Journal of Economic Modeling Research

Determining the rate of carbon emission tax in terms of endogenous growth: a case study of Iran

Rouhollah Shahnazi

Economy and environment are two interdependent systems; In recent decades, the global environment, as the most important global public good, has been heavily influenced by the negative external effects of economic growth, including climate change. In order to internalize these external effects, the use of tracking tax is a recommended method. One of the most important models designed for the integrated study of economy and climate is the Nordhaus RICE model; Of course, with the limitation that in this economic growth model, it is included exogenously. In this study, the aim of endogenizing the economic growth of the RICE model and determining the tax rate in 6 scenarios including 1) the base scenario 2) the optimal emission control rate application scenario 3) the 2°C temperature limit scenario 4) the discounted Stern scenario 5) the calibrated Stern scenario and 6) Copenhagen scenario. The results show that in the endogenous growth model, the ratio of taxes to net domestic production and CO2 emissions should increase over time. In all scenarios of Iran's endogenous growth model (except the base scenario), tax increases between 2022 and 2122 will reduce industrial CO2 emissions and reduce atmospheric carbon concentration. Finally, by applying the specified optimal tax in all scenarios, temperature changes have increased by less than two degrees Celsius.

Guide to Investing in the Tehran Stock Exchange: The Application of Machine Learning in Technical Analysis Strategies

Shahab Jahangiri

Objective: The aim of this research is to provide a practical guide for investing in the Tehran Stock Exchange by combining technical analysis techniques with advanced machine learning methods. Focusing on the analysis of buy and sell signals in selected indices of the Tehran Stock Exchange, the study seeks to evaluate the effectiveness of machine learning models in predicting market trends.
Materials and Methods: In this study, the daily data of six selected indices of the Tehran Stock Exchange, including financial, petroleum products, automotive, pharmaceutical, food, and basic metals indices, were analyzed from 2020 to January 2025. Four machine learning models, including Linear Model, Random Forest, Artificial Neural Network, and Support Vector Regression, were utilized alongside two technical analysis strategies, TEMA and MACD, to generate and evaluate buy and sell signals.
Results: The results indicated that machine learning models, particularly Random Forest and Artificial Neural Network, performed better in identifying buy and sell signals when combined with TEMA and MACD strategies. These models were able to predict market trends with higher accuracy, and the signals they generated were mostly consistent with actual price changes. The food, automotivation and financial and basic metals indices demonstrated greater sensitivity to these analyses.
Conclusion: The combination of machine learning methods with technical analysis strategies can provide investors with a powerful tool for decision-making in the Tehran Stock Exchange. This research demonstrated that using these methods can not only improve the accuracy of buy and sell signals but also reduce investment risk and increase returns. Utilizing these models can be recommended as part of an investment strategy for analysts and investors.
Originality: This research is the first quantitative study that seeks to conceptualize buy and sell signals using the combined method of machine learning and technical analysis as one of the basic tools to guide investors.

Predicting the Price Index in the Iranian Stock Market with Emphasis on the Monetary Variables: A Machine Learning Approach

Parviz Rostamzadeh

The stock market, as one of the vital components of the capital market, is an important part of the country's economy that can manage the flow of capital, optimize capital allocation, and thereby contribute to economic growth and development. More accurate prediction of the stock market trend can help investors' decision-making for higher returns by reducing risk. In general, the stock market is constantly changing and many factors influence the trend of this market, so predicting the patterns of movement in the stock exchange requires sufficient information about the past and influencing factors of the market. This article is part of the forecast of the stock market index of Iran, seeking to interpret the model and identify the most influential economic variable on the price index prediction. For this purpose, daily stock market and economic data, during the period 1394-1401 were used. Machine learning models are also used for prediction and the Shapley Additive exPlanations (SHAP) to interpret how to predict and determine the most important variables in the predictive model. Based on results from tree-based ensemble methods, the proposed model in this study, ExtraTrees, performed best based on predictive error criteria. In the study of the feature importance is also based on the ExtraTrees model, in order of the dollar rate (Nima), unemployment rate, dollar rate of market and liquidity, the most important economic variables influencing the forecast model. Also, according to other models used in the research, liquidity is the most effective variable on the stock index trend. Finally, it can be said that the most effective monetary variables on the stock market index in Iran are liquidity and exchange rate variables, so monetary policymakers and stock market investors should be more sensitive to these variables in their decisions.

