Forecasting Greenhouse Gas Emissions and Sustainable Growth in Montenegro: a SVAR Approach

This paper uses a recursive structural vector autoregression method to investigate and forecast the linkage and causality between greenhouse gas emissions (GHG) and Gross Domestic Product (GDP) in Montenegro empirically from 2006:1 to 2015:12, and out-of-sample 24-month horizon forecasting from 2016:1 to 2017:12. It is the first time that GDP and GHG are modeled and predicted for the economy of Montenegro using the SVAR approach. We examine an individual SVAR model to forecast GDP. The model uses GDP growth and GHG emissions expressed in CO 2 eq by sectors as endogenous determinants. The GHG sectors are energy, industrial process, agriculture and land, and waste. Alternative forecasting scenarios, impulse response functions and variance decomposition of forecast errors are interpreted in combination with expectations. We reveal that the sectors of agriculture and land and energy contribution explain 83.41% of the movement of GDP at the 24-month horizon.