Introduction: One of the important aspects of foreign exchange policy is the Central Bank intervention in the foreign exchange market. The Central Bank intervenes to stabilize the foreign exchange market by changing its foreign reserves. Governments prefer to keep the exchange rate stable because any sudden fluctuation can destroy the confidence of economic actors in the market and harm the financial market and the market for physical goods. In this regard, an important method of analyzing the behavior of Central Bank officials to control and manage the foreign exchange market is to estimate the reaction function and identify the factors affecting foreign exchange interventions of the bank. Knowing about the factors affecting foreign exchange interventions helps to forecast the next interventions of the Central Bank and gives economic actors a better understanding of the behavior and decisions of policymakers and their effects on financial markets and macroeconomic variables. Methodology: In this study, a Mixed frequency Data Sampling Model (MIDAS) has been used to investigate the factors affecting the foreign exchange interventions of the Central Bank. This method deals with the high-frequency variables as independent variables next to the low-frequency dependent variable. The frequency of the dependent variable must always be less than the frequency of the independent variable(s). Therefore, the Midas approach can use the maximum amount of information gained from high-frequency series. Better prediction is also made for the dependent variable. In this research, the Data related to the years 2002 to 2018 were used to estimate the reaction function of foreign exchange interventions by the Central Bank. This was done with the variables of direct foreign exchange interventions (annual), the level of foreign exchange reserves (seasonal), oil and gas exports (seasonal), and exchange rate fluctuations (seasonal). Results and Discussion: As the results showed, among the studied variables, the rate of policy-making intervention was more sensitive to the amount of export. Also, with an increase in oil exports, the Central Bank's intervention in the foreign exchange market increases. In the period under review, the central bank interventions were mostly leaning-against–the-wind. In addition, the three methods of weighting Almon, Beta, and Exponential Almon were used to compare the performance of the variables to predict foreign exchange interventions of the Central Bank. In order to compare the performance of individual forecasts and the combination of forecasts, the mean squared error rank method was used. As the results showed, the combination of forecasts did not provide better performance than individual forecasts. Therefore, the individual estimates of foreign exchange interventions are highly valid. Conclusion: Through examining the reaction function of the Central Bank of Iran interventions, one can observe the irregular behavior of monetary authorities in the face of different market conditions. The interventions of the Central Bank in Iran obey no specific rules, and the foreign exchange policymakers intervene in the foreign exchange market in a completely discretionary manner, without considering a specific pattern for intervention. Although this form of intervention in periods of abundant foreign exchange earnings has been able to stabilize the exchange rate to some extent, with a decline in the foreign exchange earnings and foreign reserves, the power of the monetary authorities to manage the exchange market has diminished and the exchange rate has jumped to adapt to the realities of the economy. Therefore, it seems necessary for the Central Bank to regulate the method of foreign exchange interventions. The necessary condition is to synchronize the growth of liquidity in Iran with global liquidity and prevent its incompatible growth with the principles of the domestic economy. In fact, one of the most important prerequisites for successful foreign exchange market management is to control the unbridled growth of liquidity. In this case, we can hope for the success and effectiveness of foreign exchange interventions. The management of foreign exchange resources during periods of boom and bust of oil revenues is of particular importance too. The government’s injection of surplus foreign exchange earnings into the foreign exchange reserves will enable the Central Bank to use the resources accumulated in the accounts to manage the foreign exchange market in the event of oil shocks and foreign exchange earnings decline. This policy will help to prevent exchange rate fluctuations due to oil fluctuations. Derivatives can also be used as an alternative to cash market intervention. The advantage of derivatives is that their use does not necessarily change the country's foreign exchange reserves, nor does it overshadow the monetary policies. Therefore, the bank can change the exchange rate or prevent fluctuations by issuing derivatives without affecting the monetary basis.

predicting the amount of country imports toward assessing trade balance and its effect on the balance of payments (BOP) and finally money supply, general level of prices and the rate of economic growth is of paramount importance. Therefore, economic policymakers seriously need a model which cannot only predict the volume of imports well but also be capable of revising the initial prediction over time as soon as new Data for the explanatory variables are available. To this purpose, Mixed frequency Data Sampling model was used which allows time series variables with different annual, seasonal and even daily frequencies to be used in a single regression model. In estimating the model using the software R, annual real imports, real exports and quarterly of real GDP, real exchange rate and the volatilities of the real exchange rate in the range of 1988 to 2014 are used. Information related to 2014 is not used in preliminary estimation of relationship, so that the predictive power of the model outside of the estimated range can be tested. The proposed model predicts that real imports of goods as49948 million dollars for 2014 which is associated with an error of only41 million dollars, or about 8 percent, compared to its real amount achieved of49907 million dollars. The result suggests that the predictive power of the MIDAS model is very satisfactory.

