جستجوی مقالات مرتبط با کلیدواژه "رگرسیون چندکی" در نشریات گروه "علوم انسانی"

The issue of intellectual capital and its use as one of the main sources of competitive advantage has been investigated in numerous studies, and human capital and structural capital have always been regarded as two important components in many studies. Considering the importance of intellectual capital, the present study explores the effect of human and structural capital on the profitability and management of Iranian commercial banks using the quantile regression method. The data taken from the audited financial statements of 12 banks for 11 years were used in this study and the results show the significant effects of human capital in different quantiles on the profitability and management of such banks, while the impact of structural capital is only in one quantile. Also, the shape of the function was examined according to the literature and the results show a U-shaped relationship between human capital on the one hand and profitability and management on the other hand.

Extended Abstract
Considering the great importance of exchange rate fluctuations in various sectors of the macro economy, the issue of exchange rate fluctuations and its control has always been discussed in the framework of currency regimes in most countries. Considering that in recent decades, the managed floating system is one of the systems of interest in the world's economies, therefore, the level of involvement in this market and its measurement has played a very important role and has attracted the attention of model makers and economists. In oil-exporting countries, because the economy of these countries is dependent on oil, currency fluctuations and the level of intervention of the monetary authority in these countries are always very important. Most of these studies have identified and explained the paths and quantitative estimation of the effect of oil price on the macroeconomics of countries separately, and very few studies have examined the indirect channels of the effect of oil price shocks on the economy of countries. The point that this study emphasizes is that there is a relationship between oil prices and stock markets in oil importing and exporting countries, which has been neglected in many studies.

In this study, the relationship between oil prices and stock markets in major oil-exporting countries (Russia, Norway, Canada, Iran) and oil-importing countries (USA, India, Japan) has been investigated. For this purpose, quantile regression and weekly data from January 2010 to June 2022 have been used.

Different phenomena in the science of statistics appear in the form of random variables and it will be possible to investigate these phenomena by determining their distribution. In this regard, there are various statistical measures, each of which provides researchers with different information from the random variable distribution. Quantile or quantile regression is a statistical method for estimating and inferring conditional quantile functions. As linear and classical regression methods are based on minimizing the sum of squared errors and can estimate a model for conditional mean functions, quantile regression methods provide a mechanism for estimating models for the conditional mean function and a wide range of other conditional quantile functions. Quantile regression is able to provide a more complete statistical analysis of random relationships between variables. While standard regression methods show how the value of a dependent variable reacts to the change of an explanatory variable; Quantile regression shows the predicted changes for the entire distribution of the dependent variable and is used for the separate effects of the explanatory variable at different points of the distribution of the dependent variable. Of course, it should be acknowledged that one of the shortcomings of standard OLS regression is that the obtained estimate is a number that is used to summarize the relationship between the dependent variable and each of the independent variables.

Knowing the common movement between oil and stock markets is of great importance for investors and policy makers. In this context, there is a lot of empirical literature that shows the complexity of oil price dynamics and stock indices and then the relationship between them. In this study, using the quantile regression method, the relationship between oil price and stock index in oil exporting and importing countries in the period of 2010-2022 has been investigated. The results of the study showed that the price of oil has a positive and significant effect on the stock index in Russia, Norway, Canada, America and India in all deciles and the first 4 deciles in the Japanese stock index. Also, the price of oil has had a positive and significant effect on the stock index in Iran in 6 deciles (first 5 deciles, seventh deciles), and in the last two deciles. Also, by examining the R2adj statistic, it can be said that in all the investigated stock exchanges, except for Russia and Iran, 50 to 70% of the stock index changes are explained by the oil price, which shows the importance of the oil price on the stock index in these countries. In other words, the stock value can be considered equal to the sum of the discount of the future liquidity flows, and since these flows are directly affected by macroeconomic events, therefore, these flows can easily be affected by negative impulses. In addition, oil price volatility also has significant effects on stock market returns. The three categories of capital, manpower and oil can be considered as the most important factors that are used in the production of most goods, therefore, changes or fluctuations in each of these three factors will affect the cash flow.

Depending on whether they are oil importers or exporters, countries have different effects on oil price changes. According to the opinion of many economists, the price increase will bring a decrease in economic growth as well as an increase in inflation for the economy of oil importing countries. But if the oil importing countries have a strong and suitable economic structure, these fluctuations will have a less destructive effect on their economic conditions. In oil exporting countries, it is expected, of course, that the increase in the price of oil will increase their income, which will lead to an increase in their liquidity in the future, which will have a direct and positive impact on the capital market and the stock index. Now, if these incomes are used to buy domestic products, this increase in income leads to an increase in production and economic growth, and consequently to an increase in investment, which leads to an increase in stock prices. Therefore, according to oil price fluctuations in recent years, investors and policymakers should consider market conditions in their decisions based on modeling the relationship between oil and stock markets. Also, considering the changes that will happen over time in relation to the price of oil and the stock market, investors and policy makers should pay attention to this dynamic behavior of stock markets.

