University of TehranJournal of Economic Research (Tahghighat- E- Eghtesadi)0039-896950420151222Examine the causality between financial development and economic growth indicators in Iran: a Markov model approach for nonlinear Markov switching MS-VARExamine the causality between financial development and economic growth indicators in Iran: a Markov model approach for nonlinear Markov switching MS-VAR7778065614510.22059/jte.2015.56145FAHosseinAsgharpurAssociate Professor University of TabrizAliMehdilooPh.D Student of University of TabrizJournal Article20140906The relationship between financial development and economic growth in order to give priority to policies that will lead to financial development or addressing other economic priorities to achieve economic growth has attracted the attention of many economics. Nonlinear models of causality and causal relationships between variables in the modification of variables in different regimes exist, why can have better results than linear causality models. For this purpose, in this study using a Markov switching model to investigate the nonlinear causality between financial development and economic growth during the years 1973-2010 have been paid. GDP per capita and Composite index of four indicators towards depth and credits granted to the private sector, the ratio of credit provided by the banking sector and gross domestic savings to GDP used for economic growth and finance development. The results of this study indicate that in the years of high economic growth (regime 1) theory, be honest and follow the path of economic growth, the demand is financial development and for the years of low economic growth (regime 2) does not make the link between financial development and economic growth in both regimes, financial development has no effect on economic growth.The relationship between financial development and economic growth in order to give priority to policies that will lead to financial development or addressing other economic priorities to achieve economic growth has attracted the attention of many economics. Nonlinear models of causality and causal relationships between variables in the modification of variables in different regimes exist, why can have better results than linear causality models. For this purpose, in this study using a Markov switching model to investigate the nonlinear causality between financial development and economic growth during the years 1973-2010 have been paid. GDP per capita and Composite index of four indicators towards depth and credits granted to the private sector, the ratio of credit provided by the banking sector and gross domestic savings to GDP used for economic growth and finance development. The results of this study indicate that in the years of high economic growth (regime 1) theory, be honest and follow the path of economic growth, the demand is financial development and for the years of low economic growth (regime 2) does not make the link between financial development and economic growth in both regimes, financial development has no effect on economic growth.https://jte.ut.ac.ir/article_56145_7f392a7e27d125e2b6abec749192abe1.pdfUniversity of TehranJournal of Economic Research (Tahghighat- E- Eghtesadi)0039-896950420151222Study of Economical Compatibility in the Eurozone and the Role of it in Euro CrisisStudy of Economical Compatibility in the Eurozone and the Role of it in Euro Crisis8078345614710.22059/jte.2015.56147FASayyed AbdolmajidJalaeeProfessor in economics department at Bahonar university of KermanSepidehSamimiPh.D Student in Shahid Bahonar University of KermanJournal Article20150311One of the necessary preconditions to the establishment and stability of optimal currency area is the homogeneity of the economic structure in Member States. But Place with Eurozone currency crisis, the question arises whether the economic structure of the Eurozone countries were homogeneous? Accordingly, this study by using an Econometrics model and estimating it by panel data method, has been discussed this issue. The results show that the level of economic development has a critical role in multiplicity of economic factors affecting the intensifying of business fluctuations and the currency crisis. Accordingly, Germany, in terms of enjoyment a current account surplus and high GDP per capita, is considered as the pattern country in the area. Then we investigated the effects of reduction of structural differences between Germany and other countries on the business fluctuation. The results show that in the countries including; Belgium, Finland, France, Italy and Spain, reducing the difference with Germany (increase) in credit of the banking sector, savings, consumption and labor force participation rates, will intensify trade volatility in these countries. Also, in countries including Cyprus, Greece, Portugal, Estonia, Slovakia, Malta, Latvia, Slovenia, reducing the