کامبیز پیکارجو

 Seigniorage, in addition to being a crucial economic tool, holds a significant position in terms of governance for governments. The way governments manage the power obtained through seigniorage can be influenced by granting independence to the central bank or by adhering to principles of good governance within social institutions. Good governance has several positive effects on government revenues and efficiency. While the use of seigniorage may initially appear as a natural right of governments, if it leads to a faster growth rate of money compared to economic growth, it can result in inflation and impose inflationary taxes.According to the theory of financial dominance, the practice of monetizing deficits undermines the independence of the central bank, rendering it unable to exercise proper monetary control and policymaking. In countries that are members of the OPEC cartel, where government budgets are often closely tied to oil revenues, budget deficits can be influenced by fluctuations in oil prices, which can incentivize governments to use seigniorage more frequently. Additionally, in the context of good governance, covering up government inefficiency and corruption can also serve as an incentive for increased use of seigniorage. This research focuses on exploring the relationship between good governance and seigniorage in OPEC member countries that have access to oil revenues.

This study has employed a panel vector autoregression model to investigate the relationship between good governance and seigniorage among the member countries of the Organization of the Petroleum Exporting Countries (OPEC) during the period of 1995-2020.

Among the OPEC members, four out of the six facets of good governance were found to have a negative correlation with the government's use of seigniorage. However, no statistically significant correlation was observed between other facets, namely changes in political stability and the absence of violence and terrorism, government effectiveness, and the government's use of seigniorage. The findings also indicated long-term effects of good governance shocks, as an institutional factor, on seigniorage, lasting for up to seven years. Therefore, this study cautiously confirms the negative correlation between the good governance index and the government's use of seigniorage. 

In OPEC member countries, where governments also control oil revenues, good governance can play a role in managing these revenues. Oil revenues have two effects on government decisions; an income effect and a monetary effect. In terms of income, it increases the government's available revenues, while in terms of money, it can contribute to the country's monetary base. However, unless the domestic equivalent of money is received from the central bank and introduced into the domestic economy, it does not lead to a change in the monetary base and inflationary effects. In countries with active and effective social institutions, better governance enables governments to use oil revenues more efficiently, often directing them towards investment funds to increase the country's wealth. Therefore, the impact of oil revenues on seigniorage in OPEC member countries is determined by the combined effect of these two outcomes. Furthermore, good and efficient governance by the government leads to a reduction in government expenditures, subsequently reducing the need for inflationary financing and the use of seigniorage. Good governance, considering factors such as responsiveness and the political costs of the government when using seigniorage, increases the costs and reduces the government's inclination to rely on inflationary financing. The research results did not indicate a negative relationship between the variables of "political stability and absence of violence/terrorism" and "government effectiveness." Several factors may contribute to this finding, including the fact that the studied countries are developing nations where government expenditures increase significantly with improved political stability through development programs. Moreover, except for Kuwait and the United Arab Emirates, the government's efficiency in most OPEC member countries is somewhat negative and exhibits low volatility, which may explain the lack of significant impact from changes in government effectiveness on seigniorage. As various studies have confirmed, improving good governance has positive effects on economic growth. With some caution, this research suggests that enhancing good governance leads to less inflationary financing and reduced government reliance on seigniorage. Therefore, establishing the necessary conditions for better governance in OPEC member countries can help curb inflation, which is one of the main consequences of the government's reliance on seigniorage. Establishing good governance based on appropriate economic, political, cultural, and social structures is feasible, but the decision and willingness of rulers to implement it undoubtedly remain among the most influential factors in this endeavor. The investigation of good governance and the seigniorage in Iran also demonstrates that Iran relies more on the average OPEC members for inflationary financing or using the seigniorage, and its governance indicators are lower than the selected countries' average. Therefore, based on the results obtained from the analysis of responce functions in empirical analysis, it is essential to pay attention to changing and improving the methods of governance and administration of the country. This way, monetary governance can be improved, and the government's use of the seigniorage can be limited

