationship between them. This study employs the Johansen maximum likelihood approach to test for co-integration as it more desirable to the other methods due to its properties (Wassell and Saunders, 2000). As a requirement of the Johansen co-integration technique, the optimums lag length, the appropriate model regarding the deterministic components in the multivariate system and the numbers of cointegrating vectors were all determined before establishing the long and short run coefficients. Table 2 below presents results an optimal lag length. As indicated in Table 2, all methods except the SC chose 6 lags, whilst 2 lags were chosen by the SC method. Brooks (2008) explains that when choosing the optimum number of lags using the information criteria, the chosen number of lags is that number minimising the value of the given information criterion. To add on, various literature for example Wahid (2008), Asghar
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ISSN 2039-2117 (online) ISSN 2039-9340 (print) Mediterranean Journal of Social Sciences MCSER Publishing, Rome-Italy Vol 5 No 15 July 2014 81 and Abid (2007) as well as Mahlo (2011) support the selection of the optimum number of lags chosen by the SC. Accordingly the Johansen cointegration test is employed using 1 lag for the VAR in this study. Table 2: VAR lag order selection criteria Lag LogL LR FPE AIC SC HQ 0 -533.4579 NA 175764.5 26.26624 26.47521 26.34233 1 -376.7503 267.5495 287.9182 19.84148 21.09531* 20.29806* 2 -348.2651 41.68562* 257.0806 19.67147 21.97016 20.50853 3 -317.5905 37.40805 226.7194* 19.39466* 22.73822 20.61220 * indicates lag order selected by the criterion, FPE: Final prediction error, AIC: Akaike information criterion, SC: Schwarz information criterion, HQ:Hannan-Quinn information criterion
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