mixed frequency data sampling

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. 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). 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. 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.

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.

One of the foundations for development and economic growth is the expansion of private sector investment. It is necessary to pay attention to the relevant variables in this regard and to examine their role. Therefore, this study examines the role of macroeconomic uncertainty and public expenditure in determining private investment in Iran based on seasonal and annual data during 1368-1394. For this, first, the macroeconomic uncertainty was extracted using stochastic volatility model. Then, to investigate the effect of macroeconomic uncertainty and public expenditure on private investment, we usedMixed frequency Data Sampling (MIDAS) approach. The results showed that the increase of macroeconomic uncertainty as well as the increase of GDP have negative and positive effectson the private sector investment in Iran, respectively. Furthermore, the public consumption expenditure has a negative and significant effect on private sector investment and significant effect on private sector investment and significant effect on private sector investment

In this paper, we will examine the effect of age structure of the population on the government’s social security expenses. For this purpose, we have estimated the corresponding function by using the relevant data in the period from thefirst season of 1367 to the fourth season of 1392. The results showed that GDP, general government revenues and age structure of population have positive and significant effects on government’s social security expenses. It means that if the number of retirees increases, the government’s expenses will increase as well. The results also showed that the government’s predicted social security expenses in the spring, summer, autumn and winter of 1393 are as follows: 311903.3 billion rials, 311928.2 billion rials, 312135.5 billion rials and 312200.4 billion rials, respectively.

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.