Granger Causality Vector Error Correction Research Articles

Determinants of carbon emissions in Pakistan's transport sector.

The transport infrastructure plays an imperative role in a country's progress. At the same time, it causes environmental degradation due to extensive use of fossil fuels. The transport system of Pakistan is largely dependent on nonrenewable energy sources (oil, coal, and gas), which are hazardous to environmental quality. This research uses an autoregressive distributive lag model (ARDL) to examine the impact of oil prices, energy intensity of road transport, economic growth, and population density on carbon dioxide (CO2) emissions of Pakistan's transport sector during the 1971-2014 period. The ARDL bounding test examines the cointegration and long-run relationships among the variables, and the directions of causal relationships are found through the Granger causality vector error correction model (VECM). The long-run results indicate that increases in oil prices and economic growth help to reduce the transport sector's CO2 emissions, while rising energy intensity, population concentration, and road infrastructure increase them, with population playing a dominant role. The findings of this study can help authorities in Pakistan to develop suitable energy policies for the transport sector. Among other recommendations, the study recommends investment in renewable energy projects and energy-efficient transport systems (e.g., light train, rapid transport system, and electric busses) and environmental taxes (subsidies) on the vehicles that use fossil fuels (renewable energy).

The relationship between electricity consumption, trade openness and economic growth in India

AbstractThe present study explores the empirical relationship between electricity consumption, trade openness and economic growth in India utilising cointegration tests and Granger causality vector error correction model for the period 1971–2016. The results of ARDL model reveal that the variables are cointegrated. The robustness of the long‐run relationship between variables is confirmed by applying Hatemi‐J cointegration model. The empirical results show that electricity consumption statistically significantly stimulates economic growth in India both in long term and short term. However, the elasticity of economic growth with respect to electricity use is found to be higher in the long term than that of the short term. Furthermore, we find the existence of a long‐term Granger causality flowing from electricity use to economic growth. In addition, the electricity consumption is also commanded by economic growth in the short‐run. As a broad policy implication, India needs to focus on the development of a sustainable mode of electricity production for achieving high economic growth in the years to come.

Debt (a real hurdle in the economic growth of Pakistan): A time series analy

This study aims to highlight the problems created by the debt (external debt) to economic growth of Pakistan. Time series data from the FY1981 to FY 2008 is used. The analysis includes five variables. Growth rate of GDP per capita is taken as a dependent variable while external debt to GDP ratio, investment to GDP ratio, population growth rate and trade openness are independent variables. The ADF- Unit Root Test is applied to check the stationarity of data. The co-integration estimation is applied, which shows the long run relationship between external debt and growth rate of GDP per capita. Furthermore, the Granger Causality Vector Error Correction (GCVEC) method has proved uni-directional relationship between external debt and growth rate of GDP per capita. There is no doubt that other macroeconomic variables also affecting the economic growth but the long term relationship of debt and economic growth has proved that the main economic indicator behind the low economic growth is debt. Key words: Debt, economic growth, Pakistan.