macro variables

Today, employment is one of the biggest economic problems of most countries, especially developing countries, and it is one of the issues that economists pay attention to. Employment is one of the most important economic issues of the country, which affects other social and economic areas and makes them undergo changes. One of the areas affected by employment is ethics, which plays an important role in the social sphere of society. In this research, an attempt has been made to study and prove the relationship between the risk of moral hazard and the transparency of the central bank on employment in MENA countries.

The current research method is applied and data collection and analysis is correlational. The statistical population used in this research includes macro variables during the years 2012 to 2022 collected from the World Bank and the corruption perception index collected from the Transparency International Organization. A simple random sampling method has been used in this field. Wavelet analysis approach has been used to measure the relationship between moral hazard risk and macro variables on employment. In this regard, MATLAB and R software was used.

Based on the estimate made based on the wave approach, it has been proven that there is a relationship between the risk of moral hazard and the transparency of the central bank on employment.

According to the estimate, it was determined that the effect of the moral hazard risk index and the transparency of the central bank on employment will be the same phase and this can lead to a decrease in the efficiency of the economic, social and political foundations of the country and ruin the society.

The dynamics of the market and the stochastic behavior of index time series worldwide have made forecasting trends a primary concern for researchers and investors in the capital market. Therefore, various models are used for forecasting. Time series analysis and prediction methods based on networks, including the visibility graph model, have proven effective for time series analysis. In this research, we aimed to predict the TEDPIX index for the years 2013-2024 using three visibility graph-based methods. These methods include the most similar node model, the weighted similar node model, and our proposed method, the cross-visibility graph model. We converted the time series of the index and some auxiliary variables into a visibility graph and predicted the aforementioned time series using these three models. The results showed that the performance of the weighted similar node model and the most similar node model varied depending on different conditions. However, our proposed model, the cross-visibility graph model, and the weighted similar node model generally provided the best results in predicting the index and were more accurate compared to other models.

In recent years, the financial condition index (FCI) has been used in many countries as an important index to determine the state of macroeconomic policies. For this purpose, in the present study, the effects of financial condition index on macroeconomic variables were investigated by applying the time-varying parameter factor-augmented vector autoregressive model (TVP-FAVAR) and using quarterly data during the period (1991-2019). The results indicate that the response type and response rate of macroeconomic variables were different due to the financial condition index shock over time, and this indicates the necessity of employing the parameter - variable approach. According to the obtained results , The unemployment rate and economic growth rate variables in the short and long term showed a Negative and positive response to behavioral changes in the financial condition index variable, respectively; The effects of the financial Conditions Index shock on the inflation rate variable appear after one period; However, the response of this variable to the financial condition index shock in the short and long term has been different according to the conditions prevailing on the economy of the country . Also, the financial conditions index shock in the short run has improved the Gini coefficient variable, but in the long run, especially in the late 2010s has rised the income gap. The response of the budget deficit variable to the financial condition index shock in the whole period under review was positive and the financial condition index shock has increased the government budget deficit.

The uncertainty of macroeconomic variables imposes irreversible effects on economic phenomena. Therefore, it is necessary to avoid the effects of uncertainty on macroeconomic variables. One of these phenomena is income inequality. Hence, investigating the effect of macro variables uncertainty on income distribution requires careful modeling. In this paper, using the fuzzy regression model with symmetric and asymmetric coefficients in MATLAB and GSM program environment, we investigate the uncertainty of macroeconomic variables on Gini coefficient of income deciles in Iran for the period 1995-2007. . The results show that the Gini coefficient of the right and left widths of all income deciles except for the second and eighth deciles did not fluctuate significantly until 2011. The increase in the fluctuation of the right-income declining amplitude of the second decile indicates the inequality and the effectiveness of this decile due to the uncertainty of the large variables. The dramatic decline in the amplitude of the 8th Right Decade Width fluctuation in 2009 is due to economic developments and improved income distribution. Also, the increase in the amplitude of the right and left Gini coefficients of income deciles, especially the upper deciles in the years after 1390, indicates an increase in inequality in the income distribution in these years. As such, this inequality is more severe in the tenth income deciles, which makes the rich richer. The most common criteria for MSE, RMSE, MAPE, MAE are used to evaluate the results, which shows the excellent performance of fuzzy coefficient regression model.

