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Developing models for accurate natural gas spot price forecasting is critical because these forecasts are useful in determining a range of regulatory decisions covering both supply and demand of natural gas or for market participants. A price forecasting modeler needs to use trial and error to build mathematical models (such as ANN) for different input combinations. This is very time consuming since the modeler needs to calibrate and test different model structures with all the likely input combinations. In addition, there is no guidance about how many data points should be used in the calibration and what accuracy the best model is able to achieve. In this study, the Gamma Test has been used for the first time as a mathematically nonparametric nonlinear smooth modeling tool to choose the best input combination before calibrating and testing models. Then, several nonlinear models have been developed efficiently with the aid of the Gamma test, including regression models Local Linear Regression (LLR), Dynamic Local Linear Regression (DLLR) and Artificial Neural Networks (ANN) models. We used daily, weekly and monthly spot prices in Henry Hub from Jan 7, 1997 to Mar 20, 2012 for modeling and forecasting. Comparing the results of regression models show that DLLR model yields higher correlation coefficient and lower MSError than LLR and will make steadily better predictions. The calibrated ANN models specify the shorter the period of forecasting, the more accurate results will be. Therefore, the forecasting model of daily spot prices with ANN can interpret a fine view. Moreover, the ANN models have superior performance compared with LLR and DLLR. Although ANN models present a close up view and a high accuracy of natural gas spot price trend forecasting in different timescales, its ability in forecasting price shocks of the market is not notable.

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Abstract

 Forecasting of crude oil price plays a crucial role in optimization of production, marketing and market strategies. Furthermore, it plays a significant role in government’s policies, because the government sets and implements its policies not only according to the current situation but also according to short run and long run predictions of important economic variables like oil price. The main purpose of this study is modeling and forecasting spot oil price of Iran by using GARCH model and A Gravitational Search Algorithm. Performed forecasts of this study are based in static and out-of-sample forecasting and each subseries data is divided in to two parts: data for estimation and data for forecasting. The forecast horizon is next leading period and its length is one month. In this study the selected models for forecasting spot oil of Iran are GARCH(2,1) and a Cobb Douglas function which is functional of prices of 5 days ago. Finally, the performances of these models are compared. For comparison of these models MSE, RMSE, MAE, and MAPE criteria are used and the results indicate that except in MAPE criterion, the mentioned criteria are smaller for GARCH model in comparison to GSA algorithm.

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This study aims to evaluate the status of Iranian gas industry and to formulate appropriate policies in order to attain the objectives of Iran’s Vision 2025. A dynamic model including exploration, production, consumption and demand sub-systems is designed based on the system dynamics approach and is simulated for the period 2010-2025. In this model, factors affecting natural gas exploration, demand and consumption as well as production, export and import of all other fuels in energy supply are identified and their dynamic interactions are investigated. The results of solving the basic model indicated that except for a 75 % share of gas consumption, none of Vision’s objectives would be attained, if current policies were followed. Accordingly, new policies are formulated and included in the model in the form of some scenarios. The results of simulating such scenarios suggest that other than coordinating the subdivisions of gas industry, production and exploration rates should be increased and significant technological exploration and production advances should be made in order to attain the objectives considered in the gas industry. Furthermore, clean energies such as water, wind and solar resources should be utilized increasingly in order to supply a part of domestic consumption. The results of model validation tests indicate the validity of the model as acceptable.

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Establishment of the Gas Exporting Countries Forum in Tehran in 2001 has proved to be one of the most important changes in the gas market. Establishment of the forum has sparked the concern among the consuming countries that a cartel is being formed in the gas market, resulting in the disturbance of supply security and gas price rise. Evidence so suggests the forum is facing fundamental obstacles to form a cartel or any other influential institution. On the other hand, considering the remarkable fall in gas prices during last months, it is necessary to present a model for determining the GECF Members Gas Export Quotas to decrease the gas supply and to increase gas prices. In this paper, we present a model which if it is applied by the GECF members we can expect that gas prices will increase. Hence in this paper first we present two mechanisms for determining the GECF member’s quotas, then considering the current situation of the members in natural gas market the optimal rationing mechanism selected. Besides, for determining the total optimal amount of production in each period as optimal total export of forum two different methods present. The first is more complicated but more accurate.

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Energy carrier’s subsidization has placed a significant pressure on government budget in Iran thus, energy price increase is performed in order to ameliorate this case. One of the main challenges that policymakers need to consider is the impact of energy prices increase on the labor market especially, when the national unemployment rate is high. This paper utilizes a computable general equilibrium model based on a Micro Consistent Matrix for 2006 in order to evaluate the impact of energy price increase on the Iranian labor market during 2006. The empirical results are based on two scenarios: Baseline and FOB price increase scenarios. They show that the activity level and demand for labor in “crude oil, natural gas, and coal” as well as “other services” sectors will increase in short-run while the energy carriers’ prices increase. However, in long-run, the labor increment will be lower. Furthermore, the model results indicate that in short-run, the activity level and demand for labor in the other sectors will decrease. On the other hand, the policy will result in a larger decrement in the activity level and demand for labor in these sectors in long-run.

