bu ali sina universityJournal of Applied Economics Studies in Iran2322-253062220170723Identifying hysteresis effect in unemployment rate with emphasis on second generation panel unit root and PANIC methodIdentifying hysteresis effect in unemployment rate with emphasis on second generation panel unit root and PANIC method131187610.22084/aes.2017.11136.2209FARezaAkhbariAllameh tabataba'i university0000-0002-7457-6665HasanTaeeAllameh tabataba'i universityJournal Article20160722In the last decade, Iran is one of the numerous countries with persistent and high unemployment rate. Examining of unemployment trend in Iran shows that over the recent years, this variable has been constantly at a high level, without any tendency to converge to a certain amount. This issue justifies the necessity of examining the existence of hysteresis effect hypothesis in unemployment rate of Iranian economy. In fact, the term of hysteresis effect means dependency to the past. Therefore, hysteresis effect in unemployment rate implies the dependency between current and past unemployment rates. In this situation, all the shocks will have a permanent effect on the path of unemployment and the economy will never achieve long-term equilibrium because the equilibrium is changing constantly. Due to above, hysteresis effect hypothesis is counterpoint of natural rate of unemployment. <br />The reaction of the labor market to the shocks will be established the existence of natural rate of unemployment hypothesis (In the absence of unit root) or the alternative (if there is a unit root process), hysteresis effect hypothesis. When natural rate of unemployment hypothesis is established, after a direct (or indirect) shock to the labor market, unemployment rate will converge to the long-term value which is calld natural rate of unemployment without any kind of intervention while if there is hysteresis effect, the convergenc of unemployment rate to the long-term value after a shock will not take place. In this study the existing of hysteresis effect in unemployment rate of Iranian economy is investigated with using first and second generations of panel unit root tests with emphasis on second one in period 2005Q1-2015Q3. <br />In addition to the evolution of unit root tests in terms of the heterogeneity problem which ocurres in time series estimations, a second evolution has also heppend recently that takes the existence of cross-sectional dependence into account. One of the methods which categorize in the second generation of unit root tests is PANIC approach. We employ the PANIC procedurs of Bai and Ng (2004), which allows us to decompose the observed unemployment rate series into common factor and idiosyncratic components. This enables us to identify the source behind the hysteretic behavior which may be found. Using this method decreases the possible doubts on the results of the previous study in two ways. First, with increasing the sample size due to using of panel data instead of time series data. Second, with applying the new method which is used frequently due to more accurate output. <br />While the results of first generation tests show that the unemployment rate is stationary, the second generation refers to the non-stationary process and prove the existence of hysteresis effect in unemployment rate. Due to the higher efficiency of second generation when the cross-section dependency in the panel is identified, we rely on this method and then hysteresis effect hypothesis is accepted which is in accordance with the evidence of labor market. The existence of hysteresis effect suggests long-run policies rather than short-run solutions to stabilize the labor market. Furthermore, proving that there is a hysteresis effect in unemployment rate, it is suggested that the sources of this effect be analyzed in future studies. It should be noted that in this study four stochastic trend that led to nonstationarity and hysteresis effect were identified. Future studies can introduce these resources by studying precisely the methods for identifying the sources which generate hysteresis effect. In the last decade, Iran is one of the numerous countries with persistent and high unemployment rate. Examining of unemployment trend in Iran shows that over the recent years, this variable has been constantly at a high level, without any tendency to converge to a certain amount. This issue justifies the necessity of examining the existence of hysteresis effect hypothesis in unemployment rate of Iranian economy. In fact, the term of hysteresis effect means dependency to the past. Therefore, hysteresis effect in unemployment rate implies the dependency between current and past unemployment rates. In this situation, all the shocks will have a permanent effect on the path of unemployment and the economy will never achieve long-term equilibrium because the equilibrium is changing constantly. Due to above, hysteresis effect hypothesis is counterpoint of natural rate of unemployment. <br />The reaction of the labor market to the shocks will be established the existence of natural rate of unemployment hypothesis (In the absence of unit root) or the alternative (if there is a unit root process), hysteresis effect hypothesis. When natural rate of unemployment hypothesis is established, after a direct (or indirect) shock to the labor market, unemployment rate will converge to the long-term value which is calld natural rate of unemployment without any kind of intervention while if there is hysteresis effect, the convergenc of unemployment rate to the long-term value after a shock will not take place. In this study the existing of hysteresis effect in unemployment rate of Iranian economy is investigated with using first and second generations of panel unit root tests with emphasis on second one in period 2005Q1-2015Q3. <br />In addition to the evolution of unit root tests in terms of the heterogeneity problem which ocurres in time series estimations, a second evolution has also heppend recently that takes the existence of cross-sectional dependence into account. One of the methods which categorize in the second generation of unit root tests is PANIC approach. We employ the PANIC procedurs of Bai and Ng (2004), which allows us to decompose the observed unemployment rate series into common factor and idiosyncratic components. This enables us to identify the source behind the hysteretic behavior which may be found. Using this method decreases the possible doubts on the results of the previous study in two ways. First, with increasing the sample size due to using of panel data instead of time series data. Second, with applying the new method which is used frequently due to more accurate output. <br />While the results of first generation tests show that the unemployment rate is stationary, the second generation refers to the non-stationary process and prove the existence of hysteresis effect in unemployment rate. Due to the higher efficiency of second generation when the cross-section dependency in the panel is identified, we rely on this method and then hysteresis effect hypothesis is accepted which is in accordance with the evidence of labor market. The existence of hysteresis effect suggests long-run policies rather than short-run solutions to stabilize the labor market. Furthermore, proving that there is a hysteresis effect in unemployment rate, it is suggested that the sources of this effect be analyzed in future studies. It should be noted that in this study four stochastic trend that led to nonstationarity and hysteresis effect were identified. Future studies can introduce these resources by studying precisely the methods for identifying the sources which generate hysteresis effect. https://aes.basu.ac.ir/article_1876_f8faf925e68b412baf40d9a7d3d18e16.pdfbu ali sina universityJournal of Applied Economics Studies in Iran2322-253062220170723Measurment of Statistical Accuracy between Commodity Balance (CB) and CHARM Methods in the Estimation of Regional Input-Output Tables (RIOTs); The Case Study of Hormozgan ProvinceMeasurment of Statistical Accuracy between Commodity Balance (CB) and CHARM Methods in the Estimation of Regional Input-Output Tables (RIOTs); The Case Study of Hormozgan Province3358187910.22084/aes.2017.12904.2391FAZahraAbdolmohammadistudentAliasgharBanouee0009-0002-1591-2531ParisaMohajeriJournal Article20170202In developing countries, planning is accepted by economic experts as well as by decision makers as one of the essential tools to promote growth and development both at national and regional levels. Because the Regional Input-Output Tables (RIOTs) can be provided the great sectoral detail from the economy of each region, they are most important for regional planning as well as for regional analysis. But the application of input-output analysis for analytical purposes in regional economic levels is often get into trouble by the fact that most statistical centers provide input-output tables only at national level and the task of constructing a RIOT is therefore left to the individual researcher. The construction of the survey-based RIOTs can be complex, expensive and time consuming. Since the 1950s regional analysts have introduced non-survey based methods like Location Quotients (LQ, such as , , , , , , and ), Commodity Balances (CB) and Cross Hauling Adjusted Regionalization Method (CHARM) to estimate Regional Input-Output Coefficients (RIOCs) and RIOTs. One of the most important shortcomings of LQ and CB methods which remained unsolved for more than six decades is underestimation of regional imports and exports on account of ruling out of Cross-Hauling which leads to the overestimation supply multipliers. Cross-hauling is the export and import of the same commodity and in Location Quotients and Commodity Balances it is assumed not to exist. In 2009, the CB method has modified which is known as the CHARM method, can solve the problem of underestimation. By identifing the heterogeneity of commodities as the main cause of cross-hauling, Kronenberg try to modify CB method. CHARM is a pure nonsurvey method and related to the traditional commodity balance approach, but, it requires fewer restrictive assumtions and has a better theoretical foundation. This method is suitable for environmental and other applications where the focus is on the overall supply of goods, regardless of their source. <br />This paper followes two main objectives: One the application of CB and CHARM methods and the measurement of their statistical errors. Second