The purpose of this research is to statistically investigate the effect of trading volume on daily returns obtained in exchange traded fund as one of the new achievements of the capital market and the results obtained with the relationship between volume - returns are compared in the capital market. In order to investigate the subject of the study, information on the financial statements of tradable investment funds was extracted by Eviews and Stata statistical software during the year 2022. The results show that the trading volume (in terms of value of rials transactions and quantity) in Exchange Traded Fund has not significant impact on returns earned by them. Also, the trading volume carried out on the units of these funds had a negative and meaningless effect on the daily returns obtained by them. Regarding the relationship between volume and return in the capital market, the findings show that trading volume had a positive and significant effect on the daily return of the market. Also, with the increasing volume of transactions, the amount of daily return has been increased and more prosperity and efficiency have been observed in the capital market. In addition the use of quantile regression compared to normal regression, gives a better view of the effect of the independent variable on the dependent variable because the normal regression presents the result of the effect of the independent variable on the dependent in the form of an average while Quantile regression shows this effect in different quantiles.

Nowadays, as the most diverse, the tourism industry is the most vital source of income and job opportunities for many countries in the world. Iran has the potential to attract tourists due to its rich historical, geographical, cultural, and natural attractions. Iran has the potential to attract tourists due to its rich historical, geographical, cultural, and natural attractions. Knowing the factors affecting the tourism industry is critical; however, identifying tourism obstacles can be more vital. In today's world, various risks affect the economy of countries. On the other hand, climate change has become the most crucial concern of humankind in this century. This paper uses the quantile regression method to investigate the impact of country risk, including economic risk, political risk, financial risk, and ecological footprint, on tourism demand in Iran from 1989 to 2020. The results of this research illustrate that country risk and its components have a negative and significant impact on tourism demand in Iran; this can demonstrate the importance of risk and uncertainty in Iran's economy. Moreover, the ecological footprint as a proxy of climate change negatively and significantly affects tourism demand in Iran. According to the research results, it is suggested that policymakers adjust the policies to reduce the country's risk and increase the confidence of foreign tourists to travel to Iran. On the other hand, paying attention to the climate change crisis should become more critical for the government and society; otherwise, this crisis can negatively affect tourist attractions in Iran.

One of the key roles of futures markets is to provide risk hedging tools. The optimal strategy for risk hedging is determined by estimating the risk hedging ratio. Calculating the risk hedging ratio and the effectiveness of hedging explicitly depend on the relationship between futures prices and spot prices. Therefore, the aim of this study is to estimate the optimal risk hedging ratio in various timeframes under low and high volatility conditions using a Markov regime-switching multivariate regression model.

The slope obtained from the Markov regime-switching multivariate regression, representing the optimal risk hedging ratio, is chosen, which is dependent on the choice of timeframes and two cases for the multivariate regression model are adopted according to the level of volatility considered.

The research results on 5 futures contracts in the period from 2014 to 2018 indicate that in three markets, normal (composite), low volatility, and high volatility, risk hedging has been able to reduce risk by at least 20%. In the high volatility market, the optimal risk hedging ratio has reduced volatility by at least 23% in all timeframes (with the mean square error criterion), and the 0/95 timeframe performs the best in terms of the highest reduction in volatility and the lowest risk hedging ratio. In the low volatility market, the optimal risk hedging ratio has reduced volatility by at least 58% in all timeframes, and the 0/05 timeframe performs the best in terms of the highest reduction in volatility and the lowest risk hedging ratio. In the composite market, the optimal risk hedging ratio has also reduced volatility by 21%.Originality / Value: The results of this study not only contribute to the literature on risk hedging but also assist all stakeholders and users in evaluating the level of attention to the risk hedging topic.