difference with Germany (increase) in banking sector credit, government spending and fixed capital formation, will led to increase the volatility of balance trade in these countries.One of the necessary preconditions to the establishment and stability of optimal currency area is the homogeneity of the economic structure in Member States. But Place with Eurozone currency crisis, the question arises whether the economic structure of the Eurozone countries were homogeneous? Accordingly, this study by using an Econometrics model and estimating it by panel data method, has been discussed this issue. The results show that the level of economic development has a critical role in multiplicity of economic factors affecting the intensifying of business fluctuations and the currency crisis. Accordingly, Germany, in terms of enjoyment a current account surplus and high GDP per capita, is considered as the pattern country in the area. Then we investigated the effects of reduction of structural differences between Germany and other countries on the business fluctuation. The results show that in the countries including; Belgium, Finland, France, Italy and Spain, reducing the difference with Germany (increase) in credit of the banking sector, savings, consumption and labor force participation rates, will intensify trade volatility in these countries. Also, in countries including Cyprus, Greece, Portugal, Estonia, Slovakia, Malta, Latvia, Slovenia, reducing the difference with Germany (increase) in banking sector credit, government spending and fixed capital formation, will led to increase the volatility of balance trade in these countries.https://jte.ut.ac.ir/article_56147_96c9942bac91440f954478d1ad857eac.pdfUniversity of TehranJournal of Economic Research (Tahghighat- E- Eghtesadi)0039-896950420151222The Impact of Spatial Interaction Effects Neighboring on Fluctuations of Trade: Spatial Panel data Econometric Method and Wavelet SmoothingThe Impact of Spatial Interaction Effects Neighboring on Fluctuations of Trade: Spatial Panel data Econometric Method and Wavelet Smoothing8358595614810.22059/jte.2015.56148FAMansurZarra NezhadProfessor of Shahid Chamran University of Ahvaz0000-0001-9981-2196Sayed AminMansouriPh.D. of EconomicJournal Article20141215The main objective of this study is to evaluate the spatial effect on fluctuations of trade based on the Spatial Panel data Econometric Method and Wavelet Smoothing using bilateral trade weighted matrix. The negative spatial dependent are estimated. Using data from 34 most-important countries the period of 1980-2010 are investigated through Spatial Model and Maximum likelihood Estimation method (ML). So that, an increase by one percent in trade fluctuations in neighboring countries causes 0.62 percent increase in trade fluctuations of each country in the reverse direction, respectively. Evaluation and interpretation of the results of spillovers elasticity of trade fluctuations showed that with the sudden increase in growth rates, prices and GDP of neighboring countries and with a sudden drop in income, the nominal exchange rate, the share of imports of raw and intermediate from total imports and the share of agricultural and food exports of total exports and geographical concentration of neighboring countries, trading will be reduced in the own countries. Comparison of the spatial estimation with standard regression estimates showed that the spatial effects cause OLS model face with the bias.The main objective of this study is to evaluate the spatial effect on fluctuations of trade based on the Spatial Panel data Econometric Method and Wavelet Smoothing using bilateral trade weighted matrix. The negative spatial dependent are estimated. Using data from 34 most-important countries the period of 1980-2010 are investigated through Spatial Model and Maximum likelihood Estimation method (ML). So that, an increase by one percent in trade fluctuations in neighboring countries causes 0.62 percent increase in trade fluctuations of each country in the reverse direction, respectively. Evaluation and interpretation of the results of spillovers elasticity of trade fluctuations showed that with the sudden increase in growth rates, prices and GDP of neighboring countries and with a sudden drop in income, the nominal exchange rate, the share of imports of raw and intermediate from total imports and the share of agricultural and food exports of total exports and geographical concentration of neighboring countries, trading will be reduced in the own countries. Comparison of the spatial estimation with standard regression estimates showed that the spatial effects cause OLS model face with the bias.https://jte.ut.ac.ir/article_56148_dab47c1dd906a81be2fce73a93aca440.pdfUniversity of TehranJournal of Economic Research (Tahghighat- E- Eghtesadi)0039-896950420151222Economic Capacity Utilization Rate in Iran’s
Electricity Generation Industry