is the decomposition of the derived statistical errors into scale error, technology error and the heterogeneity error. The quantification of these objectives is analysed with the two folloing questions: are the supply multipliers derived the CHARM method out perform the supply multipliers better than from the CB method? And which one of the three decomposed errores (scale error, technology error and the heterogeneity error) has more share in the total statistical errors? For the applications of the CHARM and CB methds we have used the following data. First- on the basis of the survey-based National Input-Output Table (NIOT) of 1380, that we have updated it for the year 1382 respectively. Second- the regional sectoral intermediate inputs and sectoral outputs of Hormozgan provience are directly taken from the regional accounts for the year 1382. Third- we used the survey-based RIOT of Hormozgan provience for the year 1382. In order to make the applications of two methods, we have aggregated all the data into seven secters: Agriculture, mining, agro-based industries, other industries, water, electricity and gas, construction and services. In order to evaluate the stimated RIOTs derived from the two methods whit the corresponding true figures, we have used five conventional statistical methods: MAD, Theil, STPE and WAD. <br />The research findings for Hormozgan Province with respect to supply multiplier matrix show that the degree of accuracy of the supply multiplier matrix of the CHARM method in all the five statistical methods are much closer to official figures than the CB method. In the other words the statistical error of CHARM method is much less than CB method in supply multipliers. In addition to, between three sources of error in CHARM’s estimates, heterogeneity errors have the highest share of statistical errors in Hormozgan Province.In developing countries, planning is accepted by economic experts as well as by decision makers as one of the essential tools to promote growth and development both at national and regional levels. Because the Regional Input-Output Tables (RIOTs) can be provided the great sectoral detail from the economy of each region, they are most important for regional planning as well as for regional analysis. But the application of input-output analysis for analytical purposes in regional economic levels is often get into trouble by the fact that most statistical centers provide input-output tables only at national level and the task of constructing a RIOT is therefore left to the individual researcher. The construction of the survey-based RIOTs can be complex, expensive and time consuming. Since the 1950s regional analysts have introduced non-survey based methods like Location Quotients (LQ, such as , , , , , , and ), Commodity Balances (CB) and Cross Hauling Adjusted Regionalization Method (CHARM) to estimate Regional Input-Output Coefficients (RIOCs) and RIOTs. One of the most important shortcomings of LQ and CB methods which remained unsolved for more than six decades is underestimation of regional imports and exports on account of ruling out of Cross-Hauling which leads to the overestimation supply multipliers. Cross-hauling is the export and import of the same commodity and in Location Quotients and Commodity Balances it is assumed not to exist. In 2009, the CB method has modified which is known as the CHARM method, can solve the problem of underestimation. By identifing the heterogeneity of commodities as the main cause of cross-hauling, Kronenberg try to modify CB method. CHARM is a pure nonsurvey method and related to the traditional commodity balance approach, but, it requires fewer restrictive assumtions and has a better theoretical foundation. This method is suitable for environmental and other applications where the focus is on the overall supply of goods, regardless of their source. <br />This paper followes two main objectives: One the application of CB and CHARM methods and the measurement of their statistical errors. Second is the decomposition of the derived statistical errors into scale error, technology error and the heterogeneity error. The quantification of these objectives is analysed with the two folloing questions: are the supply multipliers derived the CHARM method out perform the supply multipliers better than from the CB method? And which one of the three decomposed errores (scale error, technology error and the heterogeneity error) has more share in the total statistical errors? For the applications of the CHARM and CB methds we have used the following data. First- on the basis of the survey-based National Input-Output Table (NIOT) of 1380, that we have updated it for the year 1382 respectively. Second- the regional sectoral intermediate inputs and sectoral outputs of Hormozgan provience are directly taken from the regional accounts for the year 1382. Third- we used the survey-based RIOT of Hormozgan provience for the year 1382. In order to make the applications of two methods, we have aggregated all the data into seven secters: Agriculture, mining, agro-based industries, other industries, water, electricity and gas, construction and services. In order to evaluate the stimated RIOTs derived from the two methods whit the corresponding true figures, we have used five conventional statistical methods: MAD, Theil, STPE and WAD. <br />The research findings for Hormozgan Province with respect to supply multiplier matrix show that the degree of accuracy of the supply multiplier matrix of the CHARM method in all the five statistical methods are much closer to official figures than the CB method. In the other words the statistical error of CHARM method is much less than CB method in supply multipliers. In addition to, between three sources of error in CHARM’s estimates, heterogeneity errors have the highest share of statistical errors in Hormozgan Province.https://aes.basu.ac.ir/article_1879_3d93d2691222fb8c6248eb755335c919.pdfbu ali sina universityJournal of Applied Economics Studies in Iran2322-253062220170723Effect of changes in age structure of the population on government tax revenues and predicting its changes: An approach of Mixed Frequency Data Sampling (MIDAS)Effect of changes in age structure of the population on government tax revenues and predicting its changes: An approach of Mixed Frequency Data Sampling (MIDAS)5975188010.22084/aes.2017.12358.2332FAMohammadNoferstiassociateSaharDashtbanJournal Article20161208The aim of this thesis is to investigate the effect of changes in population age structure on government tax revenues and forecast its evolution using MIDAS method and time series data during the years 1367 until 1393.Changes in population age structure caused by the sharp rise in fertility in early 1360, has brought many consequences and questions. One of the questions is how changes in population age structure will affect the government's tax revenues. This paper tries to answer this question. For this purpose, by the theoretical foundations of the economy, we will specify a function for government tax revenues, where changes in population age structure is one of the explanatory variables. In this paper, by using of the method described by Ghysels, Santa-Clara and Valkanov in 2004, we estimated this function and anticipated government tax revenues.This study is done by using MIDAS method in order to estimate the specified for government tax revenues by the aid of R software.<strong> </strong>MIDAS method allows variables with different frequencis, i.e., seasonal, monthly or weekly, put together this in one equation and it is possible to revise the forecasted value for the dependent low frequency variable as soon as new high frequency data are released. Hence the publiction of seasonal data for the variables considered, sach as government total revenue and gdp at the beginning of the year, will make it possible to forecast the government tax revenues. This forecast will help the policy makers to see is the budget will face some unbalances, relevant policy action be token from just the beginning of the year. <br />The statistical data used in this study are time series, seasonal, which is used to collect them from the database of time series of the central bank, economic indicators and the statistical center. Variables used: Government tax revenues in the form an annual, gross domestic product and total imports in the form season, age structure of the population in the form an annual. <br />Before estimating the coefficients of the model, the reliability of the variables has been investigated .The results show that in the equation specified, the effect of seasonal GDP and total imports, annual age structure (the ratio of population aged 35 to 64 to the total population) on government tax revenues are statistically meaningful. Given the positive impact of the age structure of the population aged 35-64 to the total population, it can be said that, according to Ando Modigliani's theory, since this age group has higher income, they pay more taxes and therefore have a positive impact on government tax revenues. <br /> To estimate this function, we used the relevant data in the period the first quarter of 1367 to the fourth quarter of 1392. Next, we forecast government tax revenues for 1393. To assess the predictive power of the model in outside of the estimation range, we did not used the data of 1393 in initial expressed relations estimation. The government's tax predicted revenues forecasted by the model is 709,365.7 b.Rials and compered to its real data which is 709,651.9 b.Rials, indicate that the model forecast is satisfactory. As can be seen, entering the data the fourth chapter of the seasonal variables used in the relationship, the prediction value is very close to the real value. Also, the coefficient of determination of the pattern is estimated at 0/9954, which indicates the high explanatory power of the model. The quantity of the test statistic is 0.77, which indicates that adverbs applied are statistically significant and sufficiently adequate. Regarding the quantity of the camera-Watson test statistic and Shapiro-Wilk's normal test, the disturbance Sentences of the pattern, are not correlated and have normal distribution.The aim of this thesis is to investigate the effect of changes in population age structure on government tax revenues and forecast its evolution using MIDAS method and time series data during the years 1367 until 1393.Changes in population age structure caused by the sharp rise in fertility in early 1360, has brought many consequences and questions. One of the questions is how changes in population age structure will affect the government's tax revenues. This paper tries to answer this question. For this purpose, by the theoretical foundations of the economy, we will specify a function for government tax revenues, where changes in population age structure is one of the explanatory variables. In this paper, by using of the method described by Ghysels, Santa-Clara and Valkanov in 2004, we estimated this function and anticipated government tax revenues.This study is done by using MIDAS method in order to estimate the specified for government tax revenues by the aid of R software.