bayesian approach

The world has suffered severe environmental degradation in the last two decades. The effects of ecological deviations and environmental destruction are alarming and cause concern for stakeholders and environmentalists. These problems have led to environmental disasters such as extreme weather changes and rising sea levels. For this reason, countries are trying to address environmental crises and economic growth at the same time. Basically, it is believed that the destruction of ecosystems in many countries is the result of human actions, including rapid industrialization, population growth, expansion of economic activities, urbanization, and widespread consumption of fossil fuels. Undoubtedly, one of the main factors of climate change and environmental degradation is the emission of carbon dioxide (CO2) caused by economic activities. The relationship between carbon dioxide emissions and economic growth has attracted the attention of researchers and policymakers to reduce carbon dioxide emissions without affecting economic growth. Achieving this goal may be difficult, but studies have included various factors in the growth-carbon dioxide relationship, such as different energy sources, technological innovations, population, financial development, urbanization, and trade openness. In addition to the factors mentioned above, there is a need to assess other relevant factors such as human capital for effective policy development. Despite the extensive role of human capital in promoting energy efficiency and economic growth, by skilled workers in the production process and the preference of educated households to use home appliances with high energy efficiency, few researchers have included this factor in the existing literature. Education is considered as one of the most important human capital investments and plays a vital role in the process of economic growth. Education, at the micro level, affects the future income flow of people, and at the macro level, it is thought that a workforce with better education increases the stock of human capital in the economy. Increased human capital in turn can have positive externalities, such as lower crime or improved health outcomes, which are socially desirable and likely to have positive effects on productivity. In fact, it is the existence of such positive externalities that provides economic justification for governments to support education. It is believed that public spending on education and health will lead to improvement of human capital, reduction of poverty, economic growth and development, and reduction of pollution.
Theoretical studies emphasize different mechanisms through which education affects economic growth. (1) Education increases the human capital of the labor force, thereby increasing labor productivity and shifting growth toward a higher equilibrium level of output. (2) In endogenous growth theories, education increases the economy's capacity to innovate new technologies, products, and processes, thereby promoting growth.
In the environmental field, improving human capital can reduce the use of fossil fuel in the production process. The motive of this research is to understand the effect of public education expenditure shocks on economic growth and environmental quality in Iran.

To achieve the goal of the research, a dynamic stochastic general equilibrium (DSGE) model and Bayesian approach have been used. In this regard, observable variables, gross domestic product, private consumption, investment, government expenditure and the gross growth rate of money have been used in the period of 2004 to 2021.

The results indicate that an increase in public education expenses by one standard deviation increases the marginal efficiency of private education expenses. Because private and public education expenses complement each other and entered the model in the form of a Cobb-Douglas function. Therefore, education hours, investment in education and subsequently, human capital increased. The increase in human capital led to an increase in production, economic growth and a decrease in inflation. The decrease in inflation led to an increase in the real wages of the workforce and finally the desire of the household to increase the supply of labor. Consumption increased in response to an increase in real wages. Also, the behavior of investment is very similar to the behavior of consumption and production, but its changes are more intense than other expenses. Therefore, it can be said that the shock of public education expenses has affected the performance of the economy like an expansionary impulse and has improved the economic conditions. In the environmental sector, with the increase in human capital, economic growth has been improved and this has led to an increase in air pollution and a decrease in environmental quality.

In justifying these results, we can point to the weak institutional quality in Iran, that despite the huge amount of human capital investment, there is no necessary effectiveness in the environmental sector, and this is a confirmation that Iran is in the early stages of economic development. As a policy recommendation, in addition to paying attention to education and its role in the development of human capital as a long-term investment, improving the quality of institutions should be considered

According to some common views in macroeconomics, fiscal policy can be effective in stabilizing the economy and achieving macroeconomic goals. The past few decades have seen the widespread use of monetary policy tools for this purpose. Since the onset of the global financial crisis in 2008, there has been a renewed interest in using fiscal policy as a stabilizing tool. The macroeconomic implications of government expenditures and revenues and their effects on the overall economic structure have been studied by various empirical methods in several countries as well as in Iran. In this paper, far from previous studies, the issue has been implemented using Bayesian structural vector autoregression (B-SVAR). Because this method takes the previous information into account, the B-SVAR method is able to make more accurate estimates than other VAR models. Empirical findings show that government spending and income (income tax) have a limited effect on macroeconomic variables, including GDP, inflation, private sector consumption, income tax and total investment, and in the housing sector. Also, the impact of the private sector on production fluctuations in this model was not confirmed.