During 1350-1388Economic Capacity Utilization Rate in Iran’s
Electricity Generation Industry
During 1350-13888618795614910.22059/jte.2015.56149FAAhmadSeifiFaculty Member of Ferdowsi UniversityMohammad RezaDehghanpoorMaster’s Graduate in EconomicsJournal Article20131223This study aims to estimate economic capacity and, in turn the utilization rate of economic capacity in Iran’s electricity generation industry during 1350-1388. Aggregate time series data for thermal power plants are used. With parametric restrictions for linear homogeneity in input prices imposed, capital demand function is first derived and estimated. This allows the derivation of economic capacity. Then, the utilization rate of economic capacity in Iran’s electricity generation industry is obtained by dividing actual generation by economic capacity. Finally, the modeling and estimation of the (economic) capacity utilization rate function permits us to investigate the effects of various factors on the utilization rate of the generation industry. Our results point to the existence of a large difference between utilization rates as defined by engineering and economic perspectives. The economic capacity utilization rate for most years during the period of analysis is smaller than one, indicating actual generation is less than economic capacity.This study aims to estimate economic capacity and, in turn the utilization rate of economic capacity in Iran’s electricity generation industry during 1350-1388. Aggregate time series data for thermal power plants are used. With parametric restrictions for linear homogeneity in input prices imposed, capital demand function is first derived and estimated. This allows the derivation of economic capacity. Then, the utilization rate of economic capacity in Iran’s electricity generation industry is obtained by dividing actual generation by economic capacity. Finally, the modeling and estimation of the (economic) capacity utilization rate function permits us to investigate the effects of various factors on the utilization rate of the generation industry. Our results point to the existence of a large difference between utilization rates as defined by engineering and economic perspectives. The economic capacity utilization rate for most years during the period of analysis is smaller than one, indicating actual generation is less than economic capacity.https://jte.ut.ac.ir/article_56149_9d40065f558876d32f30aaa264f4ec87.pdfUniversity of TehranJournal of Economic Research (Tahghighat- E- Eghtesadi)0039-896950420151222Study of Iran’s Position in the world trade: A network approachStudy of Iran’s Position in the world trade: A network approach8819025615010.22059/jte.2015.56150FAHomayounShiraziPhD candidate of Economics, University of IsfahanKarimAzarbaiejaniProfessor of Economics, University of IsfahanMortezaSametiProfessor of Economics, University of IsfahanJournal Article20141125In the last decade, many applied common studies were done in economic social systems phenomena by using network analysis. In these studies, trade relations can be considered as a network that each vertex represents a country and the trade relations between countries represent links of this network. <br />First, we try to make the world exporting and importing networks based on the existence data for 104 countries for the years 2000, 2005, 2010 and 2011 and then network properties and topology will be studied based on the network science. Studying the role of Iran and its positions in these trade networks will be calculated and we compare Iran with other important countries in these networks.<br />Based on the obtained results, all of the constructed trade networks have power distribution and high clustering coefficient in all years and this study confirms that the world trade networks are complex networks. In addition, based on the obtained results from betweeness centrality, Iran is a middle class country in the world trade network. Also, Iran’s score in eigenvector centrality confirms that contrary to the important countries’ trade partners in these trade networks, most Iran’s partners do not trade much with other countries in the network.In the last decade, many applied common studies were done in economic social systems phenomena by using network analysis. In these studies, trade relations can be considered as a network that each vertex represents a country and the trade relations between countries represent links of this network. <br />First, we try to make the world exporting and importing networks based on the existence data for 104 countries for the years 2000, 2005, 2010 and 2011 and then network properties and topology will be studied based on the network science. Studying the role of Iran and its positions in these trade networks will be calculated and we compare Iran with other important countries in these networks.