<strong> </strong>MIDAS method allows variables with different frequencis, i.e., seasonal, monthly or weekly, put together this in one equation and it is possible to revise the forecasted value for the dependent low frequency variable as soon as new high frequency data are released. Hence the publiction of seasonal data for the variables considered, sach as government total revenue and gdp at the beginning of the year, will make it possible to forecast the government tax revenues. This forecast will help the policy makers to see is the budget will face some unbalances, relevant policy action be token from just the beginning of the year. <br />The statistical data used in this study are time series, seasonal, which is used to collect them from the database of time series of the central bank, economic indicators and the statistical center. Variables used: Government tax revenues in the form an annual, gross domestic product and total imports in the form season, age structure of the population in the form an annual. <br />Before estimating the coefficients of the model, the reliability of the variables has been investigated .The results show that in the equation specified, the effect of seasonal GDP and total imports, annual age structure (the ratio of population aged 35 to 64 to the total population) on government tax revenues are statistically meaningful. Given the positive impact of the age structure of the population aged 35-64 to the total population, it can be said that, according to Ando Modigliani's theory, since this age group has higher income, they pay more taxes and therefore have a positive impact on government tax revenues. <br /> To estimate this function, we used the relevant data in the period the first quarter of 1367 to the fourth quarter of 1392. Next, we forecast government tax revenues for 1393. To assess the predictive power of the model in outside of the estimation range, we did not used the data of 1393 in initial expressed relations estimation. The government's tax predicted revenues forecasted by the model is 709,365.7 b.Rials and compered to its real data which is 709,651.9 b.Rials, indicate that the model forecast is satisfactory. As can be seen, entering the data the fourth chapter of the seasonal variables used in the relationship, the prediction value is very close to the real value. Also, the coefficient of determination of the pattern is estimated at 0/9954, which indicates the high explanatory power of the model. The quantity of the test statistic is 0.77, which indicates that adverbs applied are statistically significant and sufficiently adequate. Regarding the quantity of the camera-Watson test statistic and Shapiro-Wilk's normal test, the disturbance Sentences of the pattern, are not correlated and have normal distribution.https://aes.basu.ac.ir/article_1880_8deb9bdda94228f2b412175e51494db2.pdfbu ali sina universityJournal of Applied Economics Studies in Iran2322-253062220170723Study the Impact of Fiscal Policy as a Transmission Mechanism of Oil Shocks on Iranian Economy Using a Structural Vector Autoregressive ModelStudy the Impact of Fiscal Policy as a Transmission Mechanism of Oil Shocks on Iranian Economy Using a Structural Vector Autoregressive Model7798188110.22084/aes.2017.12127.2312FASepidehTavakoliMahmoodHoshmandMostafaSalimifarEbrahimGhorjiJournal Article20161112In countries like Iran, where the government relies heavily on oil revenue, the response of fiscal policy to oil revenue fluctuations is a key transmission mechanism of oil revenue volatility. Several studies have documented various transmission channels through which oil price shocks affect economic activities in developed oil importing countries. But, regarding oil exporting countries, there are few such studies. According to them, government expenditure has the effects of diminishing returns; and, over-expanding by crowding out of private investment will decrease economic growth.Moreover, inefficiency, distortion in allocation of resources and corruption are other channels that have negative effects on output. <br />The aim of this study is to assess the mechanism in which oil shocks affect economic variables of an oil exporting country. For this purpose, we provide evidence on Iran, an oil exporter where a positive oil revenue shock generates an expansion in consumption and investment by both the private and public sectors. we document the effects of an unexpected increase in the oil revenue, on several variables which are GDP, government consumption of goods and services, government investment, private consumption, and private investment. <br />The effects of structural shocks on that variables have been assessed using a Structural Vector Auto Regressive (SVAR) Model in two different situations with dependence and independence of government budget to oil revenue. Due to data availability, however, we study a more recent period, from 19959 to 2015. We assumed the oil revenue is exogenous, so that it is only affected by its lagged values and a shock and the logarithms of the variables are detrended by Hodrick–Prescott Filter. <br /> As a first step of the empirical analysis, we carried out unit root tests for all of the variables and the lag length criteria were employed to select optimal lag order of a VAR model. To analyze the relationships between variables with the SVAR model, we estimated matrix B, the coefficients of structural shocks were recovered and their impacts on the system were investigated through impulse responses. To derive the set of identifications, use can be made of the economic theory which imposes a set of over-identifying restrictions on the coefficients of matrix B. Considering the existence of five endogenous variables, x = {GDP , G<sup>C</sup> , G<sup>I</sup> , C , I}and one exogenous variable, OR, we have six equations. By restrictions on matrix B, we may have an over-identified structural VAR. In this paper we have two different matrix B with different restrictions to show the dependence and independence of government budget to oil revenue. <br />The resulting structural parameter estimates of matrixes B are given and comparing them indicate that a positive oil revenue shock generates an expansion in consumption and investment by both the private and public sectors. Government investment expands about 2 percent above trend a few quarters after a 4.5 percent oil revenue shock hits the economy. Finally, we find that when fiscal policy is assumed to be unrelated with oil revenue the response of economic variables to oil shocks is smoothed. We find that fiscal policy is the main propagation mechanism that transmits the oil shocks to the economy. <br />The results of the paper carry important implications for the formulation of fiscal policy in oil-exporting countries like Iran. These countries could prevent large swings in economic activity by saving oil windfalls and investing them gradually. Therefore, controlling government expenditure as fiscal policy instrument effectively insulating the economy from the volatility of oil revenue.In countries like Iran, where the government relies heavily on oil revenue, the response of fiscal policy to oil revenue fluctuations is a key transmission mechanism of oil revenue volatility. Several studies have documented various transmission channels through which oil price shocks affect economic activities in developed oil importing countries. But, regarding oil exporting countries, there are few such studies. According to them, government expenditure has the effects of diminishing returns; and, over-expanding by crowding out of private investment will decrease economic growth.Moreover, inefficiency, distortion in allocation of resources and corruption are other channels that have negative effects on output. <br />The aim of this study is to assess the mechanism in which oil shocks affect economic variables of an oil exporting country. For this purpose, we provide evidence on Iran, an oil exporter where a positive oil revenue shock generates an expansion in consumption and investment by both the private and public sectors. we document the effects of an unexpected increase in the oil revenue, on several variables which are GDP, government consumption of goods and services, government investment, private consumption, and private investment. <br />The effects of structural shocks on that variables have been assessed using a Structural Vector Auto Regressive (SVAR) Model in two different situations with dependence and independence of government budget to oil revenue. Due to data availability, however, we study a more recent period, from 19959 to 2015. We assumed the oil revenue is exogenous, so that it is only affected by its lagged values and a shock and the logarithms of the variables are detrended by Hodrick–Prescott Filter. <br /> As a first step of the empirical analysis, we carried out unit root tests for all of the variables and the lag length criteria were employed to select optimal lag order of a VAR model. To analyze the relationships between variables with the SVAR model, we estimated matrix B, the coefficients of structural shocks were recovered and their impacts on the system were investigated through impulse responses. To derive the set of identifications, use can be made of the economic theory which imposes a set of over-identifying restrictions on the coefficients of matrix B. Considering the existence of five endogenous variables, x = {GDP , G<sup>C</sup> , G<sup>I</sup> , C , I}and one exogenous variable, OR, we have six equations. By restrictions on matrix B, we may have an over-identified structural VAR. In this paper we have two different matrix B with different restrictions to show the dependence and independence of government budget to oil revenue. <br />The resulting structural parameter estimates of matrixes