نشریه تحلیل های اقتصادی توسعه ایران
سال چهارم شماره 2 (پیاپی 11، تابستان 1395)

Prediction of variables is one of the main tasks of different sciences including economics. According to the existing literature, the Leontief demand-side model (LDM) and Ghosh supply-side model (GSM) are used to predict sectoral output. The prerequisite for predicting sectoral output is to identify the mature and immature sectors. In this paper, we predict sectoral output for two periods: First, we predict output in 1999 based on the output structure in 1988, as the endpoint of war and beginning of the reconstruction of economy. Then, we predict output in 2004 based on the output structure in 1999, as the initial point of post-reconstruction of the economy. For two periods mentioned, LDM provides low error in predicting aggregate output and is of more predictability than GSM. In addition, for the second period. i.e., post-war and construction period, GSM compared to LDM only provides low error in predicting output in mining sector among agriculture, construction and mining sectors. Therefore, mining sector appears as mature sector, and agriculture and construction sectors are considered as less-matured sectors.

This study aims to identify factors influencing performance of customer relationship management (CRM) in social media. To do this, first, we review literature and theoretical basics, and then we test a comprehensive set of variables influencing performance of CRM in social media among the Iranian selected banks. Management experts from three universities, i.e., Al-Zahra University, Tarbiat Modares University, and University of Tehran assessed and revised the research model and its measurement indexes. We collected data on the indexes through questionnaires filled by 122 experts of information and communication technology, and marketing from six selected banks in Iran. We analyzed data by structural equations using SPSS and PLS programming packages. The findings indicate significant relationships among customer and organizational performance, and CRM within social media. Despite the depending of bank funding to CRM systems, it seems that banks have failed to attain significant achievements in various functional aspects.

In this study, two algorithms, neural networks and fuzzy neural networks, are used to predict the stock prices, and two memetic algorithms, particle swarm and genetic algorithm, together with quadratic method are used to solve unbounded optimization problem by applying daily data of 23 listed companies on Tehran Stock Market during 2012-2015. The findings show that artificial neural networks have better performance than fuzzy neural network in predicting yields. In addition, the comparison of performances of quadratic, genetic and shuffle frog Leaping (SFL) algorithms reveal that SFL algorithm has recorded better performance than genetic and quadratic algorithms. As a result, artificial neural network may be a reliable algorithm for shareholders.

The Middle East countries, especially Persian Gulf and North Africa economies, are dependent on crude oil exports. As a result, volume of export, exchange rate and other macroeconomic variables, in particular government budget are volatile, and these oil-based economies are vulnerable to external shocks. On the other hand, the mentioned countries are reliant on the strategic imported commodities such as food and medicine. These factors lead to the more economic vulnerability among these countries especially I.R. Iran. The notification of the general policies of the resistive economy by Iran’s supreme leader in 2010 shows that this subject is of great importance. Therefore, the study of the economy of Iran and its place in the region is the first step in determining the economic indicators from economic vulnerability and resilience viewpoints. This paper tries to answer two basic questions: first, what economic conditions do OPEC countries, including Iran, experience based on the economic vulnerability and resilience? Second, what relationships are there among gross domestic product, economic vulnerability index and economic resilience index among the selected countries? To answer these questions, we compute vulnerability and resilience indexes for the selected OPEC countries during the 2002 - 2013. The findings show that Algeria, Ecuador, Iran, Nigeria and Venezuela are in the “prodigal son” position but they have the potential to increase the economic resilience during the next years. Kuwait stands at the best status, and United Arab Emirates (UAE) gets the self-reliant place. In addition, the economic vulnerability and economic resilience have indirect and direct relation with the gross domestic product, respectively; and the effect of economic resilience index on GDP is high in the sample countries.

One of the main economic tasks of governments is to support low-income groups and to distribute incomes fairly. Achieving this goal requires the use of economic instruments such as taxes. This study aims to investigate the effect of indirect taxes on income distribution based on a computable general equilibrium (CGE)model. Today, this model is known as one of the best tools for assessing economic shocks and policies. Concerning the subject of the study, three general scenarios are simulated: 1) Taxes on import; 2) Exemption of imports from taxes; 3) Subsidies to import. The effects of these policies on the personal and functional distribution of income are calculated. The findings show that indirect taxes change income distribution at the expense of urban households, and improve the income distribution. Subsidies to imports change income distribution to the detriment of rural household, and increase inequality. Applied scenarios upon the functionalincome distribution indicate that firstly, capital income increases more than labor income in the scenarios 1 and 2; and secondly, the payment of subsidies reduces the capital income and increases the labor income.

Regarding the importance of stock market in the economy, this study examines the effects of fluctuations in oil price and foreign exchange rate on stock exchange price index (SEPI) in Iran. For this purpose, we use the seasonal data of variables under study. i.e., stock exchange price index, black market exchange rate, and oil price, during 1992-2014. We also use autoregressive conditional heteroskedasticity (ARCH) method to compute oil price and exchange rate volatilities. We estimate the coefficients of explanatory variables affecting SEPI using autoregressive distributed lags (ARDL) method and check the existence of long-term relationship among variables using bounds test. Findings indicate that the fluctuations of oil price and foreign exchange rate have significant and negative impacts on SEPI, and inflation and money volume affect positively SEPI.