Digital currencies, or cryptocurrencies, are electronic assets that utilize blockchain technology to ensure the security and transparency of transactions. These currencies operate in a decentralized manner and are not controlled by any central authority. Bitcoin, as the first and most well-known cryptocurrency, was created in 2009 by an anonymous individual or group under the name Satoshi Nakamoto. Subsequently, other cryptocurrencies such as Ethereum, Ripple, and Litecoin weredeveloped.Transactions using cryptocurrencies are often faster and less expensive than traditional banking methods. These digital currencies have become a popular investment option, but their extreme volatility can increase investment risks. In many countries, clear regulations regarding the use of cryptocurrencies have not yet been established, which may pose legal challenges for users. The application of artificial intelligence and machine learning models, particularly artificial neural networks, is crucial for predicting cryptocurrency prices. These digital currencies are accompanied by vast amounts of data, and analyzing them manually can be challenging. AI can process and analyze this extensive data. Artificial neural networks can identify complex, nonlinear patterns in data that may not be detectable by traditional methods. Machine learning models, using advanced algorithms, can enhance the accuracy of cryptocurrency price predictions and assist investors in making better decisions. These models can perform analyses faster than traditional methods, which is highly significant in the volatile cryptocurrency markets.

Artificial neural networks consist of numerous processing units called neurons that operate in coordination, similar to human brain neurons, to solve problems. The connections between these neurons determine the network's performance. In these networks, a data structure is designed through programming to act like neurons, and then connections between the neurons are established, followed by the application of a training algorithm to train the network. Artificial neural networks learn similarly to humans through examples.Artificial neural networks have the ability to adapt to rapid market changes and can identify new patterns. Utilizing AI can help reduce errors caused by emotional decision-making or human biases. These models can simultaneously analyze the impact of various factors, such as economic news, regulatory changes, and social trends. Machine learning models, with access to more data and continuous feedback, can continuously improve their performance. The more precise information and deeper analyses provided by these technologies can serve as powerful tools for investors, minimizing risks. The real-time implementation of these models is possible, which is a significant advantage for active traders.Based on the aforementioned considerations in this research, the focus has been on predicting Bitcoin prices over short-term (10-day) and long-term (30-day) periods. For this purpose, an artificial neural network optimized by six metaheuristic optimization algorithms has been employed. These algorithms include the Political Optimizer (PO), Hierarchical Rank-based Optimization (HBO), Stochastic Colors Optimizer (SPO), Giza Pyramid Construction (GPC), Firefly Optimizer (FHO), and Fox Optimizer (FOX). Finally, the results obtained from these algorithms have been compared.In this study, Bitcoin price data were collected and examined over a 13-year period, from July 13, 2010, to December 6, 2023. This dataset includes 4,895 samples and six variables. The variables include the opening price of Bitcoin, the highest price, the lowest price, trading volume, and market value as inputs, with the final price of Bitcoin considered as the output.

This research is designed as an applied study, aiming to create a framework for predicting Bitcoin prices over short-term (10-day) and long-term (30-day) periods using the machine learning method known as artificial neural networks, optimized with the six metaheuristic algorithms: PO, HBO, SPO, GPC, FHO, and FOX.The steps of the work are as follows: Initially, in MATLAB software, the necessary data for modeling is preprocessed, and missing or unavailable values are removed. Then, to reduce the impact of data scaling on the results, data normalization is performed within the range of 0 to 1, as entering raw data can decrease the model's speed and accuracy. Subsequently, 80 percent of the initial Bitcoin price data is divided into training data, while the remaining 20 percent is allocated as test data.Afterward, using the training dataset, the artificial neural network (ANN) is trained with the mentioned optimization algorithms. In the next stage, the optimized neural network models are tested using the test dataset. Finally, the results and accuracy of various models in predicting Bitcoin prices in both training and testing scenarios are compared. Evaluation criteria include the coefficient of determination (R²), root mean square error (RMSE), mean square error (MSE), mean absolute error (MAE), relative square error (RSE), and explained variance (EVS). All modeling steps are conducted in MATLAB R2023b.