B are given and comparing them indicate that a positive oil revenue shock generates an expansion in consumption and investment by both the private and public sectors. Government investment expands about 2 percent above trend a few quarters after a 4.5 percent oil revenue shock hits the economy. Finally, we find that when fiscal policy is assumed to be unrelated with oil revenue the response of economic variables to oil shocks is smoothed. We find that fiscal policy is the main propagation mechanism that transmits the oil shocks to the economy. <br />The results of the paper carry important implications for the formulation of fiscal policy in oil-exporting countries like Iran. These countries could prevent large swings in economic activity by saving oil windfalls and investing them gradually. Therefore, controlling government expenditure as fiscal policy instrument effectively insulating the economy from the volatility of oil revenue.https://aes.basu.ac.ir/article_1881_437ed5abc4cbb8a819b760b4428cd801.pdfbu ali sina universityJournal of Applied Economics Studies in Iran2322-253062220170723Take a look at inflation in Iran: Varx approachTake a look at inflation in Iran: Varx approach99121188210.22084/aes.2017.1882FASeyed AzizArmanMojtabaGhorbannezhadVahidKafiliJournal Article20160113High inflation, along with challenges such as low economic growth (compared to other countries and compared to target values in the fifth development plan), demographic shocks and high unemployment rates, especially in youth, monetary and fiscal discrepancies, exchange rate fluctuations, lack of planning Correct, policy making, government economy, unhealthy competition, widespread political rents, sanctions, budget dependency on oil, mismanagement and weakness of institutions, privatization, implementation of the second phase of targeting subsidies and lack of political stability, One of the most important challenges facing the Iranian economy. In macroeconomic variables, inflation seems to be the most important and chronic challenge facing the Iranian economy over the last few decades. Due to the destructive effects of inflation on the economy, its control as one of the goals of macroeconomic policies has always been a matter for economists. The persistent and chronic inflation of these days of the Iranian economy is a phenomenon whose causes have been rooted in many years. <br />This article seeks to study the factors influencing inflation as the main challenge of the present Iranian economy, based on theoretical literature of macroeconomics in the field of inflation and the revealed facts of the Iranian economy. For this purpose, considering that at least two variables of oil exports and institutional variables are as external variables of the model, so, in this study, the specific patterns of VARX have been used. The VARX has this feature, In addition to the exogenous consideration of a variable in VAR, enables us to examine the effect of this variable's impact on other variables of the pattern using the shake reaction function. In the end, using the estimated macroeconomic model, we will predict inflation under different scenarios for the money base growth rate. According to the review period (1352-1944), forecasts for the three years of 2012-2013 will be based on two scenarios (the growth scenario of 30% of the monetary base and the growth scenario of 10% of the monetary base). <br />The results showed that monetary base variables, exchange rate and oil exports have a positive effect on inflation. Also, the variable of economic freedom is negative, which indicates a negative relationship between prices and economic freedom. The results of the impulse response functions also showed that due to a monetary shock, exchange rate and oil exports, the level of prices would increase, but with a decreasing rate. The results of the Bootstrap method show that the above-mentioned shocks increase the level of prices and remain at the same level. Also, for the shock caused by two variables of production and economic freedom, it is also seen that the level of prices has fallen. Using estimated macroeconomic model, we predicted inflation under two different scenarios for liquidity. In the first scenario (the current situation), oil exports are stable at the same level in 1394, the economic freedom index is fixed at the same level in 1394 and the annual growth money base is 30%. In the second scenario, the same assumptions exist, except that the base money growth is 10% annually. According to the reviewed period (1352-1394), the predictions were made for three years, 1395-1397. The results show that the difference in inflation under these two scenarios will be 3.6, 7.4 and 9 percent, respectively, during the years 2012-2013. That is, controlling the growth of the monetary base can control inflation in part. Of course, this should not be forgotten that severe monetary contraction policies could affect economic growth.High inflation, along with challenges such as low economic growth (compared to other countries and compared to target values in the fifth development plan), demographic shocks and high unemployment rates, especially in youth, monetary and fiscal discrepancies, exchange rate fluctuations, lack of planning Correct, policy making, government economy, unhealthy competition, widespread political rents, sanctions, budget dependency on oil, mismanagement and weakness of institutions, privatization, implementation of the second phase of targeting subsidies and lack of political stability, One of the most important challenges facing the Iranian economy. In macroeconomic variables, inflation seems to be the most important and chronic challenge facing the Iranian economy over the last few decades. Due to the destructive effects of inflation on the economy, its control as one of the goals of macroeconomic policies has always been a matter for economists. The persistent and chronic inflation of these days of the Iranian economy is a phenomenon whose causes have been rooted in many years. <br />This article seeks to study the factors influencing inflation as the main challenge of the present Iranian economy, based on theoretical literature of macroeconomics in the field of inflation and the revealed facts of the Iranian economy. For this purpose, considering that at least two variables of oil exports and institutional variables are as external variables of the model, so, in this study, the specific patterns of VARX have been used. The VARX has this feature, In addition to the exogenous consideration of a variable in VAR, enables us to examine the effect of this variable's impact on other variables of the pattern using the shake reaction function. In the end, using the estimated macroeconomic model, we will predict inflation under different scenarios for the money base growth rate. According to the review period (1352-1944), forecasts for the three years of 2012-2013 will be based on two scenarios (the growth scenario of 30% of the monetary base and the growth scenario of 10% of the monetary base). <br />The results showed that monetary base variables, exchange rate and oil exports have a positive effect on inflation. Also, the variable of economic freedom is negative, which indicates a negative relationship between prices and economic freedom. The results of the impulse response functions also showed that due to a monetary shock, exchange rate and oil exports, the level of prices would increase, but with a decreasing rate. The results of the Bootstrap method show that the above-mentioned shocks increase the level of prices and remain at the same level. Also, for the shock caused by two variables of production and economic freedom, it is also seen that the level of prices has fallen. Using estimated macroeconomic model, we predicted inflation under two different scenarios for liquidity. In the first scenario (the current situation), oil exports are stable at the same level in 1394, the economic freedom index is fixed at the same level in 1394 and the annual growth money base is 30%. In the second scenario, the same assumptions exist, except that the base money growth is 10% annually. According to the reviewed period (1352-1394), the predictions were made for three years, 1395-1397. The results show that the difference in inflation under these two scenarios will be 3.6, 7.4 and 9 percent, respectively, during the years 2012-2013. That is, controlling the growth of the monetary base can control inflation in part. Of course, this should not be forgotten that severe monetary contraction policies could affect economic growth.https://aes.basu.ac.ir/article_1882_2ace39476754d451282c83bff491db8e.pdfbu ali sina universityJournal of Applied Economics Studies in Iran2322-253062220170723Estimation the share of inefficient wage inequality between rural-urban in IranEstimation the share of inefficient wage inequality between rural-urban in Iran123143188410.22084/aes.2017.11959.2299FAAliFalahatiYounesGoliJournal Article20161024Wage differences between two groups can be explained by two factors, difference in human capital and occupational and activity status that observable, and another factor related to discrimination, discrimination is difference in return for equal characteristic, in fact discrimination cannot be explained by differences in characteristics such as education, experience. If the difference in wages was due to human capital difference, because each of the factors of production are paid based on the value of their marginal product. Such as wage gap is efficient and imply on optimal resource allocation. But if wage gap was due to discrimination, wage gap is inefficient. In recent years, one of the most problems in Iran migration of rural people to urban area. One of the reasons for this phenomenon is regional wage gap. The evidence of gathering data from statistical center of Iran show that wage inequality between rural-urban regional is high in 2006 and has decreased in 2014. therefore the goal of this study, the estimation of the share of inefficient wage inequality between rural-urban in Iran by using the data from Households’ Income and Expenditures surveys over 2006-2014 and applying Oaxaca-Blinder (inequality of wage at mean) and Machado-Mata (inequality of wage at entire distribution) decomposition models. <br />Theoretical framework <br />For decomposition wage inequality between rural-urban regional we use of Baker’s taste discrimination theory (1957). Becker (1957) introduces the concept of discrimination