Unemployment is one of the most important issues in all countries around the world. An increase in the number of unemployed in any society will cause many problems. Due to the high importance of the issue of unemployment, in many researches, the correct recognition and deep understanding of the factors affecting unemployment to reduce it has been considered. The current research collects data related to the Population and Housing Census 2016 of the Statistical Center of Iran. In these data, the economically active population and the number of unemployed, aged 15 years old or above are categorized by gender and different levels of education in Iran counties. The aim pursued during this research is the spatial modeling of the number of unemployed in counties of Iran, based on gender and education as covariates. To achieve this goal, the Bayesian approach and a method called “integrated nested Laplace approximation” or INLA for short, have been used. After fitting the appropriate spatial model, the coefficients of covariates have been estimated. The obtained results show that women's unemployment is higher than men's, and unemployment has also increased with the increase of education level compared to illiteracy. Finally, the spatial effects of the counties have been estimated and the counties with the highest and the lowest risk of unemployment, as well as the counties that have no significant difference with the average unemployment of the country, have been identified.

By studying financial crises and stress in recent years, especially in Iran, and its rapid spread and transfer to the economy, it can be seen that the effects of financial stress on macro variables in the field of knowledge-based economy have been given less attention. Therefore, the aim of this research is to estimate the response of macro variables related to the field of the knowledge economy (economic complexity index) in the face of financial stress shocks in Iran. In order to achieve this goal, the effects of systemic financial stress shocks on economic complexity and its determinants, including foreign direct investment, are evaluated during the period of 2017-2018 with the help of the Bayesian autoregression approach. The results of the study show It shows that the investigated variables, including economic complexity and foreign direct investment, react negatively to financial stress shocks in the long term, and their reaction is dampened over time.

An inefficient firm causes a waste of resources by using a non-optimal combination of capital and labor and especially energy. Measuring inefficiency and identifying the factors that cause it is vital in achieving the firm's potential production and, as a result, the sustainability of economic growth and increasing the economic well-being of the society. Meanwhile, choosing the right model to measure inefficiency is always one of the basic challenges among researchers in this field. This study attempts to provide an overview of the concept of efficiency and its types and different measurement models with an emphasis on the Frontier Analysis. Considering the large volume of theoretical and experimental studies in inefficiency, our review aims to provide a proper understanding of their strengths and weaknesses by investigating the evolution of the models demonstrated in the literature, and thus helps the researcher to create a clear framework for analysis and choosing the appropriate model. Furthermore, this study offers suggestions to measure inefficiency as accurately as possible from the theoretical and technical viewpoints, which have been understudied in the literature.

Multidimensional poverty is a complex issue that is affected by various factors. Therefore, accurate knowledge and its comprehensive definition are great important. The aim of this study was to investigate the effect of economic, social and institutional factors on multidimensional poverty based on a comprehensive measurement framework and using the beta linear mixed regression model, which is a suitable for response variables in the range (0,1). The parameters are calculated with the Bayesian approach. The Multidimensional Poverty Index has been calculated using the Multiple-Indicator Demographic and Health Survey 2015 and the Alkier-Foster method. In the group of economic factors affecting multidimensional poverty, four variables of inflation rate, unemployment rate, economic participation rate and net rate of provincial immigrants, in the group of social factors, two variables, level of development and urbanization, and in the group of institutional factors, three variables of crimes and the number of people supported by social harms, and the level of satisfaction of the administrative apparatus are considered for modeling. The results showed that both economic and social factors affect the multidimensional poverty index. Inflation rate and urbanization rate affect the average and unemployment rate affect the variance of multidimensional poverty among provinces

