جستجوی مقالات مرتبط با کلیدواژه "رگرسیون فازی" در نشریات گروه "اقتصاد"

The purpose of this article is to analyze the impact of open economy indicators including economic openness and foreign direct investment on income distribution in Iran during the years 1996 to 2018. For this purpose, we use fuzzy regression with asymmetric coefficients. The reason for using it is very high flexibility in analysis. For each income decile, an optimal model is estimated that shows the effect of open economy indicators on the Gini coefficient of income deciles. The results show that the average effect of foreign direct investment on the Gini coefficient of income deciles is zero or very small and reduces inequality. But the maximum impact of foreign direct investment on the Gini coefficient of income deciles has increased, which leads to inequality in income distribution. This is due to the lack of appropriate programs and policies in the economy, so that we can use the opportunity of foreign direct investment to reduce inequality. Also, the results show that the maximum effect of the degree of economic openness on the Gini coefficient of income deciles is zero, which means that the degree of economic openness does not increase inequality. In order to strengthen or weaken foreign relations. Therefore, the amount of subsidy paid to each of the income deciles provides the infrastructure needed to attract foreign investment and provides measures for the treasury to tax the income deciles.

Economic growth rate is one of the most important economic variables that are affected by various factors. According to empirical studies, many visible and invisible political, social, and economic factors have been effective on the growth rate in Iran; Factors that cannot all be included in an economic model. The conventional methods preclude us from commenting with certainty about the effectiveness of some of these factors on the growth rate. Based on this argument, in this research, the fuzzy approach has been used to investigate the effect of the economic sanctions imposed against Iran along with other variables affecting the economic growth rate in the period after the Islamic Revolution (1357-1400). The results show that severe economic sanctions with a high and strong fuzzy coefficient have had a significant negative effect on the growth rate in Iran. After severe sanctions, budget deficit, liquidity, exchange rate, and moderate sanctions have had the most negative effects on the economic growth rate in this period. The fuzzy coefficients obtained for the variables of oil revenues, labor force growth, physical capital, human capital, degree of openness of the economy, economic growth rate of the previous year, and net foreign direct investment in Iran, respectively, indicate the positive effect of these variables on the growth rate in this period of time. Also, the result of the re-estimation of the model, taking into account the first breaks of the variables of oil revenues and budget deficit in the economic growth rate, confirms the output of the first model. In this case, severe economic sanctions have had the most negative effect on Iran's economic growth rate.

The uncertainty in economic variables imposes irreparable effects on the society. Therefore, it is necessary to prevent the consequences of uncertainty in the economy. One of these phenomena is the impact of uncertainty in electricity and lighting consumption on inequality between income deciles. Therefore, investigating the impact of uncertainty in the economy on income distribution requires accurate modeling. In this article, by using the fuzzy regression model with symmetric and asymmetric coefficients in the MATLAB and Wegamz environment, the effect of uncertainty in the distribution of electricity consumption on the Gini coefficient of income deciles in Iran for the period of 2015-2018 has been investigated. The results show that the range of the right and left width of the Gini coefficient of all income deciles, except the first and second deciles, did not deviate significantly until 2013. However, since 2010, the range of deviation of the right and left width has increased, which indicates the increase in inequality in income distribution in the mentioned years. This issue highlights the importance of the distribution of electricity and lighting consumption in reducing income inequality to the extent of the left width on the income distribution. The evaluation of the results shows the extraordinary efficiency of the regression model with fuzzy coefficients.

Exchange rate is one of the important economic variables that are affected by various factors. Since the exchange rate in any country is considered one of the basic indicators in determining the degree of international competition and explaining the internal situation of that country's economy, with the aim of investigating the role of economic sanctions imposed on the exchange rate in Iran, this research was carried out. According to empirical studies, several visible and invisible factors have affected the exchange rate in the country; Factors that sometimes can not all be considered in one economic model; therefore, even with conventional methods, it is not possible to comment with certainty on the extent to which some of these factors affect the exchange rate. Based on this argument, in order to use an appropriate method, in order to investigate the effect of financial and commercial sanctions imposed on Iran along with other variables affecting the exchange rate in the period after the Islamic Revolution (1978-2020) from a fuzzy approach is used. The results show that severe economic sanctions with a high and strong fuzzy coefficient have had a positive and significant effect on the exchange rate in Iran. Also, liquidity, budget deficit, balance of payments, inflation and adaptive exchange rate expectations, respectively, after the severe sanctions, have had the greatest impact on increasing the exchange rate in recent years. The fuzzy coefficients obtained for the variables of oil revenues, the degree of openness of the economy, economic growth and interest rate on account of domestic investment deposit, indicate the negative effect of these variables on the exchange rate in Iran in this period.