on the labor market; employers maximize the utility function and have disutility from employing the some group. Equation (1) shows that wage difference can be decompose to two part, first term on the right hand side of equation (1) indicating regional wage gap due to productivity difference and second term due to discrimination. <br /> <br /> <br /> <br /> <br /> <br />(1) <br /> <br /> <br /> <br /> <br /> <br />When the first term has dominant effect, the share of efficient wage gap is high and when the size of second term is high, possibility of inefficient allocation resource has increased. <br />Methodology <br />To increasing the concordance between goal of study and theoretical framework we use the decomposition model that suggested by Blinder-Oaxaca (1973) and Machado-Mata (2005). <br /> <br />R in equation 2 is wage gap, x is characteristics component such as experience, education, gender, job and activity statues, β is estimated coefficient of characteristics of labor. The first term on the right hand of equation 2 equal to amounts to the part that is due to differences in characteristics between urban and rural (explained part of the differential), second term is the contribution of differences in coefficients (discrimination component or unexplained part of the differential). <br />Result and discussion <br />The result of study show that total wage gap (difference the logarithm of wage), has decreased from 0.41 in 2006 to 0.28 in 2014, also the efficient wage gap has decreased from 0.299 in 2008 to 0.117 in 2014, and inefficient wage gap has decreased from 0.234 in 2006 to 0.0484 in 2012 and then increased to 0.166 in 2014. But the share of inefficient wage over 2006-2014 was about 37 percent. the result of Machado-Mata decomposition show wage gap in all centile decreased and role of decreasing discrimination in lower centile more than efficient wage gap and in upper centile the role of decreasing efficient wage gap is dominant. Also increasing the migration of rural to urban and increasing competition for finding a job in urban area lead to increasing efficient wage gap in bottom centile in 2014 than 2006. in fact the result show that high wage earner necessary doesn’t have high level of human capital.Wage differences between two groups can be explained by two factors, difference in human capital and occupational and activity status that observable, and another factor related to discrimination, discrimination is difference in return for equal characteristic, in fact discrimination cannot be explained by differences in characteristics such as education, experience. If the difference in wages was due to human capital difference, because each of the factors of production are paid based on the value of their marginal product. Such as wage gap is efficient and imply on optimal resource allocation. But if wage gap was due to discrimination, wage gap is inefficient. In recent years, one of the most problems in Iran migration of rural people to urban area. One of the reasons for this phenomenon is regional wage gap. The evidence of gathering data from statistical center of Iran show that wage inequality between rural-urban regional is high in 2006 and has decreased in 2014. therefore the goal of this study, the estimation of the share of inefficient wage inequality between rural-urban in Iran by using the data from Households’ Income and Expenditures surveys over 2006-2014 and applying Oaxaca-Blinder (inequality of wage at mean) and Machado-Mata (inequality of wage at entire distribution) decomposition models. <br />Theoretical framework <br />For decomposition wage inequality between rural-urban regional we use of Baker’s taste discrimination theory (1957). Becker (1957) introduces the concept of discrimination on the labor market; employers maximize the utility function and have disutility from employing the some group. Equation (1) shows that wage difference can be decompose to two part, first term on the right hand side of equation (1) indicating regional wage gap due to productivity difference and second term due to discrimination. <br /> <br /> <br /> <br /> <br /> <br />(1) <br /> <br /> <br /> <br /> <br /> <br />When the first term has dominant effect, the share of efficient wage gap is high and when the size of second term is high, possibility of inefficient allocation resource has increased. <br />Methodology <br />To increasing the concordance between goal of study and theoretical framework we use the decomposition model that suggested by Blinder-Oaxaca (1973) and Machado-Mata (2005). <br /> <br />R in equation 2 is wage gap, x is characteristics component such as experience, education, gender, job and activity statues, β is estimated coefficient of characteristics of labor. The first term on the right hand of equation 2 equal to amounts to the part that is due to differences in characteristics between urban and rural (explained part of the differential), second term is the contribution of differences in coefficients (discrimination component or unexplained part of the differential). <br />Result and discussion <br />The result of study show that total wage gap (difference the logarithm of wage), has decreased from 0.41 in 2006 to 0.28 in 2014, also the efficient wage gap has decreased from 0.299 in 2008 to 0.117 in 2014, and inefficient wage gap has decreased from 0.234 in 2006 to 0.0484 in 2012 and then increased to 0.166 in 2014. But the share of inefficient wage over 2006-2014 was about 37 percent. the result of Machado-Mata decomposition show wage gap in all centile decreased and role of decreasing discrimination in lower centile more than efficient wage gap and in upper centile the role of decreasing efficient wage gap is dominant. Also increasing the migration of rural to urban and increasing competition for finding a job in urban area lead to increasing efficient wage gap in bottom centile in 2014 than 2006. in fact the result show that high wage earner necessary doesn’t have high level of human capital.https://aes.basu.ac.ir/article_1884_1776e59b99f01b0a214518ab6f0ca41f.pdfbu ali sina universityJournal of Applied Economics Studies in Iran2322-253062220170723The Return to Education in IRAN by Using Age Cohorts and Pseudo Panel Data ApproachThe Return to Education in IRAN by Using Age Cohorts and Pseudo Panel Data Approach145170188510.22084/aes.2017.10981.2200FAZahraAlmei0000-0002-2913-7292TaherehNikbinManiMotameni0000-0002-4814-3276Journal Article20160704Education as an effective social phenomenon has an important role in the realization of the cultural, social, political and economic objectives.The important role of education in individual and social behavior and its effect on economic growth and development process led to a new branch in economics as economics of education. Now the question for society is how education can affect their income increasing process, and how much should be policymakers invested in education? Given the importance of education on earnings, numerous studies have been performed in estimating the returns to education. In traditional approach, Mincer's earning function were used for estimating return of education. Mincer provided an earning function as a semi Logarithmic regression that dependent variable is income and independent variables are years of education and years of experience. The coefficient of years of schooling represents the average rate of return during training. <br />Using ordinary least squares (OLS) for estimation of returns to education in Mincer's earning function is unobserved heterogeneity among individuals. As a matter of fact, the main problem discussed in the literature on the returns to education is the endogeneity of the schooling variable. Individual choice of years of schooling is not exogenous and tends to be correlated with unobservables in the error term of the earnings function. The likely candidates for these unobservables are ability or motivation, which correlate with years of education and with earnings, giving rise to “ability bias” (Card, 1999). Given the expected positive correlations between ability and both earnings and years of schooling, the standard critique emphasizes an upward bias. To avoid inbreeding bias and individual unobserved heterogeneity, it is better to use panel data, but the main limitation is the lack of longitudinal data, especially in developing countries. As a result, it can be said that in studies which have been done in the field of returns to education, only a few of them have paid attention to the problem of estimation bias. Deaton (1985) used the cohort to solve the problem and suggested pseudo panel data. Each cohort is a group with specific members. Pseudo panel date is following the people's age group in a higher level than Cross-sectional data. The age groups have been made from repeated cross-sectional data from household surveys according to a group of people with specific characteristics. <br />In this study, we employ the pseudo-panel approach for estimating returns to education in Iran. Pseudo-panel data are constructed from repeated cross sections of Household Expenditure and Income Survey (HEIS) conducted by Statistical Center of Iran during 1991to 2012. The subjects were employee who was born between the years 1937 to 1981. In this study, 22 the period and 9 age group is used to form the cohort. Each cohort has been divided in 5-year period. For example, the ninth age group are 54-50 that are born between 1937-1941. <br />On the base of results, education has a positive and significant impact on earnings. The relatively high rate of educational return shows that investment in education is economically feasible; therefore, policy makers should pay more attention to its development. Also, the men’s educational return is more than women’s. On the other hand, the relation between age/ experience and income is inverse U. This means that with age / experience of the income increases. But, after reaching the maximum point, it do not expected more age / years of experience lead to an increase in income.Education as an effective social phenomenon has an important role in the realization of the cultural, social, political and economic objectives.The important role of education in individual and social behavior and its effect on economic growth and development process led to a new branch in economics as economics of education. Now the question for society is how education can affect their income increasing process, and how much should be policymakers