The purpose of this research is to measure the implementation of performance-based budgeting in the three dimensions of strategic planning, cost analysis and performance management, as well as to identify and discover effective actors on the implementation of performance-based budgeting. The questionnaire and data collection tools were collected through the distribution of 152 questionnaires among budgeting experts, accountants, senior managers and financial managers in 1400. Data analysis was done using structural equation method and SmartPLS software. The findings showed that the implementation of performance-based budgeting in the dimensions of strategic planning, cost analysis, and performance management is 25.95%, 31.25%, and 34.38%, respectively. Also, the findings showed that the actors directly and indirectly influencing the implementation of performance-based budgeting include economic actors, technical actors, management competence and organizational commitment. Based on the results of the research, it can be said that in order to implement performance-based budgeting, the existing procedure in the dimensions of financial knowledge of the organization, environmental activists, and organizational commitment should be redesigned and reconsidered. Also, the dimensions of technical actors (accounting information system and networks), economic actors (program and budget organization) and management competence and organizational culture should be modified and strengthened from a technical and supervisory point of view. The results of this study can be effective in attracting the attention of senior executive managers and academic researchers and supporting and modifying the dimensions that affect the implementation of budgeting, as well as expanding theoretical foundations in the field of performance-based budgeting.

Exchange rate volatility can affect the performance of macroeconomics, especially the competitiveness of countries. Real exchange rate volatility indicates the instability and uncertainty in relative prices. It creates an unstable and uncertain environment in the economy. Therefore, the study of the impacts of exchange rate volatility has a great importance. In order to study its effect, it is necessary to first measure the volatility as a quantitative variable. For this purpose, various econometric methods have been introduced, such as the Generalized Autoregressive Conditional Heterogeneity (GARCH). In general, choosing an accurate method for modeling and predicting volatility in economic variables has always been one of the goals of many studies. If volatility is not calculated accurately, misleading experimental results are obtained. After the critique of Lucas (1976), it became important to pay attention to models with time variable coefficients. One of the implications of Lucas' critique is that the estimated coefficients to express the relationship between the variables cannot be stable. This issue is especially important in developing countries where the process of changing economic structures is faster and wider. So, the purpose of this study is to introduce a time-varying GARCH model to calculate the exchange rate volatility in Iran.

The research was based on the monthly exchange rate data (US dollar to Rial) in the market during 1985-2019. The data were extracted from the online database of the Central Bank of Iran. For the calculation of the volatility in economic variables, the tendency to use GARCH models is greater than other models. Among the GARCH models, GARCH (1,1) is the most popular. However, according to Stock and Watson (2008), because of structural breaks, the most important problems of GARCH models is that they cannot make accurate predictions over the time. This gave more attention to time-varying parameter models and Markov chain Monte Carlo models. In these models, estimation coefficients can change over time. Also, the study of the financial time series shows that most of them are influenced by domestic or foreign political, economic and social events, such as financial crisis, oil shocks, war, political instability and sudden changes in foreign exchange policies. The effects of these events sometimes remain in the market for a long time. Therefore, it can be said that models with fixed coefficients do not have the ability to formulate the behavior of exchange rates, especially in the long-term horizons. Thus, it is necessary to use time-varying models with variable coefficients over time. In this study, in addition to conventional GARCH models, a time varying GARCH (TVGARCH) is introduced to show the different behaviors of exchange rate over time. For this purpose, the TVP-SVM model based on the model introduced by Koopman and Hol Uspensky (2002) is used to examine the exchange rate volatility in Iran. In this study, following Chan (2017), the Monte Carlo Markov Chain (MCMC) approach is used to estimate fixed coefficients and variable coefficients over time. Also, the Bayesian approach and the Markov chain algorithm have been used to simulate the prior distribution if common functions.

