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Low inflation is the prime purpose of inflation targeting but if low inflation gained at the cost of low output growth, then inflation targeting may not consider as a successful policy. Based on an adaptive dynamic model, this study investigates the impact of inflation targeting on the growth rate in non-industrial countries during 1985-2010. The research sample consists of 18 countries which have adopted inflation targeting and 26 non-inflation-targeting countries as a control group. LSDVC, a dynamic panel estimator, employed to estimate the research model. The results show that inflation targeting has no significant impact of on the economic growth rate.

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The present study has examined the relationship between energy intensity and economic efficiency and specifically, the inverted U-shaped relationship between these two variables in selected countries (17 developed countries and 14 developing countries including Iran) during 1991- 2011. In order to calculate the economic efficiency, we have employed data envelop analysis window method. Then, In order to estimate the impact of energy consumption intensity on economic efficiency, method of generalized moments has been used. The results show that an increase in energy intensity to an extreme point would increase economic efficiency too. However, after that point, an increase in energy intensity would be followed by a decline in economic efficiency. So, an inverted U shape relationship between energy intensity and economic efficiency is verified for the selected countries during this period

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According to the growth literature, dozens of factors affect the growth and its process. The multiplicity of determinants of growth has caused uncertainty in the modeling and impeded consensus among the researchers. In the growth econometric theory, the theory and the chosen region dictate the effect of several specific factors. This paper has studied conventional approaches and Bayesian methods and compares these the way in which they work efficiently in determining the most effective determinants of economic growth in the Islamic cooperative countries. Therefore, Bayesian Model Averaging, Bayesian Maximum Likelihood, Fixed effects, Random effects and GMM in panel data have been used and the results have been compared. Due to differences in the economic structure of these countries, with other countries, in some area like industrialization and diversity of exports and social features, the capabilities of Bayesian approaches and conventional methods, were compared in the identification of growth factors and in this order we used data related to the Organization of Islamic cooperation countries during the period 1975-2015. According to the results, in a Bayesian space, and in the BMA approach, factors like saving rates, the credit of private sector, foreign direct investment, and exchange rate have put the robust impact on economic growth in these countries. In applying the BMA, investment has been ineffective, and the rest of the factors have affected economic growth in a different order. The results from conventional approaches are somewhat different from the results of Bayesian methods. The results of this research can be used in modeling economic growth and better management of the growth process in Islamic countries.

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Determining the factors affecting the volatility and risk of the stock market by expanding the pricing models of capital assets has been the focus of researchers. One of the variables that has a close relationship with financial markets is block chain technology. Therefore, the present study investigated the effect of block chain technology on total and systematic risk volatility in 84 selected companies of the Iran`s stock market during April 2010 to August 2014 using Vector error correction model. The results of the study show that the systematic risk function in the first period has a positive reaction to the changes in blockchain technology, but after the second period, the impact of blockchain technology shocks on the systematic risk of companies has been positive with a constant slope. Also, since the first period, the total risk function has had a positive reaction to the changes in blockchain technology with an upward slope. Systematic risk and total risk functions have also reacted positively to the shocks of the companies' market share and the company's rate of return. The results of the variance analysis of the total risk and systematic risk functions show that in the systematic risk function, according to the asset return rate, blockchain technology and market share had the greatest effect on the changes in the risk function, and in the risk function, according to the market share, asset return rate and Block chain technology has had the greatest effect on the changes of the mentioned risk function.