invested in education? Given the importance of education on earnings, numerous studies have been performed in estimating the returns to education. In traditional approach, Mincer's earning function were used for estimating return of education. Mincer provided an earning function as a semi Logarithmic regression that dependent variable is income and independent variables are years of education and years of experience. The coefficient of years of schooling represents the average rate of return during training. <br />Using ordinary least squares (OLS) for estimation of returns to education in Mincer's earning function is unobserved heterogeneity among individuals. As a matter of fact, the main problem discussed in the literature on the returns to education is the endogeneity of the schooling variable. Individual choice of years of schooling is not exogenous and tends to be correlated with unobservables in the error term of the earnings function. The likely candidates for these unobservables are ability or motivation, which correlate with years of education and with earnings, giving rise to “ability bias” (Card, 1999). Given the expected positive correlations between ability and both earnings and years of schooling, the standard critique emphasizes an upward bias. To avoid inbreeding bias and individual unobserved heterogeneity, it is better to use panel data, but the main limitation is the lack of longitudinal data, especially in developing countries. As a result, it can be said that in studies which have been done in the field of returns to education, only a few of them have paid attention to the problem of estimation bias. Deaton (1985) used the cohort to solve the problem and suggested pseudo panel data. Each cohort is a group with specific members. Pseudo panel date is following the people's age group in a higher level than Cross-sectional data. The age groups have been made from repeated cross-sectional data from household surveys according to a group of people with specific characteristics. <br />In this study, we employ the pseudo-panel approach for estimating returns to education in Iran. Pseudo-panel data are constructed from repeated cross sections of Household Expenditure and Income Survey (HEIS) conducted by Statistical Center of Iran during 1991to 2012. The subjects were employee who was born between the years 1937 to 1981. In this study, 22 the period and 9 age group is used to form the cohort. Each cohort has been divided in 5-year period. For example, the ninth age group are 54-50 that are born between 1937-1941. <br />On the base of results, education has a positive and significant impact on earnings. The relatively high rate of educational return shows that investment in education is economically feasible; therefore, policy makers should pay more attention to its development. Also, the men’s educational return is more than women’s. On the other hand, the relation between age/ experience and income is inverse U. This means that with age / experience of the income increases. But, after reaching the maximum point, it do not expected more age / years of experience lead to an increase in income.https://aes.basu.ac.ir/article_1885_e05d7ef969b57d115b63a9486a8184cc.pdfbu ali sina universityJournal of Applied Economics Studies in Iran2322-253062220170723The amount of influence fluctuations in the price index of Tehran Stock Exchange and Dubai fluctuations in oil prices (WTI)The amount of influence fluctuations in the price index of Tehran Stock Exchange and Dubai fluctuations in oil prices (WTI)171195188610.22084/aes.2017.11581.2261FAMohammad HasanFotros0000-0001-6859-5854MaryamHoshidariJournal Article20160910One of the most important markets in each economy is capital markets. Undoubtedly, the conditions of these markets are influenced by other sectors and can affect other sectors of the economy. The stock market is one of the components of the capital market, and as part of the economy, its function is that if this market does not have a logical relationship with other sectors, then problems and deficiencies will arise in its operation. In the capital market, widespread fluctuations lead to the arrival and departure of a massive amount of capital. The Stock Exchange, an officially organized market for the purchase and sale of shares of companies under certain terms and conditions. The oil industry has always had a financial role in the economy, and its main function is to provide the currency needed by the countries. The economies of countries that rely heavily on oil revenues and foreign exchange earnings, the oil price fluctuations that originate from exogenous developments and the control of outside economic policymakers, are the largest source of disruption to the economies of the countries. Oil revenues are fluctuating. Therefore, fluctuation is one of the most important issues in the financial markets of the world. Oscillation, as an effective factor in determining the risk of investment, can play an important role in the decision making of investors. An appropriate estimation of stock price fluctuations or arbitrage transactions in an investment period is a very important starting point in controlling investment risk. One of the factors influencing the stock price index is oil prices and oil price fluctuations. Oil and its products are used as the most important source of energy in world production processes. Fluctuations in oil prices can affect the cost of production and profitability of manufacturing companies. Oil is considered to be the most important source of income for some of the exporting countries, and the price of oil and its fluctuations from this channel can also affect the real sector as well as the capital market, so that in many countries that do not have a good oil revenue management the price of oil has been accompanied by an increase in government revenues and an increase in the monetary base that has an impact on inflation. Increasing inflation also has a positive impact on stock prices; the World Oil Index is one of the most important indicators affecting economic factors and political factors in each country. The global oil price, as a powerful exogenous variable, is influenced by many macroeconomic variables, including the stock price index. The explanation of such a relationship is the policy makers' guideline in monetary and foreign policy directives. Therefore, the main focus of this paper is to examine the impact of the Tehran Stock Price Index (TEPIX) and the Dubai Dollar Index (DFM) Crude oil using monthly data over the period of 2004-2016 as well as the multivariate GARCH method. To do this, we first performed the Dickey-Fuller and Phillips-Peron stacking tests to check the variance of the variables, which is based on the results of this test at the root level of the unit, so that from all three variables, once the difference was made, and therefore all three variables with a load Mana differentiation, then we performed an ARCH test, which results in the existence of an Arch effect on the model remainders, and finally we estimated the multivariate GARCH model with the BEKK approach. According to research results, global oil price fluctuations have a positive and significant effect on the fluctuations of the Dubai stock exchange index, and the global oil price volatility has a positive and significant effect on Tehran Stock Exchange index fluctuations. On the other hand, Dubai Stock Exchange fluctuations (DFM) have a positive and significant effect on Tehran Stock Exchange Index (TEPIX) fluctuations.One of the most important markets in each economy is capital markets. Undoubtedly, the conditions of these markets are influenced by other sectors and can affect other sectors of the economy. The stock market is one of the components of the capital market, and as part of the economy, its function is that if this market does not have a logical relationship with other sectors, then problems and deficiencies will arise in its operation. In the capital market, widespread fluctuations lead to the arrival and departure of a massive amount of capital. The Stock Exchange, an officially organized market for the purchase and sale of shares of companies under certain terms and conditions. The oil industry has always had a financial role in the economy, and its main function is to provide the currency needed by the countries. The economies of countries that rely heavily on oil revenues and foreign exchange earnings, the oil price fluctuations that originate from exogenous developments and the control of outside economic policymakers, are the largest source of disruption to the economies of the countries. Oil revenues are fluctuating. Therefore, fluctuation is one of the most important issues in the financial markets of the world. Oscillation, as an effective factor in determining the risk of investment, can play an important role in the decision making of investors. An appropriate estimation of stock price fluctuations or arbitrage transactions in an investment period is a very important starting point in controlling investment risk. One of the factors influencing the stock price index is oil prices and oil price fluctuations. Oil and its products are used as the most important source of energy in world production processes. Fluctuations in oil prices can affect the cost of production and profitability of manufacturing companies. Oil is considered to be the most important source of income for some of the exporting countries, and the price of oil and its fluctuations from this channel can also affect the real sector as well as the capital market, so that in many countries that do not have a good oil revenue management the price of oil has been accompanied by an increase in government revenues and an increase in the monetary base that has an impact on inflation. Increasing inflation also has a positive impact on stock prices; the World Oil Index is one of the most important indicators affecting economic factors and political factors in each country. The global oil price, as a powerful exogenous variable, is influenced by many macroeconomic variables, including the stock price index. The explanation of such a relationship is the policy makers' guideline in monetary and foreign policy directives. Therefore, the main focus of this paper is to examine the impact of the Tehran Stock Price Index (TEPIX) and the Dubai Dollar Index (DFM) Crude oil using monthly data over the period of 2004-2016 as well as the multivariate GARCH method. To do this, we first performed