fotros mohammadhasan; mostafa omidali; amirmohammad galavani
Abstract
The aim of this study is to estimate the domestic balance of natural gas per capita in the Iran, as well as its forecast for the period 2017 - 2037. In this study, with employing dynamic models Autoregressive Distributed Lag (ARDL), at first, long-term and short-term elasticity of per capita natural ...
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The aim of this study is to estimate the domestic balance of natural gas per capita in the Iran, as well as its forecast for the period 2017 - 2037. In this study, with employing dynamic models Autoregressive Distributed Lag (ARDL), at first, long-term and short-term elasticity of per capita natural gas demand in Iran for the period 1981-2016 is estimated. Then with using a hybrid ARDL and ARIMA model, we predict the balance natural gas per capita up to the year 2037. The results show that amount of per capita natural gas demand will reach 4177.36 million cubic meters in 2037, as well as the amount of per capita natural gas supply will reach 3417.26 million cubic meters in this years. For responding this excess demand should be adopting policies to increase production or constrainting natural gas demand.
Mahmood Mohammadi Alamuti; mohammad reza haddadi; Younes Nademi
Abstract
Because of high reliance of Iranian economy to oil revenues, it is affected by the price volatility of the oil market. Therefore, the forecast of the oil price movement is very important at least in two aspects including determining the correct oil price in the government budget and also for controlling ...
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Because of high reliance of Iranian economy to oil revenues, it is affected by the price volatility of the oil market. Therefore, the forecast of the oil price movement is very important at least in two aspects including determining the correct oil price in the government budget and also for controlling the high price volatility for macroeconomic policy makers. Based on the importance of forecasting oil price movement, the purpose of this paper is to present an Early Warning System (EWS) for high oil price volatility in the OPEC crude oil market. This system, by forecasting the probability of staying in high volatility oil price in future periods, give a proper view of the trend of oil prices to policy makers. For this purpose, in the first step, by a Markov Switching GARCH model, the oil price trend and its volatility have been modeled and estimated during the period of 2010-2016. Then, using this model, the transition probability matrix, which involves the probability of staying in the high-volatility and low-volatility regimes, and the probability of switching between the regimes, has been obtained. Based on this matrix, the probability of being in a low-volatility and high-volatility crude oil price have been forecasted. so the policymakers and activists in the oil market can make better decisions to avoid of damaging effects of high oil price volatility