Tax is one of the determining variables of financial and distribution policies in the economy, Therefore, knowing and understanding the sensitivity of different sources of tax income to changes in economic growth plays an important role in determining the optimal rate of various types of tax bases. Taxes can make a significant contribution to improving the stability of government revenues and implementing policies to improve income distribution, Therefore, recognizing the effect of economic growth as one of the economic capacities has an important role in determining taxes. In this regard, this paper is done using the statistical evidence of Iran over 1981-2019 and using the quantile regression approach to estimate the asymmetric elasticity of tax to economic growth. The results show that there is a kind of asymmetry in tax elasticities and in high tax quantiles, the elasticity value is greater than one and this indicates the financial stability of government revenues in high quantiles. In addition, the low tax elasticity in the tax on goods and services, companies and imports indicates the low financial stability of the government in this type of taxes. Therefore, focusing on increasing the share of taxes with higher elasticity is the most important proposed policy for the government in order to increase revenues.

Human development index is based on aspects of human quality in a country. Human development is affected by many variables. One of the main variables is urbanization. On the one hand, urbanization promotes human development by improving the level of income, educational and health services, and on the other hand, with the rapid growth of urbanization, increasing urban congestion, pollution and greenhouse gas emissions negatively affect it. Since rapid and incomplete urbanization has positive and negative effects on the economy, the purpose of this study is to investigate the impact of urbanization on the human development index during the period 1990-2017 with a multiple regression approach. The results show that the effect of urbanization rate on human development index is symmetric, negative and significant, which has become stronger in the upper quantiles. According to other results of this study, health, education and economic growth expenditures have also had a positive effect and CO2 emissions have also had a significant negative impact on the human development index; the pattern of population growth and migration be considered to changing the laws concerning the creation of new cities in order to adapt the urbanization rate to human development in the cities development plan.

This paper aims at investigating the asymmetric impact of long-term and short-term macroeconomic variables on the capital market prices of Iran.Macroeconomic variables are inflation, exchange rate, non-oil trade balance and crude oil prices. In order to investigate these relationships, the quantile autoregressive distributed lag (QARDL) method introduced by Cho et al. (2015) has been used. For this purpose, monthly data related to Iran's economy in the period 2008: M9-2021: M6, have been used. Findings show that in the short run, the macro variables used except the trade balance and oil prices have an asymmetric effect on the capital market price index. In the long run, all variables except oil price have an asymmetric effect on the stock price index and the effect of oil price is symmetrical and significant. This conclusion shows that in situations where the stock market price index is in a state of prosperity, recession or normal, except for oil prices, the effect of research variables on this index is not the same and even this effect is different in the short and long term.

Trade and climate change are some of the important factors affecting income inequality. With the globalization and trade interrelationships between countries, income inequality has been reduced to some extent. On the other hand, climate change as an important and current issue in the world has had great effects on economic growth and income inequality of countries. The purpose of this study is to analyze the relationship between trade and climate change and income inequality. To examine the asymmetric effects of trade and climate change on income inequality in 60 countries, the quantile panel regression model is used by 2010 to 2020 data. In addition, the Climate Change Performance Index for climate change and the Trade Openness Index for trade has been used. The results show that climate change over the years has increased income inequality in selected countries. Also, the results show that the third to seventh quantiles of income have been most affected by climate change. On the other hand, trade between countries has reduced the inequality of selected countries in different income quantiles.

Improving financial performance will not be possible by maximizing profits and benefits alone. Based on social contract theory, society allows the company to operate and they expects the company to serve the interests of all stakeholders. One of the expected benefits of all stakeholders is social responsibility. The special value of a brand and trademark as a brand name also is one of the achievements of social responsibility that leads to improving the financial performance of the company. Therefore, the purpose of this paper is to investigate the effect of social responsibility on the financial performance of the company by considering the moderating role of the value of brand name and trademark. This study uses quantile regression and structural equation modeling to explore the causal linkages among these factors in firms listed on the Tehran Stock Exchange over the period 2011–2015. The results of quantile regression analysis show that the economic dimension of corporate social responsibility and the special value of a brand and trademark are a positive significant effect on all of the quantiles. The brand extension driver provides a significant positive effect on the higher quantiles of firm performance. However, the findings indicate a significant negative effect on firm performance considering the brand loyalty driver.

Noise traders have an undeniable role in determining market volatility, returns and stock price movements. Therefore, In this paper, the effect of noise traders on the stock returns of companies with the aim of presenting an appropriate picture of how they are affected in extreme situations.The statistical population of this study includes all companies listed in Tehran Stock Exchange during the period of 2009-2017. The sample of 13717 data from 150 companies listed on the stock exchange monthly. The main hypothesis of this study is to evaluate the Extreme Effects of noise trading on stock returns by quantile regression was used to analyze the data. The findings of the research show that the level of noise activity increases with the level of efficiency Moreover,the positive effect of the noise trading index on returns with a coefficient of 0.0001.Under extreme returns, this effect is greater than the intermediate values and reflects the intensification of noise trading activity in periods of decline and market growth.