Today, forecasting of economic and commercial variables as an important scientific field is developing, and forecasting of macroeconomic variables is of special importance for planners, policy makers and economic enterprises. The agricultural sector, as a producer of strategic products and provider of food for the growing population, has a great influence on economic, social and political decisions. Considering the importance of the agricultural sector in Iran as well as the existence of different and uncontrollable influential factors, the researchers who focus on agricultural sector’ growth, try to use methods of forecasting in order to get results close to reality, reduce the prediction errors, and design policies and plans to improve the place of this sector. In this paper, the Mixed frequency Data-Sampling model (MIDAS) has been used to predict the growth of agricultural sector’ value added. Comparison of the model predictions with actual Data indicates the predictive power of the model. This model has predicted the growth rate of agricultural sector's value added over the period 2017-2021 by 3. 215%, 2. 53%, 2. 92%, 5. 29%, and 5. 99%, respectively.

Introduction: In recent decades, the increase in the financial sector, compared to the real sector of the economy, and the rising inflation have made individuals and firms invest part of their resources in the financial markets to earn money and maintain their purchasing power. Therefore, the financial sector has expanded significantly compared to the real sector and beset Iran's economy with the financialization phenomenon. Financialization is a process in which the increase in the income of non-financial firms due to their activities in the financial sector and the rise in the participation of individuals in financial activities motivated by profit leads to a significant expansion of the financial sector. The rising returns of the financial sector increase the attractiveness of investment and, hence, the financial sector income. The widening income gap between the financial and real sectors of the economy worsens the income distribution. Moreover, if investment in the financial sector leads to a reduction in investment in productive activities, economic growth in the real sector will decline. Therefore, there is a close relationship between financial sector performance, economic growth, and income inequality in the financialization process. Because the total savings is affected by economic growth and income inequality (Schmidt-Hubble and Seron, 2000), financialization affects the society's savings through economic growth and income inequality. The impact of economic growth is positive on savings, however, the influence of income inequality on savings varies according to the economic structure, income level, and financial market conditions of each country (Bofinger and Scheuermeyer, 2018). Consequently, the final impact of financialization on savings is ambiguous and depends on the net effect of economic growth and income inequality on savings. Therefore, understanding the impact of financialization on national savings allows policymakers to improve the national savings by adopting appropriate strategies to address the deficiencies of financial markets. Methodology: To analyze the relationships among the variables, following the work of Schmidt-Hubble and Seron (2000) and Gangor et al. (2014), national savings are considered as a function of the logarithm of financialization, logarithm of GDP, income inequality, real interest rate, and dependency burden logarithm. The ratio of the financial sector value-added to GDP is used to measure financialization following Van Arnum and Naples (2013). To convert the nominal Data into real Data, the consumer price index is used for the base year of 2004. The source of Data is the Statistics Center and the Central Bank of Iran and includes the Data of 1988-2020. In this study, the variables of GDP and financialization have a seasonal frequency, and the other variables have an annual frequency. Hence, the MIDAS approach is applied to examine the effect of financialization on national savings. Using the MIDAS approach, it is possible to estimate the coefficients of the variables with different frequencies (Noferesti et al., 2018). Results and Discussion: The findings showed that the logarithm of financialization has a negative and significant effect on national savings in Iran. In other words, the expansion of the financial sector has led to a reduction in national savings. Also, the logarithm of GDP has a positive and significant effect on the national savings logarithm. Economic growth is considered a principal factor affecting savings. Increasing economic growth leads to an increase in national savings by raising the income of economic factors. The Gini coefficient leaves a negative and significant impact on the national savings logarithm. A rise in income inequality means an increase and a decline in the income of the rich and the poor, respectively. As income falls, people prefer to reduce their savings and maintain their current level of consumption. Since the number of the poor is more than the rich, in a country, the final effect of reducing the poor individuals' savings is greater than the effect of raising the rich individuals' savings. The real interest rate has a positive and significant impact on the national savings logarithm. With interest rates rising due to the enhanced attraction of investment, most of the income is allocated to saving. Finally, the burden of dependency has a negative and significant effect on the national savings logarithm. Increasing the dependency burden reduces the national savings due to increased consumption expenditures. Conclusion: In Iran, the economic problems, rising inflation, and the expansion of the recession led investors and firms to increasingly invest their capital in financial markets to keep the power purchase and make a profit. Thus, the financial sector grew and expanded significantly compared to the real sector. The inefficiency of the financial system in the country led to the diversion of resources from productive activities to unproductive inflationary activities, despite the increase in firms and individual participation in financial markets. Therefore, along with the demand reduction of domestic products, financialization caused a decrease in the real sector production of the country. Firms of the real sector were forced to invest part of their capital in the financial section to prevent bankruptcy due to declining production and maintain the value of their capital in the face of inflation. Also firms reduced the production costs by decreasing wages and employment. Thus, a decline in the number of workers and economic growth with rising inflation resulted in a reduction in the real income of individuals and firms. As a result, the progress and expansion of financialization brings about a reduction in economic growth and national savings.