The results of this research indicate that in the short-term (10-day) period, the neural network optimized with the GPC algorithm achieved a coefficient of determination of 0.88, and in the long-term (30-day) period, this network again performed best compared to other algorithms with a coefficient of determination of 0.7.

Macroeconomic and welfare effects of financial policy, tax incentives in the production cycle (Mixed data pattern method with different frequency (MIDAS))Abstract:In this study, the macro-economic and welfare effects of financial policy, tax incentive in the production cycle were investigated using the combined data pattern method with different frequency (MIDAS) for different seasonal and annual time frames. In the estimated model, the annual data of the effective tax rate, the combined index of economic prosperity, current and construction expenditures of the government, and the seasonal data of the oil price and exchange rate, the volume of liquidity and the production index for the years 1370-1400 have been used. according to the model estimation results; Uncertainty of current and construction expenditures of the government, volume of liquidity, exchange rate fluctuations and crude oil price fluctuations have a positive effect and increasing economic security has a negative effect on the production gap. Fluctuations in exchange rates lead to an increase in the amount of external debt of manufacturing companies, and an increase in debt will lead to a lack of liquidity in general, which in general, lack of liquidity of economic enterprises has a negative effect on the production cycle. In general, it can be said that with the increase in volatility in Iran's economic situation, uncertainty in macro-financial and currency policies, oil price fluctuations and many other factors, the volatility in the Iranian market has increased and these factors are the reason for the decrease in production.Keywords: fiscal policy, tax incentive, production cycle, mixed data with different frequency.

Monetary policy is a tool through which central banks manage the supply of money to achieve their goals, and it is one of the key elements of macroeconomic management, and its effectiveness is an important issue in the analysis of economic policies. The usual goals of monetary policy are to achieve or maintain full employment, to achieve or maintain a high rate of economic growth, and to stabilize prices. For this purpose, in this research, the effects of this policy on production and employment, which have been calibrated for the Iranian economy by considering frictional unemployment in the form of a monetary general equilibrium model, have been investigated. The results show that the effect of monetary growth on employment and production in a steady state may be non-linear depending on the two parameters of labor supply elasticity and elasticity of vacancies in job matches, so that the lower the labor supply elasticity and elasticity of vacancies in job matches, it is more likely that monetary growth will have adverse effects on employment and production. Also, in the framework of the model, the negative relationship between the unemployment rate and vacancies is calculated, and with the increase of the Quarterly monetary growth rate from zero to 5%, the employment and production values in a steady state, they increase by about 0.4%. In addition, considering the transition dynamic, with the reduction of the monetary growth rate from the level of the benchmark case to the level of 2.5%, the equilibrium values of the variables will converge to their new equilibrium values after 2.5 years. Therefore, it is suggested that the central bank should pay attention to the effectiveness of monetary policy on production and employment in order to achieve macroeconomic goals in recession and boom. And since monetary growth is not always unfavorable for production and employment, the effects of this reduction on production should be taken into account in inflation reduction policies.

Using the Markov-Switching Approach, this study analyzes the impacts of the governance index and monetary and financial policies on economic well-being in Iran's recession and boom regimes from 1996 to 2022. The findings reveal that during the boom period, each unit increase in economic well-being, regulation quality, financial stabilization, effective tax rate, and oil price reduces the production gap by 0.04, 0.02, 0.03, 0.03, and 0.14 units, respectively. Conversely, uncertainty in government current expenditures, government construction expenditures, and liquidity during the recession increases the production gap by 0.12, 0.13, and 0.14 units. Keynesian economists emphasize that regulating financial policies, such as tax and spending adjustments, fosters economic prosperity. In contrast, the classical school views taxes as a neutral financial lever. When government budget deficits result from increased current expenditures, which primarily boost aggregate demand, inflation may occur. However, when deficits arise from active fiscal policies aimed at overcoming a recession, the government’s investment spending and expansionary financial policies can stimulate production and drive the economy toward long-term growth.

