فصلنامه تحقیقات مدلسازی اقتصادی
سال ششم شماره 22 (زمستان 1394)

Regarding to the importance of the relationship between macroeconomic instability and exchange rate pass-through, present study by using EGARCH and smooth transition regression (STR) model has examined the nonlinear effect of macroeconomic instability on the exchange rate pass-through of Iran during the period 1963-2010. For this, firstly the macroeconomic instability index has been estimated using EGARCH and then, by using STR, the research hypothesis which is that the macroeconomic instability has a nonlinear and positive effect on the exchange rate pass-through has been examined. Based on the obtained results in this research, macroeconomic instability has the macroeconomic instability has a positive effect on the exchange rate pass through in both regimes, although an increasing in volatility increases rate pass-through. So, the sequence of economic policies is important and specifically, we suggest that macroeconomic instability reduction policies should be prior to exchange rate policies.

Structural parameters are necessary and important in some economic studies, especially in general equilibrium models. One of these structural parameters is degree of price rigidity. In this article we try estimate degree of price rigidity in Iran economy in a General Equilibrium Dynamic Stochastic Model with Bayesian method. Our result with using seasonal data of real consumption, GDP, inflation and taxes for 1377-1387 reach us to number 46 percent for price rigidity which show 46 percent of Iran's firms could not optimize their price in each period.

In capital markets, stock price forecasting is affected by variety of factors such as political and economic condition and behavior of investors. Determining all effective factors and level of their effectiveness on stock market is very challenging even with technical and knowledge-based analysis by experts. Hence, investors have encountered challenge, doubt and fault in order to invest with minimum risk. In order to reduce cost and raise the profit of investment, determining effective factors and suitable time for sailing and purchase is one of the important problems that every shareholder or investor in stock market should consider. To reach this goal, a variety of approaches have been introduced, which are often intelligent, statistical, and hybrid. These approaches are mostly used to predict the stock price time series. Our proposed algorithm is hybrid and involves two stages: preprocessing and predictor. The preprocessing stage involves three steps: missing value, normalization and feature selection. Since there are many features in used datasets, genetic algorithm (GA) is used as the feature selection algorithm. In order to intelligent capability of Fuzzy Neural Network (FNN), this network with two structures (Mamdani and Sugeno) is used as a stock price prediction in second stage. This network is capable of extracting fuzzy rules automatically. Back propagation algorithm (gradient decent) is used for adapting all the parameters.
Our algorithm is evaluated on ten datasets with seven features obtained from ten different companies. By comparing the simulation results of the simple and hybrid FNN network, we found that the lack of suitable feature selection algorithm will lead to high computational cost, and in many instances the hybrid algorithm outperforms the simple FNN. This results demonstrate the superiority of the hybrid FNN to the simple one. In general, since the number of Sugeno tuning parameters are more than Mamdani, its performance is better than mamdani. Moreover, our algorithm is comparable to the maximum precision rates of other approaches.

In recent decades the development of capital markets in developing countries, economic growth is desirable to have. Developed countries owe much of its development direction of financial markets, especially the stock market knows. The stock market is precisely the collection of savings and private capital to finance investment projects and on the other hand, an official and is confident that the owners of dormant savings can be relatively affordable and safe place to seek investment and their funds to invest in companies operate. The role of the stock market to boost the economy of countries like Iran and wandered from one side to the large amounts of capita and on the other hand, face a shortage of investment, is striking. Therefore, understanding the factors influencing the behavior of the stock market can be considered useful for the capital's economy. In this context, this study examines the impact of fiscal and monetary policy shocks on stock market Iran. Regression model to estimate the structural model and the data for seasonal 1991: 1-2010: 4 was used. The results of the model indicate that the short-term shock to the money supply (monetary policy instrument) and long-term government spending shocks (monetary policy instrument) Fluctuations of stock price indices explain. In other words, the impact of monetary policy on stock prices faster than the impact of fiscal policy. Because government spending through the stock market affects ,First government spending on aggregate demand and thus income consumers and the general level of prices affects subsequent stock price changes, but by changing the money supply, the faster people can spend their surplus cash available to purchase the stock of assets that form part of it. The lag effect of monetary policy is much shorter than the lag effect of monetary policy

In recent years, the sudden increase in environmental awareness has resulted in more attention to this sector. On this basis, the economic load distribution models, that previously observed merely the minimization of the cost of production and determination of optimal arrangement of producers based on minimization of the total cost, are now facing a fundamental change in execution and modeling. Based on this, the optimal arrangement of producers will now be determined based on two objectives of a minimum cost of production and a minimum environmental pollution. Obviously, with the situation in mind, the problem changes from a single- objective one to a multi-objective problem. The present study takes into account the question of optimal economic and environmental distribution, and its goal is to determine the optimal arrangement of producers in a situation where both the economic and environmental objectives are achieved. The model has been implemented by E-Constraint algorithm. The modeling in this study has been performed for the practical development in Esfahan Electricity Inc. market, in 2012. The results from this model show that the real performance of the market is different from the economic and environmental optimums. The results show the fact that because of the disregard for the environmental costs, the real deviation of performance from the optimum condition is practically much more serious and extensive in the environmental sector.

This study examines the effect of Herding in different states (low, high and extreme volatility) in Tehran Stock Exchange during the years 2009-2013 using Chang et al (2000) and Balcilar et al (2013) models. In this survey herding are tested under 3 market regimes in selected industries: Cement, Chemical, Pharmaceutical and Investment.
The results dont show evidence of herding in 4 industries using static model (Chang et al, 2000). So dynamic model (Balcilar et al, 2013) was used to analyze Herding under 3 regimes in which our results support the presence of herding under 2 market regimes (high and extreme) . The results also demonstrate evidence of herding behavior under the high volatility regime for all of the selected industries. Herding under the extreme volatility regime is only found in investment and cement industries.

One of the greatest challenges of renewable resources is unpredictable nature of these resources. Nevertheless use of fossil-renewable integrated hybrid system, which uses some renewable resources rather than a single source, for the supply of power, is the most affordable and the most reliable method. In this study by use of analytical programming approach and 2012 base year statistics, production system of Khorasan Regional Electricity CO. has been simulated and the maximum renewable electricity potential, entered into power generation system. Results show that among all of solar, wind, biomass, geothermal and hydro, only solar power have enough capacity and potential to be substituted with fossil power. The comprehensive system, which uses all renewable potential power capacity, causes 6.38 TWh reduction in fossil power, 4.28 million tons emission, 10-fold increase in spot-hourly price and 21% reduction in grid stability which shows necessity of using stabilizer and storage equipment in the hybrid integrated production system and Technical and financial support from the government to reduce the cost of solar equipment.