Exchange rate fluctuations are of particular importance in macroeconomic issues due to their impact on other economic variables. Agriculture, oil, industry and services are the most important variables that are affected by currency fluctuations. Given that there have been several fluctuations in exchange rates in Iran over the past three decades, it is important to study the effect of exchange rate fluctuations on economic indicators. The purpose of this study is to investigate the effect of exchange rate fluctuations on Iran's economic indicators. For this purpose, using annual data during the period 1991 to 2016 and using the VAR vector error correction model, the relationships between variables were examined. Therefore, in this study, the effect of real exchange rates on macroeconomic variables of the country was investigated. The results of the study indicate that the value added of the oil sector has the greatest impact on the real exchange rate. In addition, the results of the impulses of the immediate reaction and standard deviation show that if we enter a shock from the exchange rate, the oil sector variable will see the greatest effect of this shock.

In the aftermath of the global financial crisis, many countries implemented credit easing policy in the recession period. The main objective of the paper is to investigate the effect of implementing credit policy on macroeconomic variables in Iran's economy.  We estimated the VAR model with seasonal data for the years 1384-1395. In the first place, the unit root test for the time series variables were examined and then, by analyzing the VAR model, we estimated the impulse-response variables.  The results indicate a positive and significant positive effect of credit easing by 0.001 per cent on GDP growth rate and by 0.07 per cent on the private investment rate and by 0.12 per cent on non-oil exports. These effects will reduce the unemployment rate by 0.0025 per cent. It also decreases the real exchange rate by 0.02%.

The central banks typically respond to inflationary fluctuations and production gaps by using monetary policy tools. The financial crisis in 2007 indicated that the monetary policy of central banks and pricing tools used to stabilize the economy have not been effective. Thus central banks utilized unconventional monetary policy to achieve financial stability the index of financial conditions indicates the financial climate affecting firms and households. This paper aims to identify and analyze the channel of monetary policy transmission and estimate the effect of the index of financial conditions on Iran's economic activity during the 2005-2017 period. Then, using the forward and backward distribution in the Bayesian VAR model, the impulse variables and immediate response to the financial condition indicator in the period under review are estimated. The findings of the paper indicate that financial conditions have negatively affected GDP and private sector investment, and credit growth has played a significant role in the financial condition index.

Undoubtedly discovering trends in the market and earn returns is one of the important issues from the perspective of an investor. Discovering the hidden angles, is very attractive. Abundant empirical evidence worldwide suggests that repeating patterns over time is possible. This efficient market concept and theories associated with it, is not compatible with the concept of performance based foundation of modern theories of financial market as the unpredictable behavior of the market has been long established. There are exceptions to the financial markets that show deviations from the rules of logic and rational that is in violation of efficient markets. One of these exceptions, "seasonality or calendar anomalies", is that certain patterns existing at different years, months, weeks and days will be approved. Irregularities include calendar, asymmetric distribution of returns in the days and months. Research hypotheses 1. The stock returns in December are different from the stock returns in other months in Tehran stock Exchange. 2. The stock returns in April are different from the Stock returns in other months in Tehran stock Exchange. 3. The stock returns on Saturday are different from the Stock returns on other days in Tehran stock Exchange. 4. The stock returns on Wednesday are different from the Stock returns on other days in Tehran stock Exchange. 5. There is a significant relationship between GDP variations and extraordinary return of season. 6. There is a significant relationship between inflation and extraordinary return of season. 7. There is a significant relationship between annual stock returns and extraordinary return of season. 8. There is a significant relationship between extraordinary efficiency risk (deviation of stock returns) and extraordinary return of season. To evaluate the relationship between stock returns and the months of H0 and H1 hypothesis, we used: H0: M1 = M2 H1: M1 ≠ M2 M1: The percentage of average daily stock returns in the month M2: The percentage of average daily stock returns in the remaining months To study the relationship between stock returns and days of a week and also the hypotheses H0, H1, we use: H0: D1 = D2 H1: D1 ≠ D2 D1: The percentage of average daily stock returns of the desired day [[ D2: The percentage of average daily stock returns in the rest of days a week To evaluate the relationship between changes in GDP and inflation wonderful seasonal efficiency of multiple regressions, we use the following: To evaluate the relationship between changes in GDP and inflation wonderful seasonal efficiency of multiple regressions in next year, we use the following: Result 1. There is no January effect in Iran. 2. The stock returns in December are different from the stock returns in other months in Tehran stock Exchange. 3. The highest stock returns between days of a week for Iran as for European and American countries are on Wednesday. 4. The lowest stock returns between days of a week unlike European and American countries are on Sunday. 5. The highest Stock returns in seasons belong to summer and the lowest belong to the winter. 6. The highest stock returns belongs to the first half of the year and the lowest belong to the second half of the year. 7. There is no significant relationship between GDP variations and extraordinary return of season. 8. There is no significant relationship between inflation and extraordinary return of season. 9. There is no significant relationship between annual stock returns and extraordinary return of season. 10. There is no significant relationship between extraordinary efficiency risks (Deviation of stock returns) and extraordinary return of season. Based on the results we find that Macro Variables of Economic does not effect season extraordinary return and we know some days and some months have extraordinary return that effect investors’ investing.

