The Concept Of Co-Integration Analysis

The goal of inferential statistics is to end up rejecting the null hypothesis and concluding that a significant relationship exists; therefore, the null hypothesis always presume no relationship.

The ADF and PP unit root test results, indicated that, Agric growth, Exports, Infrastructure development, Intellectual Property Rights and Research & development are integrated of order one I(1) intercept and trends, that is, they achieved stationary after taking their first. But the other variable achieved stationery after their first difference I (1) none. Having ascertained the absence of I (2) variable in the series, the ARDL approach to co integration is now applied to the

4.|When hypothesizing a difference of 0, if the confidence interval does not contain 0, the null hypothesis is rejected.|

Also, this estimation allows the using of a lagged dependent variable to check whether there is a possible cointegration in the economic variables, with the possibility of one variable forecasting another (Campbell & Shiller, 1988). Including the lagged dependent variable can reduce the occurrence of autocorrelation arising

Additionally, I will also be testing for granger causality. In order to test for granger causality though we need to use variables which are stationary.

In the ADF level test, we cannot reject the null hypothesis that there is a unit root in each series of both VISA INC. and WATERS. This is shown in the LEVEL columns above. The 1st difference columns above show the output of the ADF test for the first-differenced series. All the series are significant so that they reject the null hypothesis. We can say that the first-differenced series are I(0), so they are stationary. And we can say that each level series contains a unit root and is integrated of order 1(I(1)).

For the output gap analysis, the data are in billions of chained 2009 dollars. At the start of recession one, quarter three of 1990, the output gap was -30, which was the highest point the output gap reached in this recession. At the end of recession one, quarter one of 1991, the output gap was -277, which was the lowest point the output gap dropped in this recession. At the start of recession two, quarter two of 2001, the output gap was 21, which was the highest point the

What are the major features of the “full integration model”. What makes this model different from other models?

Table 4.1 presents the panel unit-root test results. There are two groups of hypotheses that are involved here. In the first four methods, the null hypothesis is: there is panel unit-root and the alternative hypothesis is: there is no panel unit-root and the decision

As follow-on to the model results, we evaluate the relationships between the variables in each of the statistically significant models to test the hypothesis. Table 15 provides the standardized regression coefficient (B),

It is a well-known fact that most economic and particularly macroeconomic time series have been found to be nonstationary, requiring differencing to induce stationarity, this is reinforced by the fact that a study regarding the distribution of test statistics of a time series as early as the year 1942, (Anderson, 1942). Hence, several tests were composed most popularly the Phillips-Perron test (Phillips and Perron, 1988), KPSS test (Kwiatkowski, Phillips, Schmidt and Shin (1992)) and the augmented Dickey-Fuller test (Dickey and Fuller; 1979, 1981). Several drawbacks

At first we couldn’t rejected the null hypothesis because result from table 1 shows that only two variables were stationary at level while four variables became stationary at first difference and the result gives us the strong proof of unit root in the variables. Therefore all variables were tested at first difference and the ADF test make known that all the variables became stationary at first difference, therefore, the null hypothesis at first difference of unit root were rejected so we concluded by saying that the variables became stationary after first difference. Which means that the variables are co-integrated of order one I(1), and this give us the chance to performed co-integration analysis.

In order to test for a long-term relationship in the Sri Lankan economic model, we use the Engle-Granger residual-based approach with all the variables and test for cointegration.In this case, we test the stationarity ofβ_1 x_1t+β_2 x_2t+⋯β_n x_nt=e_t. This approach is based on the Chapter 6(pp.343) in “Applied econometric time series” (Enders, 2015).

Wijeweera, Dollery & Pathberya (2005), investigated the connections between external debt servicing and economic growth in Srilanka during 1952-2002 by using co-integration methodology for the long run error

The study conduct a time series analysis of the determinants of gross domestic saving in Ethiopia using co-integration and error correction econometric modeling(ECM), and employed data for the period of 1971-2009 collected from National bank of Ethiopia (NBE), MOFED, CSA and world bank on annual base. The study revealed that GPCI, DR, INF, GTR and GGDS1-t significant influence on GDS in cointegration model. Further more GPC, INF, GTR and GGDS1-t are also significant in ECM model while PCI, RIR and GM2 showed insignificant coefficient in both