<br />Based on the obtained results, all of the constructed trade networks have power distribution and high clustering coefficient in all years and this study confirms that the world trade networks are complex networks. In addition, based on the obtained results from betweeness centrality, Iran is a middle class country in the world trade network. Also, Iran’s score in eigenvector centrality confirms that contrary to the important countries’ trade partners in these trade networks, most Iran’s partners do not trade much with other countries in the network.https://jte.ut.ac.ir/article_56150_fc715d405c29b0257c9c36ab214aaec2.pdfUniversity of TehranJournal of Economic Research (Tahghighat- E- Eghtesadi)0039-896950420151222Estimate of investment risk in an asset portfolio in IranEstimate of investment risk in an asset portfolio in Iran9039235615110.22059/jte.2015.56151FAFaramarzTahmasebiFaculty member of Payam Noor University, Zanjan Khodabandeh - Department of Economics0000-0003-3628-8678Journal Article20150411criteria in household portfolio. To do this, the data which are related to the asset price are used including: bank deposit, bonds, stock, exchange, coin, land and housing in time period of 1997 to 2011. In this research, portfolio VaR id calculated in the confidence level of 90%, 95%, and 99% and in time periods of one year and 14 years. After calculating returns, return standard deviation, correlation coefficient among assets, VaR of every asset is extracted by using Variance- mean model, MATLAB software, and optimal mix of assets in household portfolio. Assets portfolio risk is calculated by VaR method. The result indicated that in the time period of 14 years, there is the most portfolio risk of 43/77% with the probability of 99% for high risk people and the lowest portfolio risk of Zero% with the probability of 90% for low risk people. In one year period, there is also the most portfolio risk of 16/92 with the probability of 99% for high risk people and the lowest portfolio risk of 0/13% with the probability of 90% for low risk people.criteria in household portfolio. To do this, the data which are related to the asset price are used including: bank deposit, bonds, stock, exchange, coin, land and housing in time period of 1997 to 2011. In this research, portfolio VaR id calculated in the confidence level of 90%, 95%, and 99% and in time periods of one year and 14 years. After calculating returns, return standard deviation, correlation coefficient among assets, VaR of every asset is extracted by using Variance- mean model, MATLAB software, and optimal mix of assets in household portfolio. Assets portfolio risk is calculated by VaR method. The result indicated that in the time period of 14 years, there is the most portfolio risk of 43/77% with the probability of 99% for high risk people and the lowest portfolio risk of Zero% with the probability of 90% for low risk people. In one year period, there is also the most portfolio risk of 16/92 with the probability of 99% for high risk people and the lowest portfolio risk of 0/13% with the probability of 90% for low risk people.https://jte.ut.ac.ir/article_56151_b3318c0cb1209afe813ec3d03416a78c.pdfUniversity of TehranJournal of Economic Research (Tahghighat- E- Eghtesadi)0039-896950420151222Distribution of Strongest Players' Free Riding Strategy in NetworksDistribution of Strongest Players' Free Riding Strategy in Networks9259585615310.22059/jte.2015.56153FAEbadEbadiM.Sc. , Economic, London School of Economoics, London, UKGhahremanAbdoliAssociated Professor of Economics at University of Tehran0000-0002-9398-5376Journal Article20150412Analyzing Free Riding Problem through static games considering homogenous players has been resulted in choosing the free riding strategy as a dominant strategy for the players. In this paper Free Riding is analyzed through cooperation game imposing non-homogenous players using network models. Players would be differentiated by their links with their neighbors in the game. Evolutionary Game theory especially small-world networks models are used for the analysis. The strongest player is the player with most links in the model. Studying the consequences of choosing free riding strategy by the strongest player is the foremost concept of this work. After proposing definition for strength of players, we continue to examine distribution of choosing Free Riding Strategy by the strongest player. Due to the strength of the well-linked player and the structure of network, results would be different in terms of having monomorphic or polymorphic final forms. In addition, we continue the discussion using the proposed model to analyze the decision which has been made through 166th OPEC meeting on Nov. 2014 regarding maintaining production level as was agreed in Dec 2011.Analyzing Free Riding Problem through static games considering homogenous players has been resulted in choosing the free riding strategy as a dominant strategy