This study was intended to predict the success of countries at the Asian Games through macro-economic, political, social and cultural variables.
We used the information of all variables including urban population, education expenditures, age structure, GDP real growth rate, GDP per capita, unemployment rate, population, inflation average, current account balance, life expectancy at birth and merchandise trade of all the participating countries in Asian Games from 1970 to 2006 in order to build the model. This model was then tested by the information of variables in 2010. The prediction was based on the total number of medals received by in each country. In this study, we used WEKA software that is a popular suite of machine learning software written in Java.
The value of correlation coefficient between the predicted and original ranks was 77.18% according to Tree model, 90.42% according to Neural Network model and 91.35% according to K-Nearest Neighbor Algorithm. Based on the analysis, K-Nearest Neighbor Algorithm had the highest correlation coefficient among the 3 models. This model, among 28 countries which won medals in Asian games, predicted the ranks of 23 countries with the maximum difference of 3 between predicted and original ranks, 3 countries with the maximum difference of 4 to 6 between predicted and original ranks and 2 countries with the maximum difference of more than 6 between predicted and original ranks. The previous studies have mostly focused on a small number of variables. The question is why the impact of macro level factors on sport success has been decreased. The results of this study indicated that the macro level variables still have a great impact on sport success and this reduction in impact proportion is due to the replacement of variables.
This study aimed to use cultural and social variables besides political and economic variables, because in previous studies due to quantification problems, they were not included in the research design. The point is that many variables can simultaneously have economic, political, cultural and social weights, but their weights can be different in each of these ranges.

This study is an attempt to apply a static Computable General Equilibrium model based on Iranian Social Accounting Matrix of 1380 in order to investigate the impact of 20 percent increase in Government expenditures under floating and fixed exchange rate regimes, on macro variables in the agricultural sector and Iranian economy as a whole. The parameters of the model are obtained using Calibration method. The model contained two sectors including the agriculture and non-agriculture. The results show that the increased Government expenditures would influence variables of agricultural sector and the macro Iranian economy undesirably, and this would be more serious under the fixed exchange rate regime as compared to the floating regime. It was also revealed that the increment in Government expenditures affects agricultural and rural sectors more in an unpleasant manner than the non-agricultural and urban sectors.

This paper uses a quarterly data to study the effect of the main economic variables on the stock price index in Iran over the period 1993:3–2003:2. An autoregressive distributed lag (ARDL) approach to cointegration analysis is used to study both short- and long-run movements of stock prices in Tehran stock market. The explanatory variables include money supply, production level of large manufacturing companies, and the ratio of domestic to foreign price level, exchange rate, oil price, gold coin price, and housing price index. The results show that there is a long-run equilibrium relationship between the variables. According to our finding, the ratio of domestic to foreign price levels, the price of housing and the gold coin price index have positive impacts on the stock prices. Exchange rate and money supply have significant negative effects on the stock price index in Iran. However, we found that the production level of large manufacturing companies has not affected the stock price index. The result of our error correction model indicated that 54 percent of deviation of the stock price from its equilibrium path is corrected each period.