Convergence of ICT in the user and infrastructure level of Iran's provinces

parvaneh salatin

Today, information and communication technology affects all aspects of human life. The result of which is a transformation in all methods of production and distribution to education, exchanges and human relations. On the other hand, the requirement for the realization of economic development and growth is the higher growth rate in poor and underdeveloped areas than in rich and developed areas, which is proposed as the hypothesis of convergence. In this regard, regional inequalities are a fundamental challenge for the development of regions and these inequalities are a serious threat to create a balanced development of regions. Therefore, the main goal of the current study is to investigate the convergence of Fava among the provinces of the country. The results using the Nahar and Inder method during the period of 2002-2013 showed that out of the 30 investigated provinces, divergence in land use occurred in 22 provinces. Also, at the infrastructure level, the average slope of 31 provinces is positive, but the t-value is significant for the provinces of South Khorasan, Khuzestan, Alborz and Fars, which shows that digital divergence has occurred in these provinces during the period under review. Therefore, it can be seen that although in terms of infrastructure, we have had less divergence at the level of the provinces, but in terms of usage, this gap and divergence has increased.

Investigating the Effect of Logistics Performance Index on Exports of Iran's Free Zones (Spatial Panel Data Models)

Marjan Daman Keshideh

Investigating the spillover effect of logistics performance index on exports in free zones is considered one of the important cases for special attention in these areas, which in recent years has received less attention. Therefore, in this study, by using different methods of spatial panel data, to investigate the spillover effect of the logistics performance index among the seven existing free zones and based on six the performance indices of the logistics industry has been studied during the years 2014 to 2023. This study has investigated the performance of logistics on exports (which is a composite index and areas) in Iran’s free zones by using different spatial regression models. The present study was conducted with the panel data and it shows that findings of this study indicate that the effect of logistics performance index in the SDM model has been confirmed and logistics performance has a positive and significant effect on the export of free zones, so that the growth of logistics indicators in the country leads to growth and speed of exports in free zones. Moreover, investment, labor and the degree of openness of the economy have a positive and significant effect on the export value of free trade zones. Therefore, it is suggested that economic policy makers improve production and export capacity in these areas by improving the logistics, investment and employment performance index.

Extracting Endogenous Jumps in Financial Markets Analytically Using Kramers-Moyal Method

Hassan khodavaisi

Based on the stylized fact, the behavior of price in financial markets is not a continuous process, but we observe jumps in the price that can be endogenous or exogenous. it is claimed that the source of exogenous jumps is news, and the source of endogenous jumps is internal interactions between the market agents. Our goal is to extract these endogenous jumps as a function of the system state variable and time. First, by introducing the Langevin equation as the governing dynamics and linking its parameters with Kramers-Moyal coefficients, we show that these parameters can be extracted based on conditional moments. Next, we use the generalized Langevin equation to model the observed jumps in the data and show that in the new model, the drift coefficient is still equal to the first Kramers-Moyal coefficient, but the diffusion coefficient in this case is lower than the second Kramers-Moyal coefficient. In our model, the jump term consists of two components: jump rate and jump size. We show that these two new components can also be extracted based on Kramers-Moyal coefficients. Also, we introduce a practical criterion based on the fourth and sixth Kramers-Moyal coefficients to choose between the diffusion and jump-diffusion model. Applying the Kramers-Moyal method to extract the generalized Langevin equation shows that this method can accurately reconstruct the process. Tests to evaluate the accuracy of the reconstruction have been used from the information theory. In a practical application, we have extracted the price dynamics of an asset and then shown by simulation that this model is able to answer common statistical questions about stochastic processes with good accuracy. Also, by performing simulations, we show that this model has a good out-of-sample prediction ability. The potential function, which is calculated from the first KM coefficient, is a quadratic parabola for the studied process, and as a result, we have a stable equilibrium at the zero point.