Cointegration Test

Running header: Analysis 1 Student Name: Student ID: Institutional Affiliation: Course: Instructors Name: Date: Running header: Analysis 2 Analysis This chapter will give adeeper interpretation of what is observed from the results and at the same time answering the main objectives of the study; determining the association of discharge of carbon, consumption of energy and the development of economy in SE Asian countries. The results after partaking the Unit root test for stationarity having all the null hypotheses rejected, itmeans there is stationarity. This elaborates in the wider perspective that, there is a relationship between emission of CO2, the consumption of energy in acountry, the GDP per head, trading activities and the urban population setup. The presence of stationarity or rather the absence of aunit root enables the future prediction of events like acountries ’economy given the variables like CO2 emission, Energy, GDP and Trade. The Durbin Watson statistic results have their values ranging between 0and 2, and therefore depicts an absence in negative autocorrelation. An absence in negative autocorrelation is literally the presence of positive autocorrelation. By the analysis of our results, we can confidently and statistically deduce that there is an autocorrelation between emission of CO2, Consumption of energy in acountry, trading activities and the urban population setup in determining the GDP of every country. Stationarity is considered present if any shift in time does not cause achange in the shape of the distribution, this is the property that enables aresearcher to predict the series since its properties will still be intact in the future as compared to the past. The emission of CO2, definitely has an effect on the growth of an economy. The Granger-Causality test which is anonparametric test used in testing astatistical-hypotheses, for determining whether atime series is useful in forecasting the efficacy of one time series for another, had the following results. Having performed the test in two lag levels, for the first level lag test, we find that Trade Granger-Cause CO2, this implies that trading activities all over the country leads to the emission of CO2 in certain amounts of volume. We also observe that Energy Granger-Cause GDP. This implies that, the rate of energy consumption in an economy literally has effect to the GDP both in the negative or positive direction; all depends on the amount of energy consumed. We also observer that GDP Granger-Cause Trade. Analyzing from each individuals annual per capita income, asolid conclusion is made in that an increase in GDP, will definitely lead to an increase in per capita income and definitely there will be an automatic rise in trading activities within the country. We also observe that GDP_SQ Granger-Cause Trade. Running header: Analysis 3 This implies that GDP in squared constant US dollar has apositive effect on trading activities across the country. An interesting combination is when we observe UP Granger-Cause Trade and Trade Granger-Cause UP. This is an all way possibility of either of each having an effect on the other and vice versa. An urban population has apositive effect to trade since there will be arise in both demand and supply of goods and services. The vice versa is also the same since a favorable trading condition automatically leads to an increase in urban population. In the two- lag Granger-Causality test, we observe that GDP_SQ Granger Cause Trade. This implies a doubling of the US dollar value increases trading activities. We therefore conclude that there is no inter relationship between the series being regressed. For the Cointegration test, when two time series are individually integrated, but some linear combination of these series has alower order of integration, then the series are said to be cointegrated. For this study, we conclude that there is cointegration between the variable series. But cointegration is so far the best technique for establishing relation between variables. The pedroni residual cointegration test was applied to determine multiple linearly independent cointegrating factors. The GDP is clearly linearly independent of Trade, GDP, CO2 emission, and urban population. The AR(1) and the variance values for each cross section is the same. On the basis, when we reject the null hypothesis, we conclude that there is cointegration between the variable series The regression test results therefore, have the coefficients being significant except the foreign trade levels. The impact of foreign trade levels therefore do not have asignificant impact on the CO2 levels. This can be explained by the fact that the domestic consumption of the countries can be high enough compared to the foreign exports that the overall CO2 levels are high. All the coefficients are negative. This is not in line with the theoretical review as all the factors like energy consumption, rise in GDP, increase in urban population as well as increasing level of foreign trades should add to the CO2 levels in the air. However, the constant C is very high and all the coefficients are significant at 95% level and above. This can be attributed to more of an equation balancing act by the constant value. The regression value of rsquared (0.66), means that the model equation can explain 66% of the variations in the dependent variable. The adjusted rsquared value has increased indicating that there is little scope of results getting better Running header: Analysis 4 with the addition of avariable. The study therefore can be marked as being successful based on the results. References Ahmed, K., Bhattacharya, M., Shaikh, Z., Ramzan, M. and Ozturk, I., 2017. Emission intensive growth and trade in the era of the Association of Southeast Asian Nations (ASEAN) integration: An empirical investigation from ASEAN-8. Journal of Cleaner Production ,154 ,pp. 530-540. Apergis, N. and Ozturk, I., 2015. Testing environmental Kuznets curve hypothesis in Asian countries. Ecological Indicators ,52 ,pp, 16-22. Behera, S.R. and Dash, D.P., 2017. The effect of urbanization, energy consumption, and foreign direct investment on the carbon dioxide emission in the SSEA (South and Southeast Asian) region. Renewable and Sustainable Energy Reviews ,70 ,pp. 96-106. Bekhet, H. A. and Othman, N. S, 2017. Impact of urbanization growth on Malaysia CO2 emissions: Evidence from the dynamic relationship. Journal of cleaner production ,154 ,pp .374- 388. Özokcu, S. and Özdemir, Ö.,2017. Economic growth, energy, and environmental Kuznets curve. Renewable and Sustainable Energy Reviews ,72 ,pp. 639-647. Pablo-Romero, M.D. P. and De Jes ús, J., 2016. Economic growth and energy consumption: The energy-environmental Kuznets curve for Latin America and the Caribbean. Renewable and Sustainable Energy Reviews ,60 ,pp. 1343-1350. Pala, A., 2016. Which energy-growth hypothesis is valid in OECD countries? Evidence from panel Granger causality. International Journal of Energy Economics and Policy ,6(1). Saidi, K. and Hammami, S., 2015. The impact of CO2 emissions and economic growth on energy consumption in 58 countries. Energy Reports ,1,pp. 62-70.