for the players. In this paper Free Riding is analyzed through cooperation game imposing non-homogenous players using network models. Players would be differentiated by their links with their neighbors in the game. Evolutionary Game theory especially small-world networks models are used for the analysis. The strongest player is the player with most links in the model. Studying the consequences of choosing free riding strategy by the strongest player is the foremost concept of this work. After proposing definition for strength of players, we continue to examine distribution of choosing Free Riding Strategy by the strongest player. Due to the strength of the well-linked player and the structure of network, results would be different in terms of having monomorphic or polymorphic final forms. In addition, we continue the discussion using the proposed model to analyze the decision which has been made through 166th OPEC meeting on Nov. 2014 regarding maintaining production level as was agreed in Dec 2011.https://jte.ut.ac.ir/article_56153_541ea5268c62ff788a1cf8a6d1792a66.pdfUniversity of TehranJournal of Economic Research (Tahghighat- E- Eghtesadi)0039-896950420151222Optimal Asset Allocation in the Presence of Macroeconomic Uncertainties and International sanctions against IranOptimal Asset Allocation in the Presence of Macroeconomic Uncertainties and International sanctions against Iran9599885615410.22059/jte.2015.56154FAAliakbarGholizadehAssosiate Professor of Bu-Ali Sina university0000-0002-8656-942XBehnazKamyabPhD student of Bu-Ali Sina UniversityJournal Article20141012The current study addresses an estimation of investor's optimal portfolio under conditions of uncertainty by using a combination of artificial neural network and Markowitz models. For this purpose, such assets as stock prices, house prices, coin and bonds price are used with monthly data over the period 1378-1392. Three variables including inflation uncertainty, oil uncertainty and free market dollar rate are used as state variables to investigate the impact of macroeconomic shocks on investor's decisions when choosing an optimal portfolio. Autoregressive conditional heteroskedasticity (GARCH) is used to estimate state variables. Following an estimation of the state variables, assets return and uncertainty were measured using Multi Layer Perceptron (MLP) and Radial Basis Function (RBF) neural networks. The results obtained from neural network model are used as input variables in estimation of Markowitz's optimal portfolio. The results of analyzing mean variance show that housing is the dominant asset among uncertain assets over the period of real-estate boom holding the largest share of assets. Over the recent periods, considered to be the period of housing downturn, investors no longer include housing in their portfolio turning instead to stocks and coins as prominent alternatives. Generally, bonds have shown to be an asset with no uncertainty in all the periods making them a reliable alternative in the investor's optimal portfolio.The current study addresses an estimation of investor's optimal portfolio under conditions of uncertainty by using a combination of artificial neural network and Markowitz models. For this purpose, such assets as stock prices, house prices, coin and bonds price are used with monthly data over the period 1378-1392. Three variables including inflation uncertainty, oil uncertainty and free market dollar rate are used as state variables to investigate the impact of macroeconomic shocks on investor's decisions when choosing an optimal portfolio. Autoregressive conditional heteroskedasticity (GARCH) is used to estimate state variables. Following an estimation of the state variables, assets return and uncertainty were measured using Multi Layer Perceptron (MLP) and Radial Basis Function (RBF) neural networks. The results obtained from neural network model are used as input variables in estimation of Markowitz's optimal portfolio. The results of analyzing mean variance show that housing is the dominant asset among uncertain assets over the period of real-estate boom holding the largest share of assets. Over the recent periods, considered to be the period of housing downturn, investors no longer include housing in their portfolio turning instead to stocks and coins as prominent alternatives. Generally, bonds have shown to be an asset with no uncertainty in all the periods making them a reliable alternative in the investor's optimal portfolio.https://jte.ut.ac.ir/article_56154_0c46c83cdb5378aa8665249b064867e3.pdfUniversity of TehranJournal of Economic Research (Tahghighat- E- Eghtesadi)0039-896950420151222Private returns of education in the urban araes of Iran: a quantile regression analysisPrivate returns of education in the urban araes of Iran: a quantile regression analysis98910165615510.22059/jte.2015.56155FAGholamrezaKeshavarzHaddadAssociate Professor,Faculty