mohammdhasan fotros

The unemployment rate and Gross Domestic Product (GDP) are among the most important economic indicators that understanding their current and future trends can help policymakers and decision-makers adopt appropriate solutions to prevent crises and improve the country’s economic situation. Accurate prediction of these two indicators can be useful in future planning and improving the country’s economy and people’s livelihoods. In recent years, artificial intelligence techniques and tools, given their many capabilities, can play a very important role in predicting important economic indicators. Therefore, given the high importance of the two indicators of unemployment rate and GDP on the economy of our country Iran, this article intends to first predict these two indicators separately and then predict the rate of GDP growth based on the unemployment rate using artificial intelligence techniques. For this purpose, in this research, seasonal data related to GDP and its components and the unemployment rate for the years 1976-2022 have been used. Also, machine learning models based on regression have been used for prediction. The results show that the predictions of the mentioned models have an appropriate accuracy in terms of evaluation criteria such as root mean square error, mean absolute error, mean absolute percentage error, which indicates.

The aim of this paper is an examination of the co-movement between OPEC oil price and Tehran Exchange Market returns. To analyze the relationship between two variables, applied the wavelet coherence approach and utilized daily data during 2009-2020. According to the wavelet approach, a dynamic causal relationship between oil price and the stock market return provides for the causality between the two variables and the type of causal relationship in the time-frequency analysis. Findings showed there is a positive correlation between oil price and stock market earnings. Comparison of the data in annual time-frequency scale indicated that the oil price and stock market returns are in phase from 2009 to 2011, and observed a positive relationship between them. From late 2011 to mid- 2015, both variables are in phase, and oil prices are the leading factor in the stock market. During the period 2015 to 2021, both variables are in phase, but co-movements of oil price and stock market returns not observed. The non-causal effect of oil price on stock market earnings at some times does not mean that oil prices do not affect the stock market. As mentioned, in most periods, oil price and stock market profits are in phase. Factors such as imposing economic sanctions on Iran such as oil sanctions, fluctuations in the value of the dollar, volatilities in oil prices, changes in global demand for exports and imports from Iran, privatization, increasing the gap between the official and the market exchange rate, financial markets shocks, Middle East price tensions and the financing process affected the relationship between the stock market and oil price.

Development is not just an economic phenomenon but it includes also economic, social, political and cultural aspects. Therefore, to study the development of each sector, it is necessary to consider its various dimensions. On the other hand, in order to accurately plan and effectively implement policies, it is necessary to pay attention to its evaluation at the partial and provincial levels, instead of examining the extent of development in general and at the national level. Therefore، This paper used numerical taxonomy and SPSS and Excel softwares to evaluate the degree of development of the provinces of Iran. It also examined the existence of a core-periphery model to determine the process of inequality change between central and border provinces with the help of 25 indexes for the years 2006, 2011 and 2016. Results confirmed the model of the core-periphery; central provinces such as Tehran, Isfahan, Yazd have had the highest level of development and border provinces such as Sistan Baluchestan, Hormozgan, and Kurdistan were at a low level of development. Also, considering the inequality index, inequality between central provinces was more than the inequality between border provinces. In addition, during the years 2006 to 2016, the level of development and inequality in the provinces of the country had been decreasing.

This study identifies and estimates the effective factors on housing prices over the years 1996 to 2017 in terms of model uncertainty and with BACE approach. The statistical data of 18 variables including 15 external variables (socioeconomic variables) and 3 internal variables (housing sector variables) affecting housing price according to the theoretical foundations and experimental studies have been used for this research. The results suggest that the growth of urban population, household income, the unemployment rate, the average cost of 1-square-meter building, expected inflation, income inequality, oil revenues growth, liquidity, and exchange rate are the most effective variables in housing price pattern in Iran. There is no strong evidence of effectiveness of other variables on housing price over the period of this research. The results can be used for creating appropriate patterns for explaining the issues related to housing price and better management of housing sector policies.