In order to calculate exchange rate volatility using GARCH family models, the ARIMA model was first estimated using the Box-Jenkins method, and then the GARCH model was estimated on the ARIMA model residuals.The results of the Zivot- Andrews unit root test showed that the exchange rate has a non-stationary level. The test also confirmed the existence of a structural break in the time series. The estimated values ​​for the coefficients of the GARCH and EGARCH models showed that the conditional volatility in this period is positively affected by the amount of variance in the previous period. Also, more than 95% of the variance of each period is transferred to the next period. The estimated coefficients also showed that there is a significant difference between the effect of good and bad news on exchange rate volatility; good news (i.e., the news that increases the dollar price) compared to bad news (i.e., the news that decreases the dollar price) have greater impacts.As the results of the TVGARCH model showed, the volatility average was 0.358 with 90% confidence, and the logarithm of the variance index was, thus, in the range of -0.79 to 1.557. In the calculations, ϕ represented the effect of the variance of the previous period on the variance of the current period. It was positive and indicated that approximately 96% of the volatility created in each period was transferred to the next period. Also, α represented the relationship between the exchange rate volatility and the exchange rate in each period. The average obtained for this coefficient was positive, implying that, in periods when the exchange rate increases, the exchange rate volatility is higher.According to the comparisons performed on the sample and based on the MSE criterion, TVGARCH is the most accurate, and GARCH is more accurate than EGARCH. Moreover, according to the QLIKE criterion, the TVGARCH model is more powerful than the other two conventional models, and the EGARCH model has a higher accuracy than GARCH.

According to the results, the TV-GARCH model is more powerful than conventional GARCH models. This indicates structural failure and changes in the behavior of the exchange rate in the Iranian economy. It has caused the relationship between the components of the conditional variance to change over time. In other words, there are different values ​​for GARCH coefficients in different periods. According to the results of the TV-GARCH model, the relationship between exchange rate and volatility has been varied over the time, which implies changes in the behavior and motivation of economic agents and the structural changes in the Iranian economy. According to the results, the use of the TV-GARCH model instead of the conventional GARCH with fixed coefficients is recommended.

Using the monthly data of the returns of 5 assets during 05/31/2011 to 02/28/2021, the volatilities of Iranian asset markets have been modeled in this paper. Factor multivariate stochastic volatility model in the framework of space-state approach is the basis for decomposing the asset market volatility into two components, “volatility rooted in latent factors” and “idiosyncratic volatility” and estimating time-varying covariance matrix and dynamic pair-wise correlation of time series. The findings reveal that: first, there are two latent factors. Second idiosyncratic volatilities of 3 assets, including stock, dollar and gold have increased since mid-2017, and emerges evidence of clustering behavior. Third, the volatility of inflation is explained by the hidden factors, and consequently the idiosyncratic volatility is almost smooth. Fourth, the volatility of stock return is highly correlated with the volatilities of inflation and dollar. In addition, there is a significant pairwise correlation between inflation-dollar and inflation-interest rate.

Modeling of oil price behavior depends on whether we distinguish the observed data as examples of deterministic or random trend processes. The distinction between these processes is crucial because of the long-term response of data series to shocks. Generally, in standard significance tests, the null hypothesis is tested for the difference between the insignificant and significant hypotheses. In the present study, to investigate the root of the non-difference stationary unit from Bayesian methods, we estimate the RCAR model considered for oil prices. The results of this study show that despite confirming the unit root in oil price data, it is not clear that whether a random step process is suitable for predicting Brent crude oil prices or not because confirming the existence of a random unit root does not necessarily mean that the crude oil spot prices follow a Brownian process. Therefore, it is not possible to conclude from such results that the efficiency of the oil market is insignificant. However, because the unit root in the crude oil data is random, it does not persist with the differentiation of the crude oil data; therefore, the use of the differentiated variable in conventional co-integration models will be incorrect. Also, because the crude oil is the input of many industries, this anonymity could be transferred to other macroeconomic variables. Therefore, theories of business cycles that portray temporary economic shocks will have weak support.

Using the daily data of the price index of 15 industries during 2016/10/30 to 2021/3/27, the volatility of Iranian stock market has been modeled in this paper. Factor multivariate stochastic volatility model in the framework of space-state approach is the basis for decomposing the stock market volatility into two components, “volatility rooted in latent factors” and “idiosyncratic volatility” and estimating time-varying covariance matrix and dynamic pair-wise correlation of time series. The findings reveal that: first, there are two latent factors; the first affects the volatility of domestic industries (real estate, agriculture, food products, sugar, cement, etc.) and volatilities of commodity-based industries (chemical and petrochemical industries, petroleum products, basic metals and metal products) are affected by the latter. Second idiosyncratic volatilities of each industry increase during this period, and evidence of clustering behavior emerges. Third, the volatility of banking industry’s stock return is influenced by both of latent factors and its idiosyncratic volatility lies in the middle of industries. This finding is logical and predictable because banks lend to a wide range of different industries, so idiosyncratic volatility of this sector and factors loading will be the weighted average of other industries. Fourth, the highest degree of pairwise correlation is observed between the 4 commodity-based industries, which have had an upward trend during this period. JEL Classification: C11, C32, C58, G17