the Dickey-Fuller and Phillips-Peron stacking tests to check the variance of the variables, which is based on the results of this test at the root level of the unit, so that from all three variables, once the difference was made, and therefore all three variables with a load Mana differentiation, then we performed an ARCH test, which results in the existence of an Arch effect on the model remainders, and finally we estimated the multivariate GARCH model with the BEKK approach. According to research results, global oil price fluctuations have a positive and significant effect on the fluctuations of the Dubai stock exchange index, and the global oil price volatility has a positive and significant effect on Tehran Stock Exchange index fluctuations. On the other hand, Dubai Stock Exchange fluctuations (DFM) have a positive and significant effect on Tehran Stock Exchange Index (TEPIX) fluctuations.https://aes.basu.ac.ir/article_1886_eacb2c98711784b6d55f67c34d97944b.pdfbu ali sina universityJournal of Applied Economics Studies in Iran2322-253062220170723Investigation of short- and long-run impacts of economic sanctions of capital goods on GDPInvestigation of short- and long-run impacts of economic sanctions of capital goods on GDP197209188810.22084/aes.2017.9258.2035FAAliMehregan0000-0001-9565-8881HamidKordbacheJournal Article20160108Since countries need each other to achieve eligible economic growth, sanctions against Iran can slow down economic growth and prevent the country from achieving its visionary goals. Of course, the Iran’s economy may have been able to cope with these sanctions by relying on domestic capabilities and changing the main trading partners of the country and have found some ways to counteract or mitigate their effects, but it is clear that the imposition of this sanctions has a significant negative effect on the performance of the national economy that the evaluation of various aspects of it requires different studies. This study merely uses a distribute regression method to identify the likely short-term and long-term effect of capital goods sanctions on gross domestic product (GDP). What is clear is that the boycott of capital does not show its effects on production immediately and the occurrence of these effects is interrupted. The main goal of this study is on how and when the effect of “economic sanctions on capital goods” on “GDP” will occur. <br />The effect of sanctions on GDP can be explained more by the effect of these sanctions on investment. Therefore, this study will explain about the effects of sanctions on production through their effects on investment. Considering that the largest portion of the country’s investment that could have the greatest effect on sanctions is the import of capital goods, so, in this study, we examine the possible effects of the boycott of capital goods on the gross domestic product by using the data of the import of capital and GDP statistics from 1355 to 1391. The main goal is to show that, if the impost of capital goods decreases because of the economic sanctions, how long does it take for the country, or when can we see the maximum impact of the sanctions. To achieve this goal, we used a distributed regression method. On this basis, we assumed that the coefficients of interruptions are behavioral in the form of a quadratic polynomial. By this method, after estimating the model, we can predict the effects of sanctions of each of the preceding periods. Of course, the sanctions on the exports of capital goods by US and European countries to Iran have caused Iran’s attention to other countries, which could be accompanied by a decline in the quality of imported machines and an increase in import prices. <br />This study uses the regression method; because its purpose is to assess the effects of sanctions on the most important macroeconomic variables, namely, gross national products. Also, because the effects of economic sanctions appear over time and gradually, we need to use distracting models to explain these effects. The use of these models also provides an opportunity to examine the short-term, medium-term and long-term effects of economic sanctions on gross domestic products. <br />Since the import of capital goods affects on interrupted production, therefore, in order to determine the time of the effect of importing the aforementioned commodities on production, the variable has appeared with interruption in the model. More precisely, since there is a considerable time of installation and eventually their placement in the production process, it can be claimed that the effect of the change in capital goods on gross domestic product follows a similar U-inverse trend. Based on such assumption of the formation of sanctions on the country’s production, one can use a polynomial distributive breakdown model (Almon). <br />The length of the interruptions we are looking at is estimated to be 7 years based on the Akaike criterion and Schwarz criterion. In other words, the import of capital goods has an average of 7 years in gross domestic products. <br />In summary, the results show that a one percent decrease in imports of capital goods this year due to a boycott, assuming other factors are fixed, will reduce the annual GDP by 0.04753 percent this year, indicating an immediate effect of the boycott. <br />Also, a one percent decrease in importing capital goods this year will reduce the GDP by 0.08279 percent in the following year. The total effect of the first two periods will be equal to 0.1217. <br />What visible from the result and followed by the Almon method is that as the length of the interruption increases, first the incremental coefficients increases and them decreases. As it can be seen, the effect of the boycott of capital goods imports on the reduction of GDP in the third period reaches its maximum and then until the seventh year its effect continues to decline. Finally, with a sum of coefficients of independent variable with interruption, the long-term coefficient is 0.68034. So, a 1 percent decrease in the imports of capital goods due to sanctions, reduces GDP by 0.88 percent over an 8-year period. <br />The average gap is equal to 3/4, which indicates that the effect of each reduction unit on the import of capital goods on GDP is, on average, over a period of 3/4 years or 3 years and 146 days. <br />The mid-interruption indicator shows us that the period during which 50 percent of the total effect of a unit of change in the import of capital good occurs, is 3 years and 298 days. <br />The results of this model show that the decrease (increase) in the import of capital goods for any reason such as a boycott of the economy does not directly affects on GDP’s decreasing (increasing) and till many year after the boycott, the significant effects of this year’s boycott on GDP still can be seen. <br />This paper examines the whole economy as a unit, while the results for each section may vary. For instance, it is expected that the length and extent of the effect of the sanctions on capital goods on sectors such as agriculture and services, and the length and extent of the impact of sanctions on capital goods on sectors such as oil and gas, and industries and mines are more than what would have been achieved for the entire economy. However, the study of each of these sections can be an issue for independent research.Since countries need each other to achieve eligible economic growth, sanctions against Iran can slow down economic growth and prevent the country from achieving its visionary goals. Of course, the Iran’s economy may have been able to cope with these sanctions by relying on domestic capabilities and changing the main trading partners of the country and have found some ways to counteract or mitigate their effects, but it is clear that the imposition of this sanctions has a significant negative effect on the performance of the national economy that the evaluation of various aspects of it requires different studies. This study merely uses a distribute regression method to identify the likely short-term and long-term effect of capital goods sanctions on gross domestic product (GDP). What is clear is that the boycott of capital does not show its effects on production immediately and the occurrence of these effects is interrupted. The main goal of this study is on how and when the effect of “economic sanctions on capital goods” on “GDP” will occur. <br />The effect of sanctions on GDP can be explained more by the effect of these sanctions on investment. Therefore, this study will explain about the effects of sanctions on production through their effects on investment. Considering that the largest portion of the country’s investment that could have the greatest effect on sanctions is the import of capital goods, so, in this study, we examine the possible effects of the boycott of capital goods on the gross domestic product by using the data of the import of capital and GDP statistics from 1355 to 1391. The main goal is to show that, if the impost of capital goods decreases because of the economic sanctions, how long does it take for the country, or when can we see the maximum impact of the sanctions. To achieve this goal, we used a distributed regression method. On this basis, we assumed that the coefficients of interruptions are behavioral in the form of a quadratic polynomial. By this method, after estimating the model, we can predict the effects of sanctions of each of the preceding periods. Of course, the sanctions on the exports of capital goods by US and European countries to Iran have caused Iran’s attention to other countries, which could be accompanied by a decline in the quality of imported machines and an increase in import prices. <br />This study uses the regression method; because its purpose is to assess the effects of sanctions on the most important macroeconomic variables, namely, gross national products. Also, because the effects of economic sanctions appear over time and gradually, we need to use distracting models to explain these effects. The use of these models also provides an opportunity to examine the short-term, medium-term and long-term effects of economic sanctions on gross domestic products. <br />Since the import of capital goods affects on interrupted production, therefore, in order to determine the time of the effect of importing the aforementioned commodities on production, the variable has appeared with interruption in the model. More precisely, since there is a considerable time