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Showing 2 results for Khorsandi

Mehran Amirmoeini, Teymour Mohammadi, Morteza Khorsandi,
Volume 5, Issue 18 (12-2014)

This paper tries to model the electricity demand in Iran’s industrial sector which captures economic factors and also non-economic exogenous factors. The structural time series model (STSM) approach is employed which allows using economic theory and time series flexibility. In this approach the role of UEDT (Underlying Energy Demand Trend) including technological improvement and structural changes is modeled, therefore the income and price elasticity are estimated more accurately. The results show that the UEDT has the stochastic nature. And UEDT has a great impact on industrial energy demand during 1975-2012. So, the electricity has not been used efficiently in this sector. In the short run the estimation of the income and price elasticity are 0.42 and 0.11 respectively. The value of the cross-price elasticity of electricity demand is estimated about 0.06. It shows that natural gas substitute electricity in industrial sector, however it is small.
Navid Salek, Morteza Khorsandi,
Volume 13, Issue 47 (5-2022)

The price of crude oil is one of the factors affecting economic indicators. Therefore, the prediction of oil prices and the accuracy of the applied methods have always been discussed by economists. In this study, the effect of all effective variables on the supply and demand of crude oil based on McAvoy's competitive theory is investigated, and the supply and demand are estimated using the system of simultaneous equations and conventional statistical methods. Then, using algebraic operations and the assumption of equality of oil supply and demand in the long term, the long-term potential of oil supply and demand is extracted with respect to each of the variables in the model. Based on the results, the world's gross domestic product (GDP) has the greatest impact on oil prices with a demand potential of 0.6039, and the world's military and security tensions have the least impact with a demand potential of –0.0110. After estimating the model, the prediction accuracy of three combined mothod is compared with conventional and single-variable methods of neural network and ARIMA. These three combined methods are: (a) neural network and system of simultaneous equations, (b) ARIMA and system of simultaneous equations, (c) neural network and ARIMA and system of simultaneous equations. The results showed that the combined method of ARIMA and simultaneous equation system provides better reslts for 5-year forecasts while the combined method of neural network and ARIMA and simultaneous equation system shows better results for 10-year forecasts.

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