Taxes and tax revenues are one of the most important tools for stabilizing governments. In macroeconomics, the impact of taxes is examined in terms of their impact on total consumption and aggregate demand. The role of the tax system in an economy is based on economic, political, and social objectives, while the political and social objectives are more influenced by the economic objectives of taxation, considering the monetization aspects of the government and its impact on the other sectors of the economy. Since the tax size plays a key role in providing the revenue requirements of the governments, implementing any tax policy requires good insight into how the economy must perform to achieve the optimum size of tax revenue with minimum inefficiency. Implementing any tax policy to provide the revenue sources needed for government spending can have different effects on economic growth and income distribution. In this regard, many studies have shown that, among the tools of government fiscal policy, construction expenditures and taxes can have significant direct and inverse effects on economic growth respectively, while consumption expenditures do not have a significant effect on economic growth.

In general, because of their effect on the return on physical and human investment, taxes can affect economic decisions and ultimately the growth rate of the economy as a whole. Given the current state of the Iranian economy, which is accompanied by low economic growth, dependence on oil, the existence of inequality in income distribution and change of the population ratio in favor of the retired and the disabled, reducing the economy's dependence on oil revenues and increasing the government reliance on tax revenues seem more than necessary. For this purpose, in this study, the Bayesian approach and Klein's basic model were used to study the demand structure of Iran during the years1989-2016, which provides the possibility to appraise the impact of policy variables on aggregate demands. The advantages of this method are due to the use of non-data to obtain a clear picture of the state of the economy as a result of economic change.

According to the results, the effect of taxes on reducing consumption and aggregate demand is relatively small. Therefore, as it seems, raising taxes will not reduce production. This is only a short-term consequence of tax increases. On the other hand, raising taxes by stimulating people's tendency to save can improve the country's long-term growth conditions. Also, the use of appropriate increases in tax rates means that the methods of financing the inflation deficit are not used and, thus, the value of the national currency is not weakened. In addition, financing through taxes reduces the country's dependence on foreign countries in order to cover the capital deficit in the balance of payments. The results of this study indicate that, in order to cut the government's dependence on oil revenues, the government could rely on tax revenues given that the total effect of taxes on income and consumption is not high; it is better and quicker to reach the desired income (an increase of one trillion Rials in taxes will lead to a reduction in consumption of just about 200 billion Rials). Moreover, the results indicate an increase in the total expenditures by the government to 1,000 billion Rials will increase the consumption to about 290 billion Rials. According to the results, the effect of taxes on reducing consumption and aggregate demand is relatively small; therefore, as it seems, raising taxes will not reduce production. This is only a short-term consequence of tax increases. On the other hand, raising taxes stimulates people's tendency to save, which can improve the country's long-term growth conditions. The use of appropriate increases in tax rates means that the methods of financing the inflation deficit are not used and, thus, the national currency is not devalued. Financing through taxes also reduces the country's dependence on foreign countries in order to cover the capital deficit of the balance of payments. According to the results, the ratio of taxes to GDP is always lower than the ratio of current government expenditures to GDP. This indicates that, as governments have grown, the share of government revenues from taxes has been very low. Therefore, to maintain this ratio, it is necessary to either increase government revenues or reduce government expenditures. Of course, regarding the government expenditures, the difference between the current and development expenditures must be taken into account. This is because the reduction of construction costs and insufficient growth in infrastructure can reduce economic growth in the future.