of installation and eventually their placement in the production process, it can be claimed that the effect of the change in capital goods on gross domestic product follows a similar U-inverse trend. Based on such assumption of the formation of sanctions on the country’s production, one can use a polynomial distributive breakdown model (Almon). <br />The length of the interruptions we are looking at is estimated to be 7 years based on the Akaike criterion and Schwarz criterion. In other words, the import of capital goods has an average of 7 years in gross domestic products. <br />In summary, the results show that a one percent decrease in imports of capital goods this year due to a boycott, assuming other factors are fixed, will reduce the annual GDP by 0.04753 percent this year, indicating an immediate effect of the boycott. <br />Also, a one percent decrease in importing capital goods this year will reduce the GDP by 0.08279 percent in the following year. The total effect of the first two periods will be equal to 0.1217. <br />What visible from the result and followed by the Almon method is that as the length of the interruption increases, first the incremental coefficients increases and them decreases. As it can be seen, the effect of the boycott of capital goods imports on the reduction of GDP in the third period reaches its maximum and then until the seventh year its effect continues to decline. Finally, with a sum of coefficients of independent variable with interruption, the long-term coefficient is 0.68034. So, a 1 percent decrease in the imports of capital goods due to sanctions, reduces GDP by 0.88 percent over an 8-year period. <br />The average gap is equal to 3/4, which indicates that the effect of each reduction unit on the import of capital goods on GDP is, on average, over a period of 3/4 years or 3 years and 146 days. <br />The mid-interruption indicator shows us that the period during which 50 percent of the total effect of a unit of change in the import of capital good occurs, is 3 years and 298 days. <br />The results of this model show that the decrease (increase) in the import of capital goods for any reason such as a boycott of the economy does not directly affects on GDP’s decreasing (increasing) and till many year after the boycott, the significant effects of this year’s boycott on GDP still can be seen. <br />This paper examines the whole economy as a unit, while the results for each section may vary. For instance, it is expected that the length and extent of the effect of the sanctions on capital goods on sectors such as agriculture and services, and the length and extent of the impact of sanctions on capital goods on sectors such as oil and gas, and industries and mines are more than what would have been achieved for the entire economy. However, the study of each of these sections can be an issue for independent research.https://aes.basu.ac.ir/article_1888_71efcd175da2800b14886a123b98a145.pdfbu ali sina universityJournal of Applied Economics Studies in Iran2322-253062220170723Effect of Houshold Head’s Income & Education on housing tenure choice in Iran’s urban areasEffect of Houshold Head’s Income & Education on housing tenure choice in Iran’s urban areas211230189110.22084/aes.2017.1891FAAli AkbarGholizadehassociateMotaharehKhaksarJournal Article20150311Housing is one of the most essential needs of a household, which it can have an important role in dimensions of macroeconomics and household, being provided in both rented and ownership types. Several factors can have effect on households choosing to buy or rent,identification of these factors and amount of effect can help to make policy and housing planning. Logit and probit models are used for this purpose and the results obtained from the estimation model are compared to determine the best model. Used data are cost and income data of urban households 93. two criteria AIC and BIC have been used to determine the best model among the two predicted models of Logit and Probit. Based on these two criteria, a model with the highest likelihood and minimum BIC and AIC is selected as the optimal model. Accordingly, by comparing two models of Logit and Probit, the logit model has the maximum likelihood and minimum BIC and AIC and is selected as the best model. for choosing the best and most significant model, we use the LR statistic between the permanent income logit models and the current income logit model and assume that the model with the highest LR statistic has less bias and the best model, but in these two models (model Permanent income logit and current income logit model) Because of the equality of degrees of freedom of both models (degree of freedom 8), there is no possibility of carrying out the LR test and LR CHI ^ 2 is used to determine the best model. By comparing the value of the LR CHI ^ 2 statistic in two permanent income logit models and the current income logit model, the permanent income logit model has a higher LR CHI-2 statistic and the model has a higher degree of certainty. Following the two permanent income logit models and the current income logit model, the logit model is selected with a permanent income and the interpretation of the model results is based on the logit model with the permanent income and the effective factors on housing tenure choice are defined: permanent and current income, age, gender and education, occupied household head and size, Based on the permanent logit model, all independent variables of the model have a positive effect on the probability of ownership of the housing and all the estimated coefficients in the model, except for the household status quotient ratio, are significant but the focus of this study is on households' income and education and The results show that income and education of the head of family has positive significant impact on housing tenure choice. The results of the marginal impact assessment show that the increase of 10 million rials in the household's permanent income increases the probability of ownership of housing by 76.1% in this research. due to the use of cross-sectional data, the explanation of the type of housing tenure has been considered. Because of the lack of policy variables in household income-expenditure data, the possibility of providing policy recommendations is limited. Nevertheless, the results can provide the basis for providing policy recommendations that may be directly or indirectly related to the results of the research. The results show that the most important and effective factor influencing the increase in the share of civil housing is permanent income; in other words, one of the main tenants of low-income groups and special groups is that they do not have permanent occupation and income, and therefore policies affecting the permanence of this job At the same time, the groups will have a decisive impact on their housework.Housing is one of the most essential needs of a household, which it can have an important role in dimensions of macroeconomics and household, being provided in both rented and ownership types. Several factors can have effect on households choosing to buy or rent,identification of these factors and amount of effect can help to make policy and housing planning. Logit and probit models are used for this purpose and the results obtained from the estimation model are compared to determine the best model. Used data are cost and income data of urban households 93. two criteria AIC and BIC have been used to determine the best model among the two predicted models of Logit and Probit. Based on these two criteria, a model with the highest likelihood and minimum BIC and AIC is selected as the optimal model. Accordingly, by comparing two models of Logit and Probit, the logit model has the maximum likelihood and minimum BIC and AIC and is selected as the best model. for choosing the best and most significant model, we use the LR statistic between the permanent income logit models and the current income logit model and assume that the model with the highest LR statistic has less bias and the best model, but in these two models (model Permanent income logit and current income logit model) Because of the equality of degrees of freedom of both models (degree of freedom 8), there is no possibility of carrying out the LR test and LR CHI ^ 2 is used to determine the best model. By comparing the value of the LR CHI ^ 2 statistic in two permanent income logit models and the current income logit model, the permanent income logit model has a higher LR CHI-2 statistic and the model has a higher degree of certainty. Following the two permanent income logit models and the current income logit model, the logit model is selected with a permanent income and the interpretation of the model results is based on the logit model with the permanent income and the effective factors on housing tenure choice are defined: permanent and current income, age, gender and education, occupied household head and size, Based on the permanent logit model, all independent variables of the model have a positive effect on the probability of ownership of the housing and all the estimated coefficients in the model, except for the household status quotient ratio, are significant but the focus of this study is on households' income and education and The results show that income and education of the head of family has positive significant impact on housing tenure choice. The results of the marginal impact assessment show that the increase of 10 million rials in the household's permanent income increases the probability of ownership of housing by 76.1% in this research. due to the use of cross-sectional data, the explanation of the type of housing tenure has been considered. Because of the lack of policy variables in household income-expenditure data, the possibility of providing policy recommendations is limited. Nevertheless, the results can provide the basis for providing policy recommendations that may be directly or indirectly related to the results of the research. The results show that the most important and effective factor influencing the increase in the share of civil housing is permanent income; in other words, one of the main tenants of low-income groups and special groups is that they do not have permanent occupation and income, and therefore policies affecting the permanence of this job At the same time, the groups will have a decisive impact on their housework.https://aes.basu.ac.ir/article_1891_5deee2770bf7b3bd05e3a088b98ff9b8.pdf