ofManagement and Economics,Sharif University of Technology0000-0001-5873-8217Seyyed MeysamNoorashrafoddinMA EconomicsJournal Article20140222Evidence from Iranian Households’ Expenditures and Income Survay(IHEIS) in urban area shows that, average of real hourly wage against the schooling years is declining in the right tail of wage distribution between 1384 and 1390 (2005 and 2011). A tentative justification for this observation is that the private rate of return to education at upper quantiles and higher years of schooling have reduced over the period. We estimate a Mincerian generalized wage equation by making use of quantile regressions with controlling for sample selection problem. Results indicate that the private rate of return to education has decreased between 1390 and 1384. Also educational attaionment reduces wage gap between the upper and lower deciles for females, however a reverse relation is observed for males. <br />Keywords: wage inequality, private return to education, quantile regression, sample selection bias<br />JEL Classification: J31, J23, J24, I21Evidence from Iranian Households’ Expenditures and Income Survay(IHEIS) in urban area shows that, average of real hourly wage against the schooling years is declining in the right tail of wage distribution between 1384 and 1390 (2005 and 2011). A tentative justification for this observation is that the private rate of return to education at upper quantiles and higher years of schooling have reduced over the period. We estimate a Mincerian generalized wage equation by making use of quantile regressions with controlling for sample selection problem. Results indicate that the private rate of return to education has decreased between 1390 and 1384. Also educational attaionment reduces wage gap between the upper and lower deciles for females, however a reverse relation is observed for males. <br />Keywords: wage inequality, private return to education, quantile regression, sample selection bias<br />JEL Classification: J31, J23, J24, I21https://jte.ut.ac.ir/article_56155_11a635fa86e34d683c9adba40e245fed.pdfUniversity of TehranJournal of Economic Research (Tahghighat- E- Eghtesadi)0039-896950420151222Investigating Relationship between Gross Domestic Product and Ecological Footprint as an Environmental Degradation IndexInvestigating Relationship between Gross Domestic Product and Ecological Footprint as an Environmental Degradation Index101710335615610.22059/jte.2015.56156FAMortezaMolaeiAssistant Professor, Agricultural Economics Department, Urmia UniversityEhsanBasharatM.Sc. in Agricultural Economics, Urmia UniversityJournal Article20150424The ecological footprint is a useful tool that we can use it to inform the public about the pressure on ecology and the environment. Also, by measuring it, policymakers can design and implement the necessary programs to help reduce that pressure. In order to measure the potential impact of future activities and policies of development programs on the environment, it is necessary to study the short and long term relationship between ecological footprint and economic development. The purpose of this study was to investigate short-term and long-term relationship between per capita GDP and per capita ecological footprint in Iran for the period of 1965-2011; to do this the Autoregressive Distributed Lag Model (ARDL) was used. The results show that the increase in per capita GDP has positive impact on per capita ecological footprint both in short-term and long-term. The error correction coefficient that obtained from the estimation of Error Correction Model shows that 73 percent of disequilibrium in per capita ecological footprint adjusted after each period and goes close to its long-term trend.The ecological footprint is a useful tool that we can use it to inform the public about the pressure on ecology and the environment. Also, by measuring it, policymakers can design and implement the necessary programs to help reduce that pressure. In order to measure the potential impact of future activities and policies of development programs on the environment, it is necessary to study the short and long term relationship between ecological footprint and economic development. The purpose of this study was to investigate short-term and long-term relationship between per capita GDP and per capita ecological footprint in Iran for the period of 1965-2011; to do this the Autoregressive Distributed Lag Model (ARDL) was used. The results show that the increase in per capita GDP has positive impact on per capita ecological footprint both in short-term and long-term. The error correction coefficient that obtained from the estimation of Error Correction Model shows that 73 percent of disequilibrium in per capita ecological footprint adjusted after each period and goes close to its long-term trend.https://jte.ut.ac.ir/article_56156_140e65c3274e69a7f8a871ac9e8f6d7d.pdf