In this study, analysis and identification of oil, gold and currency relations on the trade balance in Iran using the new hybrid approach of fuzzy regression and multi-criteria decision making. For this purpose, five fuzzy regression models FERRARO, M_ESTIMATION, LA_FUZZY, WEIGHTED FUZZY RWGRWSSION and FUZZY_OLS used for this purpose three goodness of fit criteria, in the form of options and indicators of a multi-criteria decision problem as input, were used for the annual data during the period of 1357-1999. TOPSIS decision making models were organized and then the above models were ranked and compared. To run the model, Python 3.9 software is used, and among the above models, the fuzzy weighted model has been introduced as the selected model. According to the results, among the variables of oil, gold and foreign exchange, the exchange rate variable has the most impact on the country's trade balance during the period under review. other results showed that the use of regression approaches with various logics in the design of the error function produces more accurate estimates of the coefficients. The range of minimum and maximum effect of exchange rate on trade balance was extracted as (1.474 and -1.382). These fuzzy cumulative regression coefficients, which are a combination of (regression coefficients, correlation coefficients, entropy, absolute regression coefficients) of variables, show that the priority of influencing the trade balance category is: exchange rate, oil price and then gold price.

Iran's economy has been facing the problem of inflation for more than half a century. According to empirical studies, various visible and invisible political, economic, cultural, etc. factors have been effective in increasing the inflation rate; Factors that sometimes cannot all be considered in one economic model. Therefore, it is not possible to comment with certainty on the extent to which some factors affect the inflation rate with conventional methods. Based on this argument, in order to use an appropriate method in this study, from the fuzzy approach to investigate the severity of the impact of economic sanctions imposed on Iran along with other variables affecting inflation, in the period after the Islamic Revolution (1978-2020), Used. The results show that severe economic sanctions against Iran with a strong fuzzy coefficient, have a positive and significant effect on inflation in Iran. Also, liquidity, people's adaptive expectations, budget deficit and exchange rate fluctuations, respectively, after the severe sanctions, have had the greatest impact on increasing inflation. The obtained fuzzy coefficients of oil revenues and economic growth (excluding oil) indicate the negative effect of these two variables on the inflation rate in Iran.

The uncertainty of macroeconomic variables imposes irreversible effects on economic phenomena. Therefore, it is necessary to avoid the effects of uncertainty on macroeconomic variables. One of these phenomena is income inequality. Hence, investigating the effect of macro variables uncertainty on income distribution requires careful modeling. In this paper, using the fuzzy regression model with symmetric and asymmetric coefficients in MATLAB and GSM program environment, we investigate the uncertainty of macroeconomic variables on Gini coefficient of income deciles in Iran for the period 1995-2007. . The results show that the Gini coefficient of the right and left widths of all income deciles except for the second and eighth deciles did not fluctuate significantly until 2011. The increase in the fluctuation of the right-income declining amplitude of the second decile indicates the inequality and the effectiveness of this decile due to the uncertainty of the large variables. The dramatic decline in the amplitude of the 8th Right Decade Width fluctuation in 2009 is due to economic developments and improved income distribution. Also, the increase in the amplitude of the right and left Gini coefficients of income deciles, especially the upper deciles in the years after 1390, indicates an increase in inequality in the income distribution in these years. As such, this inequality is more severe in the tenth income deciles, which makes the rich richer. The most common criteria for MSE, RMSE, MAPE, MAE are used to evaluate the results, which shows the excellent performance of fuzzy coefficient regression model.

Increasing economic growth is one of the most important goals of countries. To achieve this goal, it is necessary to know the factors affecting economic growth. The purpose of this study is to use the fuzzy regression model with symmetric and asymmetric coefficients to investigate the factors affecting Iran's economic growth with an emphasis on foreign direct investment and the banking sector in Iran during the period 2016-2016. The results of the fuzzy regression model with symmetric coefficients indicate that the increase in financial development reduces the instability of economic growth due to the bank-oriented nature of the financial sector in Iran's economy. The results of the fuzzy regression model with asymmetric coefficients show that the investigated factors have a constant effect on economic growth. Therefore, before expanding relations with foreign countries, policy makers should improve the necessary infrastructure and develop resources to increase the country's competitiveness and attract possible foreign direct investment spillovers. Considering the importance of the influence of the financial development index on economic growth, it can be said that the expansion of financial systems is a prerequisite for the positive impact of capital inflows on economic growth.