Due to the difference in the final tendency for consumption among different segments of the society, the adoption of any tax policy or the imposition of any tax revenue does target the income of a segment of the society. Therefore, different types of taxes can have different effects on the private consumption of the society, and recognizing it will help the country's tax policymakers to increase the efficiency of different types of taxes. Given the recession-inflationary conditions of the Iranian economy, which is accompanied by low oil-dependent economic growth, rising prices and inequality in income distribution, reducing the economy's dependence on oil revenues and increasing government dependence on tax revenues seem more than necessary. Utilizing the maximum tax power of the country and increasing the tax base to improve the performance of the country's tax system will make it possible to provide goods and public services desired by citizens by relying less on oil revenues. Since the effect of the total taxes on income and consumption is not significant, in order to break the government's dependence on oil revenues, it is possible to act better and faster to achieve the desired revenue. However, the effect of the exogenous increase of taxes by the government on the total expenditures should not be overestimated. Given the low impact of consumption taxes, it is highly recommended to determine the optimal rate of consumption tax from the revenues of this type of tax to build the corresponding infrastructures. Moreover, appropriate resources and increased government spending on development should serve to reduce the role of oil revenues from the budget to create the conditions for better economic growth in the future.

During the last decade, studies on the factors affecting stock market returns have reached a peak with the advances of financial economics in the field of statistics and mathematics, and modeling is of great importance in this regard. Accordingly, this study seeks to present a new approach to modeling the nonlinear relationship between financial variables and stock returns. This paper employs Bayesian Markov chain Monte Carlo (MCMC) sampling methods for updating the estimates and quantile threshold regression with heteroscedasticity. To study and model this approach, we used returns of the Tehran Stock Exchange, Coin Price, Oil, and Gold Price from 2011 to 2019. The results show that these variables have different effects under low and upper quantile levels. Also, the impact of the financial variables on the stock returns is different under higher and lower threshold amount for each quantile levels. Based on the result, we can say that stock returns have a nonlinear relationship with other markets in the bullish and bearish market.

The purpose of this research is to measure the efficiency of selected Iranian banks from 2010 to 2016. We estimate efficiency, productivity and efficiency changes under the Bayesian setting. In this research, we consider the Non-Performing Loans (NPLs) of banks as bad outputs, while good outputs are loans, off balance sheet assets and securities. Also, variables such as number of employees, bank capital, fixed assets and deposits play inputs role. Results show that the productivity changes of selected Iranian banks are positive over the period of the study, which is mainly due to the improvement in technology like e-banking services, while efficiency changes continued to be negative over the same period. It is observed that the average efficiency for banks is 85 percent. The Karafarin bank has the largest efficiency among selected banks while Refah Kargaran bank efficiency has the smallest one. Results also show that, during the specified period, private banks have a few better efficiency estimate in comparison with the efficiency of state owned banks.

The purpose of this study was to develop Bayesian modeling of returns volatility and turnover volumes. On this basis, the volatility of the Tehran Stock Exchange index returns and its trading volume have been studied with daily, weekly and monthly frequencies during the period of 21 April 2015 to 27 February 2019. The research findings show that the CCC model assumption of constant conditional correlation between the variables is violated and it is observed that the existing relationship is a dynamic conditional correlation type of DCC model and a negative one which implies that with increasing returns, investors due to optimism is less reluctant to sell their stock and by failing to sell it reduces the volume of transactions in the market and vice versa. On the other hand, the research findings show that considering the skew student t distribution for errors with a wider tail than the normal distribution and skewness application, it has a better performance than the other distributions.

Characteristic of skewness, fat tails and frequency dimension are important features of financial time series that have not been taken into account in classical econometric models. Therefore, in this study, the Bayesian method for conditional variance heteroscedasticity based on wavelet analysis has been used to investigate the volatility spillover effect and dynamic conditional correlations in three sub-periods between the daily return data of selected Tehran Stock Exchange (TSE) indices during the period from December 14, 2008 to April 20, 2019. Sub-periods are defined according to the Joint Comprehensive Plan of Action (JCPOA), which includes the pre-JCPOA period, JCPOA period, and the period after United States withdrawal from the JCPOA. The results of the Bayesian DCC GARCH (1,1) model, with the rejection of the constant conditional correlation hypothesis versus the dynamic conditional correlation hypothesis based on posterior marginal distribution in all subsections, indicates that the impact of shocks on the volatility of stock returns in wavelets and sub-periods are not the same. Also, Bayesian dynamic conditional correlation graphs are recommended for each sub-periods and in each wavelet, a different stock for a suitable investment. The main idea used in this research can be used in the calculations of asset risks and the selection of optimal asset portfolios.