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Showing 3 results for dehghan shabani
Effects of Industrial Agglomeration on Regional Economic Growth in Iran
Zahra Dehghan Shabani,
Volume 3, Issue 8 (6-2012)
Abstract

  This research aims to analyze the effects of industrial agglomeration on regional economic growth in the Iranian provinces. For this aim, this study is divided into theoretical and applied sectors.

  In the theoretical point of view, the research has proposed a simple theoretical framework to study the impacts of industrial agglomeration on regional economic growth. In applied sector, we have specified econometrics models and estimated them by using a system of simultaneous equations using Panel Data for 28 provinces of Iran over the period 2000-2006.

  Results show that regional economic growth is positively affected by industrial agglomeration and regional knowledge level and negatively affected by human capital mobility cost and per capita income. Results also show that regional economic growth, transportation cost, household expenditure and human capital mobility cost have positive effects on industrial agglomeration in the Iranian provinces.


Predicting the Price Index in the Iranian Stock Market with Emphasis on the Monetary Variables: A Machine Learning Approach
Majid Shafiei, Parviz Rostamzadeh, Mohammad Rastegar, Zahra Dehghan Shabani,
Volume 14, Issue 53 (12-2023)
Abstract

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.

The Role of Productive Capacity in Renewable Electricity Generation: A Dynamic Panel Threshold Model
Dr. Mahboubeh Jafari, Ms Fatemeh Rashidi, Dr. دهقان شبانی Dehghan Shabani,
Volume 14, Issue 54 (2-2024)
Abstract


Promoting electricity generation from renewable energy sources has emerged as a cornerstone of sustainable development strategies worldwide to mitigate greenhouse gas emissions and address the pressing challenges of climate change. This study aims to investigate the nonlinear relationship between the Productive Capacity Index (PCI) and renewable electricity generation across a sample of selected developing countries during the period 2000–2022. To this end, the dynamic panel threshold model proposed by Kremer et al. (2013) is utilized, as it enables the analysis of nonlinear interactions among variables in panel data while addressing potential endogeneity. Our findings reveal a non-linear relationship between PCI and renewable electricity generation. Importantly, the influence of PCI on the share of electricity generated from renewable sources intensifies beyond a specific threshold value. This implies that as PCI levels increase, their impact on clean energy production becomes more significant, emphasizing the importance of advancing productive capacities to accelerate the transition to renewable energy. Furthermore, the results underscore the critical role of several key factors in enhancing renewable electricity generation. Rising geopolitical risks, improved financial development, greater trade openness, and an increased share of gross fixed capital formation in GDP are identified as pivotal drivers that positively contribute to the expansion of renewable electricity generation. Conversely, weak environmental policies can significantly hinder this progress. Furthermore, the Dumitrescu and Hurlin (2012) panel causality test confirms the existence of a bidirectional causal relationship between the share of renewable electricity generation and the other explanatory variables. This study highlights the critical need to build and strengthen productive capacity to support the growth of renewable energy. The findings provide a valuable foundation for informed decision-making by policymakers and planners in developing nations.
 

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