In this paper, we investigate how novel approaches can improve the predictions made by traditional econometric approaches in the field of forecasting. Autoregressive integrated moving average (ARIMA) is known as one of the most widely used methods for predicting economic and financial time series, providing a good framework, especially for short-term linear predictions of time series. However, the assumption of nonlinear effects in time series and the emergence of novel deep learning algorithms, which can extract complex features of time series and model them, have motivated researchers to examine the predictive power of traditional and novel modeling approaches. In this study, two methods are examined for predicting the prices of the four most valuable cryptocurrencies. ARIMA and three approaches in the field of deep learning, including (RNN, LSTM, and GRU), are investigated. In addition, a hybrid model of deep learning and ARIMA has been introduced, which is a combination of the strengths of both models to increase the accuracy of predictions. The results show that the hybrid models perform better in predicting future time series than each of the ARIMA and deep learning models separately. Also, the ARIMA-GRU model has fewer prediction error values than all estimated models.

ne of the important issues that have been paid attention to in recent decades is the issue of employment. Different societies are looking for ways to promote employment and make better use of human resources available to them. In doing so, new technologies play an important role both in creating or eliminating employment and in the process of sustainable development in the agricultural sector in countries. In recent years, the application of information and communication technology (ICT) has exerted effects on various aspects of human life and brought about tremendous changes in the economic and social dimension. ICT has extensive effects on the type of work and employment structure, which has brought many changes and developments to employment at all levels. This technology has also affected the structure of jobs and has changed the nature of jobs in societies by reducing the average difficulty of work, making jobs more intellectual, unstable and specialized. In this research, in order to investigate the effects of information and communication technology on the employment and sustainable development of Iran's agricultural sector in the period of 1370 to 1400, among the variables of agricultural sector employees, the production value of the agricultural sector, the capital stock of the agricultural sector, and the share of ICT costs in the gross domestic product of the agricultural sector, under the labor demand function, have been used and the coefficients have been estimated using the soft transition regression model approach. The estimation results of the model indicate that with the increase and development of information and communication technology indicators, employment in the agricultural sector has improved.

The objective of this paper is to examine the welfare cost of inflation in the context of Iran's economy, incorporating labor market frictions (search theory) within a monetary general equilibrium model. The model also features a cash-in-advance constraint. To achieve this goal, through the utilization of calibration and sensitivity analysis in the steady state, the findings indicate that both employment and production are contingent on the elasticity of labor supply and the elasticity of vacancies in job matches. Depending on these two parameters, they may exhibit an increase in response to an escalation in the inflation rate. Additionally, the three-month optimal inflation rate in a stable state is determined to be 4.66%. The welfare gains, derived from reducing the three-month inflation rate from its current level of 5.3% to the optimal rate in a steady state, is calculated to be 0.0031% of total consumption. Therefore, based on the results, it is suggested that in inflation reduction policies, the effects of this reduction on welfare are taken into consideration, and special attention should be paid to the area of business investment by giving tax incentives, especially tax credits.

Four major approaches to financial analysis can be defined. Traditional, Ultra -Traditional, modern and postmodern approach. In the novel theory of business resilience system, in measuring financial risks or otherwise, conditions are provided that by comprehensiveness and also considering the rule of contingency paradigm, the possibility of measuring financial indicators and other cases on a case-by-case basis. Or comprehensive, be provided. Or comprehensive, be provided. Statistical data of listed companies in the period 1390 to 1398 were used. In this study, using different methods based on innovative financial instruments, the power of these instruments in estimating financial distress, traditional, ultra -Traditional, modern and postmodern perspectives are compared. Finally, it was found that the financial engineering method, relying on the theory of business resilience system in determining financial distress, reduces the fluctuation of the estimate and increases the accuracy of the estimate of financial distress. Of course, along with new methods, traditional methods should always be considered as indicators to determine the extent of financial distress and for comparison.

Bank controllers and regulators have always focused on bank capital and capital adequacy rate as two important factors for sustaining and improving banking activities. In this research, we have tried to investigate the role of capital and the relations between the banking sector with the economic real variables. We also sought to study the impact of bank capital on the real sector of an economy, using the DSGE model. Accordingly, in this model, to comply with the facts of Iran's economy, we defined the capital adequacy rate based on Basel I and II standards and took into account the probability of default for the loans and facilities of the banks and firms endogenously. Meantime, since the banking sector plays a crucial role in the country’s economy, we tried to examine the relations between the banks and Iran Central Bank (CBI) as the regulator within Iran's economic ecosystem.