Population age structure is a main factor affecting government consumption expenditure. This paper examines the effects of changes in population age structure on government consumption expenditure by using a Mixed frequency Data Sampling (MIDAS) approach. The estimation results indicate that population age structure are of positive and significant effects on government consumption expenditure. In addition, government consumption expenditure is forecasted for 2014. To assess the predictive power of the model, the actual Data in 2014 was not used. The expenditure forecasted by the model is 1437079 billion Rials, and corresponding real value is 1438316 billion Rials. This indicates the goodness of fit of model.

The aim of this thesis is to investigate the effect of changes in population age structure on government tax revenues and forecast its evolution using MIDAS method and time series Data during the years 1367 until 1393.Changes in population age structure caused by the sharp rise in fertility in early 1360, has brought many consequences and questions. One of the questions is how changes in population age structure will affect the government's tax revenues. This paper tries to answer this question. For this purpose, by the theoretical foundations of the economy, we will specify a function for government tax revenues, where changes in population age structure is one of the explanatory variables. In this paper, by using of the method described by Ghysels, Santa-Clara and Valkanov in 2004, we estimated this function and anticipated government tax revenues.This study is done by using MIDAS method in order to estimate the specified for government tax revenues by the aid of R software. MIDAS method allows variables with different frequencis, i.e., seasonal, monthly or weekly, put together this in one equation and it is possible to revise the forecasted value for the dependent low frequency variable as soon as new high frequency Data are released. Hence the publiction of seasonal Data for the variables considered, sach as government total revenue and gdp at the beginning of the year, will make it possible to forecast the government tax revenues. This forecast will help the policy makers to see is the budget will face some unbalances, relevant policy action be token from just the beginning of the year. The statistical Data used in this study are time series, seasonal, which is used to collect them from the Database of time series of the central bank, economic indicators and the statistical center. Variables used: Government tax revenues in the form an annual, gross domestic product and total imports in the form season, age structure of the population in the form an annual. Before estimating the coefficients of the model, the reliability of the variables has been investigated. The results show that in the equation specified, the effect of seasonal GDP and total imports, annual age structure (the ratio of population aged 35 to 64 to the total population) on government tax revenues are statistically meaningful. Given the positive impact of the age structure of the population aged 35-64 to the total population, it can be said that, according to Ando Modigliani's theory, since this age group has higher income, they pay more taxes and therefore have a positive impact on government tax revenues. To estimate this function, we used the relevant Data in the period the first quarter of 1367 to the fourth quarter of 1392. Next, we forecast government tax revenues for 1393. To assess the predictive power of the model in outside of the estimation range, we did not used the Data of 1393 in initial expressed relations estimation. The government's tax predicted revenues forecasted by the model is 709, 365.7 b.Rials and compered to its real Data which is 709, 651.9 b. Rials, indicate that the model forecast is satisfactory. As can be seen, entering the Data the fourth chapter of the seasonal variables used in the relationship, the prediction value is very close to the real value. Also, the coefficient of determination of the pattern is estimated at 0/9954, which indicates the high explanatory power of the model. The quantity of the test statistic is 0.77, which indicates that adverbs applied are statistically significant and sufficiently adequate. Regarding the quantity of the camera-Watson test statistic and Shapiro-Wilk' s normal test, the disturbance Sentences of the pattern, are not correlated and have normal distribution.

With the expansion of the tourism industry, many countries have been able to improve their economic situation and resolve their economic problems, such as low per capita income, high unemployment and lack of foreign exchange earnings. Since tourism creates a very good source of income for a country, it can be considered as an industry. So, this results in a very broad concept in various economic, social and cultural dimensions. Therefore, this study examines the effect of exchange rate volatility on tourism flows in Iran based on seasonal and annual Data during 1368-1394. For this, first, the real exchange rate uncertainty is extracted using stochastic volatility model with leverage effects. Then, to investigate the effect of real exchange rate volatility on the flow of tourism, we will use Mixed frequency Data Sampling (MIDAS) approach. The results showed that the increase of real exchange rate volatility as well as the increase of GDP have negative and positive effects, respectively, on the arrival of foreign tourists to Iran.

The present study intends to use the Mixed Data Sampling model (MIDAS) to investigate the effect of exchange rate changes on the variable of real production in Iran during the period 1397: 1 to1380: 4 that Provides implement a flexibility model whit high descriptive power, including variables of varying frequency (eg daily, weekly and monthly) together in a regression. The results of this study show that the MIDAS model is statistically strong in identifying the asymmetric dynamic effects of the independent variable with higher freuency (exchange rate changes) on the dependent variable with lower frequency (GDP) compared to the same frequency model of these variables and shows asymmetry better. Also, based on the obtained results, the intensity of the impact of the momentum and the persistence of each momentum are different, so that the negative momentum of the exchange rate has more intense and lasting effects on Iran's GDP.

In genetic linkage analysis when a genetic trait is regressed on some factors such as polygenic values and environmental effects, one needs to estimate the model parameters and test some hypotheses. Since only the phenotypes of the individuals are observed, calculation of likelihoods involves consideration of all compatible configurations of genotypes. The number of this configurations increases as the size of the pedigree and the number of loci involved increase. One natural approach is the use of Monte Carlo methods. In this paper we use the Gibbs Sampling Method to do the calculations.