Cointegration

To examine the effect of monetary policy on determination of coal prices in the U.S., we rely on a dynamic framework of cointegrated vector autoregression (CVAR). The prerequisite requirement for the application of the CVAR approach is that the selected variables must be nonstationary (i.e., I(1) series). The presence of a unit root in the variables is thus tested using the Dickey Fuller generalized least squares (DF-GLS) test (Elliott et al., 1996). Panel A of Table 1 reports the results of the

taken as the proxy for Nepalese economic growth. Some attention is necessary while employing FMOLS test. The variables under study must be cointegrated. So before applying the FMOLS we examine the cointegration by method of Johansen’s (1990) cointegration test. Prior to employing the Johansen’s Cointegration test we perform unit root test using ADF method. FMOLS method was designed by Phillips and Hansen (1990) to estimate the cointegrating

3. Methods and data Granger causality test (Granger 1969) has been generally used to find the causality relationship between variables. This test told that if past values of a variable (y) significantly contribute to forecast the future value of another variable (x) then y is said to Granger cause x. Conversely, if past values of x statistically improve the prediction of y, then we can conclude that x Granger causes y (ibid). This paper also uses the tool of the Granger causality test to examine

4. Empirical Results In this section, we discuss our findings of Engle–Granger cointegration test which we applied in order to identify whether there is cointegration relationship between dependent variable – the real non-oil GDP and independent variables - real credit to the private sector and non-oil sector real effective exchange rate. The steps of the EG approach have been undertaken in order to obtain the long-run model that explains the relationship between these variables. 4.1. Unit Root

export-led growth [ELG] hypothesis postulates a causal connection between export and growth. This study investigates ELG hypothesis using quarterly time series data for the period 1980:1-2007:2 in Turkey. The hypothesis is tested by applying the cointegration and error correction procedures. We find an evidence to support the hypothesis that there is a long-run and short-run bidirectional causality relationship between export growth and real GDP growth in Turkey. Key Words: Export-Led Growth Hypothesis

taken as the proxy for Nepalese economic growth. Some attention is necessary while employing FMOLS test. The variables under study must be cointegrated. So before applying the FMOLS we examine the cointegration by method of Johansen’s (1990) cointegration test. Prior to employing the Johansen’s Cointegration test we perform unit root test using ADF method. FMOLS method was designed by Phillips and Hansen (1990) to estimate the cointegrating regressions. This method employs a semi-parametric correction

causation also provides worthwhile information for further studies. Results of the analysis are given in Table-1. The results reject the null hypothesis (of no causality) and statistically prove causality between FDI and exports in both directions. Cointegration Relationship Test In spite of statistically significant Granger-causality between FDI and Exports, this test alone is not that strong to fully establish dependence of one variable on other. It, in fact, only establishes that explanatory variable

natural gas prices using cointegration technique Dr Salman Saif Ghouri Abstract This paper uses Augmented Dickey-Fuller and Phillips-Perron technique for determining whether individual crude oil prices (West Texas Intermediate, Brent, Japan crude cocktail) and natural gas prices- Henry Hub (HH), National Balancing Point (NBP), European and Japanese liquefied natural gas (LNG) prices are stationary or non-stationary. It then applies Johansen and Juselius cointegration technique for establishing

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)). In this part, I make the lag interval as 1 and do the Johansen’s Cointegration Test for 3 groups of data separately. This step is to test whether there is a long term relationship between those series so that we can decide whether

H0: There are no significant short-run and long-run impacts of trademarks on profit in Unilever (Gh.) Limited. H1: There are significant short-run and long-run impacts of trademarks on profit in Unilever (Gh.) Limited. H0: There are no significant short-run and long-run impacts of brand awareness on profit in Unilever (Gh.) Limited. H1: There are significant short-run and long-run impacts of brand awareness on profit in Unilever (Gh.) Limited. 3 Research Methods 3.1 Research Design The study