To examine the actions and reactions of key dynamics of the Iranian banking sector with the economic real variables, we used Dynamic Stochastics General Equilibrium (DSGE) model, by using quarterly modified data during 1996-2019.

The results of simulating the model in response to the bank capital increase and oil positive shock on the defined variables proved the designed model to be highly consistent with the theoretical expectations and the past experiences of Iran's economy. Furthermore, it was found that due to the increase in oil price and the positive balance of payment because of the increase in the oil income, bank loans could be increased. Therefore, by improving economic variables temporarily, oil positive shock would result in a decrease in the probability of default in banks, whilst increasing their profits. The results also indicated that the bank capital can be considered a key factor for evaluating real variables in Iran's economy by the CBI, so the increase in bank income would temporarily end in a decrease in GDP, because of different factors, including the cost of bank finance and the loan rate.

It can be concluded that capital plays an important role in the bank transition mechanism to society and the Central Bank can also play an important role in the process through applying the needed changes in the capital adequacy rate. Nevertheless, as the increase in the capital adequacy rate in the long term would decrease risk and improve banking activities, it would affect real variables during the transition period. Furthermore, any increase in liquidity and bank loans would decrease the real interest rates, affecting the economic variables positively. In addition, the effects of any increase in oil income can be shown in bank profit. Therefore, oil shock changes can be generalized to other economic real variables.

This article examines the non-linear effect of inflation on the economic growth of Iran, Algeria, Saudi Arabia and Nigeria. So far, there have been many studies in the field of economic growth, in which different estimation methods have been used. The dependent variable of this research is GDP per capita as an indicator of economic growth, and the variables of inflation rate, investment, labor force, income from oil export and government expenditure have been used as explanatory variables. In the current study, using the non-linear distributional autoregressive model (NARDL) with the bound approach, the effect of inflation rate on the economic growth of the countries under investigation has been investigated asymmetrically. In this project, the positive changes in the inflation rate are shown with the POS variable and the negative changes in the inflation rate are shown with the NEG variable. The research period is 1990 to 2019 and the data are annualized. The results of Curran's test indicate the existence of a long-term relationship between research variables. The results in the case of Iran show that the variable coefficient of NEG is negative and significant, and the variable coefficient of POS is meaningless. In the case of Algeria, both coefficients of NEG and POS variables are negative and significant. For Nigeria, POS variable coefficient is negative and significant, but NEG variable coefficient is meaningless. Therefore, for all three countries of Iran, Algeria and Nigeria, the effect of inflation rate on growth is negative and due to the different coefficients of NEG and POS variables, this effect is non-linear. In the case of Saudi Arabia, both coefficients of POS and NEG variables are meaningless.

The soundness and validity of banks are one of the important subjects that neglecting them could have been leaded to adverse consequences for every countrychr('39')s economy. Therefore, investigation of relationship between efficiency and camel composite as a measurement of banking soundness and validity, within 16 Iranian bank data, between the years 1389 to 1396, has been studied. Efficiency has been estimated by the Translog cost function and stochastic frontier analysis, which its result shows an aggregate ascending trend. Camel composite which its calculation depends on arithmetic mean, is computed by exploratory factor analysis; exploring two factors, which is called financial ability and financial performance, is the result. For studding of variables relation, a panel data model and an auto regressive panel vector model have been used. The main result shows a positive financial ability and financial performance and significant relationship with efficiency; another result shows one side causality between financial performance and efficiency.