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Due to the potentiality of the accumulation of atmospheric carbon dioxide and its permanent nature, the actual amount of carbon dioxide in the atmosphere, the accumulation of effective per capita carbon dioxide and the accumulation of effective per capita of this pollutant in the steady state has been estimated estimated through Kalman filter approach in a Ramsey equilibrium model over the period of 1991- 2007 for Iran. Thereby the researchers were able to estimate parameters such as the coefficient of environment cleaning for carbon dioxide, the share of fossil resources in production, the rate of time preference and the elasticity of emission function with respect to reduction activities.
   The empirical results of the study concerning the minimum, equilibrium & maximum rate of the coefficient of environment cleaning, indicate that for 1991 to 2007 in Iran the elasticity of fossil energy in production function is 0.4475, the rate of time preference is 0.12, the elasticity of emission function with respect to reduction activities is 4.45 and the coefficient of environment cleaning for carbon dioxide is 0.02. The effective per capita accumulated co2 & effective per capita accumulated co2 in steady state with the coefficient of seasonal cleaning of 0.02 respectively have the average of 50.45, 52.97 metric ton based on constant 2005 (PPP). Also the average of effective per capita consumption of the fossil fuel energy and the effective per capita capital in steady state are respectively 4.468 kg and 6.56  $ based on constant 2005 (PPP). The surpassing of the average value of the accumulation of carbon dioxide in steady state compared to its accumulation average value indicates that the accumulation path of co2 will have an increasing trend in next years.

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Natural resources as wealth in general and oil and natural gas in particular can have a potentially beneficial impact on the economic prosperity. However, economic experience implies that many of the major oil exporting countries are facing instability in economic growth, Dutch Disease, corruption and under- development. Owing to the fact that natural resources can play a vital role in development, the present study tries to investigate the econometrics relationship between export of natural resources (as a proxy for abundance) and governance indicators (as alternative variables for institutional development) in selected oil-exporting and OECD countries through the application of Generalized Moment of Method (GMM), for the period lasting from 1996 to 2011. Findings of the study revealed that the strong and statistically significant evidence confirms the negative impact of the export of natural resources on the governance index, quality of regulations, rule of law and control of corruption in the selected OPEC’s member countries. Nevertheless, such a negative impact does not have any statistically significant strength in developed countries. This is due to the improvement made in the surveillance, technical and executive mechanisms of the institutions in the selected OECD countries. It seems that the enormous incomes accrued from the export of natural resources in the oil producing countries in question will induce a decrease in transparency and accountability, instability and frequent changes in economic policies, extension of rent-seeking, corruption and authoritarianism.

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Public pricing of products is one of the most important economicalissues, since any changes in the pricing, affects both the welfare ofconsumers and quantity of goods and Services which are produced.
In this paper which is done for natural gas pricing  in Iran, the purpose is giving a price that the government can consider it as a suitable choice for using in subsidies targeting project. These prices have two advantages: first, they try to maximum the social economical welfare (summation of producer and consumer surplus) second, this method solve the problem that the producer has in covering their costs (by marginal cost pricing) because of increasing returns to scale.
This paper deals with the optimal gas pricing in household sector in Iran by the Ramsey method of pricing.
In this regard we have used fuzzy regression (because of its accuracy and devoid of classic regression restrictions) and the data from 1977 to 2011 for estimating production function and returns to scale in natural gas production side. Also for estimating demand function and elasticity we have used ARDL method and data from 1350 to 1389. The results shows that the current prices aren’t optimum and despite implementation of subsidies targeting project the prices are low.

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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.

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This paper aims at estimating Geometric Brownian Motion (GBM) Model, based on two central parameters in this model (volatility and drift), and forecasting Henry Hub natural gas daily spot prices (07/01/1997-20/03/2012). Researches reveal that two mentioned parameters estimation can be satisfied with different approaches and in various time scales. Therefore, two approaches of backward looking and forward looking have been used in different time scales and sub-periods. Results show that the volatility and drift values are highly dependent on the time scale and backward results are lower than the forward ones. Moreover, along with increasing the number of random runs of the model although the fluctuating range decreases, the predicted line slope is very close to the actual line. Ultimately, the performance evaluation criteria yields that forward method, clearly in 2009, has the best performance. The sub-periods of 2001-2004 in backward and forward methods have the next best performances, respectively. These sub-periods can be used as a basis for calculating the central parameters of the model. In addition, the results suggest that relying on data used in the most recent period is not sufficiently accurate. Also, it is observed that sub-periods or time scales with higher volatility show better performance evaluation criteria, therefore they can be applied in price forecasting with GBM model.

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As one of the important energy forms, natural gas consumption has an upward trend in recent years. Therefore management and planning for provision of it requires prediction of the future consumption. But many of prediction procedures are inherently stochastic therefore it is important to have better knowledge about the robustness of prediction procedures. This paper compares robustness of two prediction procedures Artificial Neural Networks as a nonlinear and ARIMA as a linear model. using resampling method to predict the monthly consumption of natural gas in the household sector. Data spans from 2001-4 to 2012-3, to train the networks, we used genetic algorithms and Particle Swarming Optimization then results were compared using 10-fold method. According to the results, the particle swarm optimization (PSO) outperforms the genetic algorithm. Then we used data from 2001-4 to 2010-3, with resampling by 2000 to predict the  natural gas consumption for the 2001 -4 to 2012-3 and to form critical values. Results show that prediction by a mixed method using ANN and PSO is more robust than ARIMA method.