Considering the role of monetary policy in this paper, using linear regression model with symmetric and assymmetric fuzzy coefficients to analyze the money demand function in Iran economy for the period of 1396- 1375.We also used the Friedman and Chordiy study (1988 and 1996) to justify the model.The results of the estimation show the superior performance of the linear regression model with symmetric and asymmetric fuzzy coefficients. On this basis, it can be stated that in the estimation of the fuzzy regression model with the coefficients, the function of the money demand function is the same for all degrees of membership from 0.1 to 0.9. There is not even a very slight change in the mode value. Which implies keeping money with speculative and precautionary motives and will have a recession-induced monetary effect on the economy. On the other hand, in this regression, changes in the coefficients of elasticity have not resulted in any changes in the value of the objective function (money demand), which indicates the stability of the demand for money in Iran. Since the time period is considered in this long-term study, In terms of membership, 0.8 and 0.9, the demand for money is the highest and lowest, respectively, expressed in terms of liquidity and uncertainty conditions in the economy. Also, the stability of the coefficients of the explanatory variables of money demand in the linear regression model with symmetric and asymmetric fuzzy coefficients in Iran is somewhat consistent with the theoretical model of Sidrauski (1967). In other words, this monetary theory can justify the conditions of the Iran economy

Today, foreign direct investment is an important factor in creating broad economic changes for all countries, especially developing countries. Therefore, considering the importance of attracting foreign investment in the Iranian economy, the present study has examined the effect of economic sanctions and exchange rate uncertainty on FDI absorption in Iran using fuzzy regression model during the period 1990-2018. The results showed that high-intensity sanctions, which were mostly economic and comprehensive sanctions (such as the Central Bank and Iran oil sales sanctions), had a much deeper impact on capital inflows than low-intensity sanctions. Have had. It was also shown that the main obstacle to attracting foreign direct investment in Iran is the severe sanctions, which, like a high wall, overshadow other factors and have greatly reduced the incentives for foreign direct investment in Iran. Also, according to other findings, exchange rate uncertainty has a negative effect on attracting foreign direct investment in Iran. This finding confirms that the widespread exchange rate uncertainty in the Iranian economy has created an environment of uncertainty for investors to make decisions. Because it is difficult to predict profitability in the commercial and non-commercial sectors, as well as the cost of new capital goods.

In the modern world economy, foreign direct investment plays a very important role. Empirical studies show that the simultaneous study of the impact of corporate tax and exchange rate uncertainty on FDI absorption has not received much attention. In this article, we refer to the tax on corporate profits and exchange rate uncertainty on attracting FDI in Iran during 1981 to 2018 were studied. Other innovations of the present study can be described in terms of two virtual variables of low intensity and high intensity sanctions as well as the use of fuzzy logic concepts in estimating the coefficients of variables. The results showed that the intersecting effect of corporate income tax and exchange rate uncertainty, which represent profit and risk factors on FDI absorption, respectively, had a negative effect on FDI absorption. Also, comparing the coefficient of this variable with other factors of inflation rate, exchange rate and sanctions, it can be concluded that the intersecting effect of corporate tax and exchange rate uncertainty after the sanctions factor is the most important obstacle to attracting foreign direct investment in Iran.

Over the past three decades, Internet has been playing a major role in societies, and the rapid increase in its use reflects its impact on all aspects of the economy, including international trade. The present study intends to investigate the effect of Internet influence on Iran's goods and services trade with its largest trading partners. For this purpose, the fuzzy regression method was applied to investigate the relationships between variables from 1995 to 2016. In the present study three experimental models: 1) the effect of Internet influence on all goods trade and services of Iran with trading partners; 2) the effect of Internet influence on the Iranian goods export and services to trading partners; and 3) the effect of Internet influence on the Iranian goods import and services from commercial partners, within the framework of the modified gravity model are estimated. The results show that in all the three models, the variables of Internet diffusion and GDP per capita of Iran and trading partners and the population of Iran have a positive effect on trade in goods and services. Also, according to the results, it was found that the population of Iran's trading partners has a positive effect on the total Iran’s trade and exports to these countries, while it has a negative effect on Iran’s goods import and services. Furthermore, the findings suggest that the effect of the variables the distance and the exchange rate between Iran and trading partners on the total trade of Iran with these countries and export to them and imports from them is negative.