Investigating banks’ status has always been a concern to its management and regulatory decisions, which can be carried out by means of income diversification and CAMEL composite. The importance of income diversification is to decline the operation risk. The soundness of banking is also another essential component in the banking industry that can be evaluated by CAMEL. This article’s two primary goals are computing CAMEL composite index and investigating the relationship between income diversification and banking soundness, using CAMEL composite index within the financial statements of 16 Iranian banks between the years 2010 to 2017. This study applied an exploratory factor analysis (EFA) to compute the CAMEL composite calculated by arithmetic mean in the previous studies. In addition, the Herfindahl-Hirschman index (HHI) has been used to calculate income diversification. The generalized method of moments (GMM) panel model has been utilized to study the relationship between variables. The result of EFA confirms two factors: financial ability and financial performance. The result of GMM panel model shows a positive and significant relationship between financial ability and financial performance on income diversification.

The purpose of this study is to study the impact of information and communication technology on rural employment in the central part of Aliabad Katoul city. For this purpose, the threshold approach (STAR) and annual data for the years 1399-1385 were used. In the present age, this technology has many effects on all aspects of human life. One of the most important effects of ICT is its impact on the economy and especially rural employment. Considering the development of ICT in the country's villages since the beginning of the 1980s, in this study, 30 villages from the central part of Aliabad Katoul have been selected as a sample. The results of model estimation show that the variables of ICT costs share of household costs and Internet penetration coefficient in both regimes have a positive and significant effect on employment. According to the research results, it is suggested that the government, by holding training courses to explain the skills of using this technology to the villagers in order to create jobs and income, seems necessary.

Based on studies conducted at the international, regional and national levels, this study identifies the factors affecting the productivity growth of all factors of production. Since the growth of productivity of all factors of production provides arrangements that ultimately increase the well-being of individuals in a society, such a study can be of great importance in advancing the convergence literature. To achieve this goal, a review of studies conducted in recent decades, it is necessary to distinguish between two categories of medium-term and long-term factors. Long-term variables are commonly known as the fundamental factors of growth in the economic literature and include such things as institutions, geography and trade. Mark similar items. Although this study is more of an overview of the medium-term factors affecting the productivity growth of total factors of production, but due to the complexity between these two categories of factors, the study of both is somewhat inevitable.

Solvency is one of the essential components that illustrates financial status of an insurance company. Therefore, providing a model with considering internal and external factors which could predict decreasing solvency can improve the efficiency of this index. The main purpose of this paper is to present a model for an early warning system based on decreasing probability of solvency from its critical amount in point of supervisory authority view in Iranian insurers. To this end, based on previous studies, a number of indexes, as independent variables affecting solvency, are selected and classified as economic, financial, and corporate governance. In this respect, the empirical model of research was estimated by econometric method of logit panel data for 18 Iranian companies during 1387-1396. The results depict that interest rates and the change in board of directors have the most and the least impact on the probability of falling insurers’ solvency, respectively. Also, the impact of loss ratio differs in various quantities. Moreover, the impact of variables on probability of decreasing of solvency of insurers are verified; including macroeconomic; interest rate, economic growth and international economic sanctions, Financial variables; current ratio, loss ratio, the Herfindahl–Hirschman Index, and change in board of directors.

Financial solvency is one of the essential components that illustrates financial status of a financial enterprise, at large, or an insurance company to be specific. In addition to solvency ratio, other metrics and indices are also early warning indicators of an upcoming crisis in insurance industry. Therefore, the main purpose of the present paper is to provide a model for an early warning system of solvency for insurance companies and Iranian insurance companies in particular. To this end, a number of indexes and ratios, as independent variables affecting solvency, are selected and classified as economic, corporate, and corporate governance, that distinct this article from other studies. In this respect, the empirical model of research was estimated by econometric method of panel data for 18 Iranian companies during 1387-1396. The results of the research depict that interest rates with one-period delay and the change in board of directors have the most and the least impact on Iranian insurers’ solvency, respectively. Also, due to its cube strength, the impact of loss ratio differs in various quantities. Moreover, all hypotheses that are based on meaningful impact of variables on financial solvency of Iranian insurers are verified; including macroeconomic (inflation rate with one delay), interest rate (with one delay), economic growth (with one delay), corporate variables (ratio of investments in risky assets to all assets), loss ratio, the Herfindahl–Hirschman Index, and corporate governance (percentage of major shareholder’s ownership and change in board of directors) along with international economic sanctions.