Depreciation of national currency has been one of the most important issues in Iranian economy throughout the recent years. The government has always tried to maintain stability and control this macroeconomic variable. One of the most important aims for the central banks all around the world is to preserve the value of the national currency; hence, they must implement proper policies that do not result in reducing the purchasing power of national currency. Alongside economic enhancement, the value of currency could be appreciated in regard to the relations between such variables as GDP, inflation, employment rates, and etc. Accordingly, the present study using a series of data in the period of 1973 to 2018 and applying the quantile regression investigates the effect of some macroeconomic variables such as bank interest rate, inflation, and GDP on the depreciation of currency in Iranian economy. The results show that bank interest rates and inflation have a positive and significant effect and GDP has a negative and significant effect on the depreciation of the national currency. Therefore, it is necessary for policymakers to help increase national production in the long run by controlling the real rates of interest in the banking system. It is obvious that increasing production will enhance the value of the national currency over time. Thus, governments can achieve the main goal of the economy that is economic growth.

The purpose of this article is to examine the impact of education and work experience on earning. For this purpose, Mincer’s wage equation, quantile regression estimation method and the microdata from Iranian survey of household income and expenses in 2016 have been used. Estimation results show that education returns are positive in all income quantiles, and education in lower-income quantiles has a stronger positive effect than in higher-income quantiles. Also, the average experience have a positive effect on the earnings of individuals, with a stronger effects in low-income quantiles than high-income quantiles. Gender coefficients show that female earnings in all income quantiles are much lower than males, but this negative effect was much bigger in lower-income quantiles, implying gender-based discrimination against women in low-income quantiles. According to Machado and Mata's decomposition, gender discrimination (against females) was estimated, -30% in the first decile, and -4.5% in the ninth decile. Women's education has narrowed the gap somewhat on behalf of women. According to the results, education efficiency in Iran is far lower than many other countries in the world. Therefore, it is necessary to reform educational structures, in particular to guide them towards labor market needs and economic benefits.

Monetary and fiscal policies are the most important policy tools that affect economic activities through economic authority decision. The aim of this study is to investigate the asymmetric effects of fiscal policy on economic growth in Iran by quantile regression approach using quarterly data of 1988-2016 period. The results showed that tax, investment and foreign trade had a significant positive effect on economic growth. In the low-quantiles of economic growth, the impact of government spending on economic growth has been positive, but the impact of government spending on economic growth has been negative and significant in high growth rates. Also, the effect of the budget deficit on economic growth at low rates of economic growth has been negative. At high rates of economic growth, this effect has been positive and significant. Also, the results showes that the effects of government deficit and consumption on economic growth were asymmetric. For the other variables symmetric effects were confirmed.

The importance of surveying the relationship between human capital and labor productivity is major at the macroeconomic level. Since the service section has a high share in the economy, this issue becomes more important. In this regard, in the paper, we have been studied the impact of different dimensions of human capital (education, health, and experience) on improving the workers' productivity of the service section in the micro level. In this order, we emphasize the individual characteristics of employees in this sector. We use the net income received from the wage earners and salaried workers in the private sector of services section (as a proxy for the labor productivity) to estimate an econometric model for the Iranian economy. In order to estimate Quantile Regression (QR), Households Income and Expenditure Survey is used for urban areas in 2013. The results show all dimensions of human capital (education, health, and experience) have a positive and significant effect on the productivity of labor (or net income) among wage earners and salaried workers of the service sector in different quantiles. In addition, in various quantiles, not only the coefficients of health indicators are more volatile, but also the coefficients of these indicators in many quantiles, especially in the lower half, are higher than the other indicators of human capital. In Q1, the rate of labor productivity response to health indicators is higher than that response in the higher quantile (Qh).

The aim of this research is to focus on systemic risk by assessing the extent to which the distress caused by the main different financial sectors including banks, insurance and investment companies can be spread over the risk of financial system. For this purpose, a method of measuring changes in Conditional Value at Risk (CoVaR) based on the financial sectors return is used and its value is estimated using quantile regression. Also, two-sample Kolmogorov-Smirnov test was used to determine the impact of risk imposed by financial institutions to financial system and to achieve a ranking of financial sectors contributing to systemic risk. In this paper, 24 financial institutions listed in Tehran Stock Exchange during the time period of 2011-15 was selected. The results show that all 3 sectors contribute significantly to systemic risk in Iran during this time period and the investment companies have the highest share in creating systemic risk and then the banking and insurance sectors, respectively.