Foreign direct investment (FDI) is one of the major factors affecting the economic growth and development of a country. Iran's economic condition not only steers liquidity towards non-productive activities but also doesn't have sufficient domestic capital for economic growth and propensity. Thus, absorption of the foreign financial funds seems to be a useful and effective way to compensate for this shortcoming. Therefore, in this paper, we discuss on the importance of the variables affecting the FDI absorption in Iran during the period 1981-2016 using fuzzy regression with emphasis on the role of income tax. The results of the research show that income tax has a small effect on Iran's FDI absorption with a negative and negligible fuzzy coefficient. Further, income tax is not considered as the main determinant factor in attracting foreign investment in Iran. Also, economic factors such as GDP, commercial openness, human capital and population have a positive effect and inflation and exchange rates have a negative effect on FDI inflows in Iran.

The trade balance is one of the most important macroeconomic variables, and the macroeconomic strategic constraints for developing countries. The main target of this paper is study the effect of savings rate on the trade balance. According the article target we used time series data of Iranian macroeconomic variables during 1960-2015 with Application of fuzzy regression approach and Auto Regressive Distributed Lag. The results of Fuzzy regression approach shows that savings rate and GDP per capita has a positive effect on the trade balance in the short term and long term. In the other hand the real effective exchange rate Degree of trade openness has a negative effect on the trade balance in long term. Also The results of Auto Regressive Distributed Lag approach shows that savings rate, trade openness and GDP per capita has a positive effect on the trade balance and the real effective exchange rate has a negative effect on the trade balance. The other results improve that: error correction coefficient shows that 93 present of unbalanced short term adjusted to achieving long term balance. According to the results of research to reduce the trade deficit, an increase in gross domestic savings can be one of the important policy recommendations.

Internet by creating new conditions and opportunities for economic growth, allows the less developed countries to reduce their deep economic gap with developed countries. The purpose of this study is to investigate the effects of economic factors on the internet diffusion in Iran over the period of 1994-2015. Using a fuzzy regression, this study is going to estimate the type of relationship between variables which estimates a range of possible values of parameters. Results of the model show that, per capita income, urbanization rate, openness, human capital, and share of the service sector in GDP have a positive effect on the internet diffusion. But the impact of agriculture sector’s share in GDP is negative on the internet diffusion.

One of the main goals of developing countries is abating of stable and enduring economic growth. Therefore, recognition of effective factors on this economic growth is veryimportant. Due to special significance of effective factors on non-oil exports in trade policy making, in this study the influence of different variances such efficacy of export insurance subsidy on non-oil exports has been studied. Because of the high power models to predict the behavior of economic systems based on fuzzy regression, fuzzy regression is used for modeling were also fed examine the relationship between non-oil exports with export insurance and other variables in the period 1374 to 1394 subsidy fuzzy regression Used. The results obtained from this study show that short term and long-term export insurance subsidy have positive effect on non-oil exports of the nation. According to these results and main foresighted objectives defined in future economic development document, which include reduction of dependency to oil export revenues, in order to increase non-oil exports it is suggested to use state aid conveniences like export insurance subsidy.

National accounts data are of the most important statistical tools in planning and making economic policy. Therefore, forecasting the main economic variables in the economy is of great importance. Economic growth is one of the key macroeconomic variables, which gets top priority in forecasting. The purpose of this study is to identify the appropriate methodology for forecasting economic growth in Iran. This study introduces fuzzy regression model and its’ ability to forecast economic growth of Iran in comparison with Error Correction Model (ECM). To do this, the Iran’s GDP is modeled through ECM and Fuzzy regression models using annual data form 1959 to 2001. Then, Iran’s GDP growth is predicted for 2002-2012. Finally, the performances of these models are compared using common criteria for evaluating forecast accuracy including mean absolute error (MAE), root mean square Error (RMSE), mean absolute percentage error (MAPE) and Theil’s inequality coefficient (TIC). The results indicate that the performance of fuzzy regression is far better than that of ECM in predicting GDP growth in Iran. Moreover, forecast accuracy of fuzzy regression model is of statistically significant difference in comparison with ECM model.