In this paper, the effect of government debt crowd out on private sector investment in the Iranian economy is investigated, using the New keynesian model in a stochastic dynamic equilibrium model (DSGE) and the Bayesian estimation solution method during the years 1997-2017 .Financing government resources is important, especially in recent years, when government issues bonds and it is necessary to apply shocks and examine the effects of government policy. In this regard, several policy shocks have been applied to the system and the response between fiscal and monetary policies has been examined. The results show that direct government policy interventions are made, the effect of crowd out government debt on private investment is greater than in conditions where there is a market system or at least less government policy.Contrary to the conventional view, no systematic relationships between real interest rates and  private investment.

This paper aimed to create a composite index for environmental quality. Since the results of empirical studies on economic - environmental variables nexus, considering different environmental indicators, are not consistent with each other; it seems necessary to use a comprehensive index that includes all dimensions of environmental pollution.

Using 6 environmental indicators related to two groups of selected OPEC and OECD countries for the period 2010 to 2019 and using three methods including principal component analysis, kernel-based principal component analysis, fuzzy robust principal component analysis, the creation of a composite environmental quality index is examined.

The results showed that FRPCA method has more efficiency and ability in weighting environmental indicators than other methods due to having the lowest error criteria. Therefore, using this method, the composite index was calculated. Moreover, the results showed that along with the upward trend of economic growth, the quality of the environment follows a downward trend in OPEC countries and an upward trend in OECD countries.

 Based on the results, policy recommendations as well as new perspectives and suggestions for future studies are presented.

This paper, in terms of government financing bonds as a distinguishing feature of this study from other studies, seeks to examine the impulsive effects of government investment policy on private investment and its interaction with these bonds in a dynamic general equilibrium model. It is a coincidence with the Bayesian solution method in the 70s and 90s.Also, This article seeks to compare the effects of this policy by eliminating tax shocks. The results show that in the event that the government issues bonds to finance itself, initially public and private investment have crowd out effect and with the completion of government development projects and the preparation of infrastructure by the government, private investment will increase. Also, value added and price levels increase. Also, these results may not lead to such results if tax policies are not implemented. Due to government intervention and government investment, and thus the formation of the total investment by government, it is obviously, with this intervention, there is no traditional relationship between interest rates and private investment and the other hand, this traditional link, between interest rates and investment has been dispelled by domestic resource theorists. In particular, as Shaw and Mckinnon show, this relationship is eliminated in developing countries, although the lack of this relationship is also observed in other developed countries. Also, the results of the present study confirm these economic theories about the Iranian economy. And the other hand, after presenting the conclusions and recommendations of the model, other offers are also provided to public financing

The purpose of this study is to investigate the impact of information and communication technology on employment in Iran. For this purpose, the threshold approach (STAR) was used based on annual data during the years 1370-1397. The results of model estimation show; The variables of GDP per capita, Internet penetration rate, physical capital, human capital and government spending on education, research and development have a positive impact on employment in Iran, given that the country's political and social security is closely related to economic security, which of course employment Sustainable is one of the manifestations of economic security, so it can be concluded that the development of ICT has a positive effect on the country's security. Also, the variable of information and communication technology in both regimes did not show the effect on employment in Iran, which according to the theoretical foundations of the subject, technology in addition to job creation, has also been employment-creating. Therefore, due to the small budget for research and development in GDP, it is suggested that more attention be paid to the quality of human resources in the country to train more skilled, experienced and capable workforce and work in different parts of the country. Also, holding formal training courses, increasing the quality level and increasing the relationship between training and specialization of courses with the needs of production and service sectors of society and allocating a fixed amount of resources to companies for research and development, increases the inventory of science and knowledge at a constant rate. The government pays more attention to this issue in the country.

Oil price shocks have an undesirable effect on financial stability and banking systems, in addition to creating uncertainty and negative effects on the macroeconomic performance of oil-exporting countries. In fact, the dependency of government spending policies on oil price movements in oil exporting countries creates feedback loops between asset prices and bank credits that could lead to an increase in vulnerability of the financial sector. Therefore, considering the importance of the issue, this study aims to investigate the asymmetric effects of oil prices on non-performing loans (NPLs), as credit risk criteria, by applying data from 18 selected banks in Iran during 2006-2017. In this regard, the relationship between variables has been estimated using Panel Nonlinear Autoregressive Distributed Lag (PANEL NARDL). The predictability of symmetric and asymmetric PANEL ARDL models is assessed by applying RMSE and Campbell and Thompson (2008) tests. The results show that the asymmetric model has better performance and efficiency than the symmetric model. These asymmetric effects are significant in both short-term and long-term. Based on the results, the impact of oil price on the NPLs of some banks is positive and in the others is negative and significant.

Transportation activities and related services can be considered as a link between all sectors and economic activities, and it is considered as one of the factors influencing the development of the economy. Considering the position of the transportation sector in the economy, the purpose of this study is to investigate the short and long-term causality relationship between transport variables and economic growth in Iran's provinces. In this regard, the variables of the volume of freight transported through the air transport fleet and the number of its passengers are considered as representative of the transportation sector. For this purpose, using panel data during the years 2005-2017 and cointegration approaches, Dynamic Least Squares (DOLS), Vector Error Correction Model (VECM), and Granger causality method, examined the long-term and short-run causality relationships among the above variables. The results of this study show that in the short run, the Granger causality relationship is not significant between these variables, but according to the output of the DOLS model, in the long run, the positive effect of the number of passengers on economic growth is greater than the volume of load. In this study, the error correction component, which indicates the speed of adjusting the short run to long-term equilibrium, has been evaluated. Estimates show that there is a one-way Granger causality relation between the volume of displaced load and economic growth. Also, this relationship for the number of passengers and economic growth variables is one-way from passenger to economic growth.

Nowadays, the role of government interference in the economy is accepted in various economic theories. Governments can adopt monetary and financial policies to influence economic fluctuation and growth.. The extent of this role depends on the structure of the economy and the economic institutions of the society. In many macroeconomic texts and business cycles, fiscal policy has been introduced as part of the process of economic stabilization. The automatic changes in government revenues and expenditures help to respond to production fluctuations to pave the way through the multiplying factor of traditional demand. In this regard, this research seeks to investigate the effect of government financial policies on economic activities in the Iranian economy through different phases of a business cycle using nonlinear patterns in the period of 1988-2016. Also, financial policies and production, the relationship between the two in different economic situations, and the business cycle of recession and prosperity are analyzed. In this regard, using the threshold regression model, the effect of financial policies is studied using the introduced variables, the corresponding tests were performed, and the ESTAR model is selected. Based on this model, the asymmetry of the impact of financial policies on expenditure and taxes is accepted. The findings of this study have important implications for combining optimal financial policies throughout the various stages of a business cycle.

The transportation industry of any country represents the economic situation and the extent of its industrial development, so it can be considered as one of the important factors in the economic, cultural and social development, etc. of each region. Therefore, the growth of economic activities in Iran and the need for faster transportation of people and goods enables the country to serve as the main transport hub for export, import and transit. Reviewing the role of this type of Transportation is important in economic growth and can be effective in identifying opportunities and challenges, and ultimately providing a better view for policymakers and operators in this field. Accordingly, the purpose of this study is to investigate the role of air transportation (number of passengers and the volume of freight transported) on economic value added in the provinces of the country (31 provinces) between 2005 and 2017. The results of this study, using the dynamic panel econometric approach (Generalized Method of Moments or GMM), show the positive and significant effects of variables on the number of air transport passengers and education, as well as the negative and significant effects of unemployment and the volume of freight transported by air transportation on the economic value added of the provinces of Iran. According to the results, it can be argued that air transportation of passenger in the country's situation during the period of this study can be the factor for growing economic value-added, while, the freight transportation in terms of the costs has not economic efficiency. Furthermore, the results illustrate that establishing international airports in the province has a positive effect on economic value-added and also the value-added of the previous period has positive and significant effects on current economic value-added which represent dynamic of growth.