Economics Research

Decomposition of Gross Export Value Added in Iran's Economic Activities: An Inter-Country Approach

Esfandiar Jahangard; Alireza Jahangard; Negar Ebrahimi

Volume 23, Issue 90 , October 2023, Pages 5-51

https://doi.org/10.22054/joer.2024.78216.1201

Abstract

In recent years, with the increased availability of data and statistics, particularly multi-country input-output tables and firm-level microdata, along with advances in the data processing capacity of personal computers for managing these vast datasets, as well as information and communication ... Read More In recent years, with the increased availability of data and statistics, particularly multi-country input-output tables and firm-level microdata, along with advances in the data processing capacity of personal computers for managing these vast datasets, as well as information and communication infrastructure, the efficient shared use of databases for foreign trade analysis has become possible. The goal of this paper is to implement the gross export decomposition method by Borin and Mancini (2023), using a source-based approach and the perspective of the exporting country, as a foundational analysis for decomposing value-added in the gross exports of Iran’s economic activities. The contribution of this paper to the economic literature on Iran can be summarized in the following three aspects: First, it utilizes data from the 2016 inter-country input-output database, including data on Iran, for empirical documentation. Second, it focuses on the most recent theoretical framework presented by Borin and Mancini (2023), with a source-based approach and country perspective, to decompose the value-added in the exports of Iran’s economic activities. Third, it offers a structural interpretation of the value-added decomposition of Iran's exports for the year 2016, which can be useful for researchers and policymakers in understanding the global value chains of Iran’s economic activities. The results show that Iran plays a small and fragile role in the global economy. Introduction The global economy has become increasingly interconnected, necessitating comprehensive tools to understand complex trade relationships. Traditional trade statistics, which rely on gross export values, often obscure the actual value added by countries. The emergence of inter-country input-output tables allows for a detailed examination of value-added flows within international trade networks. These tables track the production processes across different countries, shedding light on how value is added at each stage of production. This paper builds on the gross export decomposition framework developed by Borin and Mancini (2023), which enables a nuanced analysis of value-added in exports. By applying this framework to Iran's economic activities, we can gain a clearer picture of how different sectors contribute to the country's export economy. This approach provides a more accurate reflection of Iran's role in global value chains (GVCs), moving beyond traditional metrics that may understate or overstate its economic contributions. The study utilizes the Inter-Country Input-Output(ICIO) database for the year 2016 which includes Iran, which provides comprehensive data on trade and production relationships among various countries. The ICIO database is particularly suited for this analysis as it captures the interconnected nature of global trade and production networks. The Borin and Mancini (2023) methodology involves decomposing gross exports into three main components: The domestic value-added (DVA), that is value-added exported in final or intermediate goods. This is part of the Domestic Content – the part of exports that originated in the country – and is also a measure of GDP in gross exports or in intermediates absorbed by direct importers. The foreign value-added (FVA) that is value-added contained in intermediate inputs imported from abroad, exported in the form of final or intermediate goods. This is part of the Foreign Content – the part of gross exports that originated abroad. The returned value-added is domestic VA in intermediates exported. By applying this decomposition method, we can analyze the contribution of various sectors to Iran's export economy. This analysis involves several steps: Data Preparation: Extracting relevant data from the ICIO database for Iran and its trading partners. Decomposition Calculation: Applying the Borin and Mancini (2023) method to decompose Iran's gross exports into DVA, FVA, and RDVA. Sectoral Analysis: Examining the results to identify key sectors contributing to Iran's value-added exports. Results and Discussion The results reveal significant insights into the structure of Iran's export economy. In 2016, Iran's gross exports were composed predominantly of Domestic Value Added (DVA), reflecting the substantial contribution of domestic industries to the country's exports. The analysis shows that the oil and gas sector plays a crucial role in generating DVA, given Iran's abundant natural resources. However, the study also highlights the presence of Foreign Value Added (FVA) in Iran's exports. This indicates that foreign inputs are integrated into Iran's production processes, demonstrating the interconnectedness of Iran's economy with global supply chains. For instance, machinery and equipment imported from other countries are essential for Iran's manufacturing sector, contributing to the FVA in its exports. The Returned Domestic Value-Added component, although smaller, provides interesting insights into the circular nature of some value-added flows. This component illustrates how certain domestic value-added returns to Iran after being processed abroad. For example, raw materials exported from Iran may be processed into intermediate goods in other countries and then re-imported for further manufacturing. The application of the Borin and Mancini (2023) value-added decomposition method provides a detailed and nuanced understanding of Iran's export economy. By distinguishing between Domestic Value Added (DVA), Foreign Value Added (FVA), and Returned Domestic Value Added (REF), this analysis offers a comprehensive view of how different sectors contribute to Iran's gross exports. Conclusion The study reveals that while Iran's export economy is heavily reliant on domestic industries, it is also deeply from oil and mining interconnected with global supply chains. Furthermore, the Returned Domestic Value-Added component highlights the circular nature of some value-added flows, illustrating the complexity of global trade relationships. For policymakers and researchers, these insights are invaluable. Understanding the composition of Iran's export economy can inform strategies to enhance domestic industries' competitiveness and better integrate into global value chains. Additionally, recognizing the role of foreign inputs in domestic production can guide policies aimed at improving the efficiency and resilience of supply chains. In summary, the value-added decomposition method employed in this study offers a robust framework for analyzing Iran's export economy. It provides a clearer picture of how domestic and foreign industries interact within global trade networks, offering valuable insights for enhancing Iran's economic performance in the context of global value chains.

Investigating Factors Influencing the Success of Initial Coin Offerings Using Logistic Regression

Fatemeh Teimoora; kazem yavari

Volume 23, Issue 90 , October 2023, Pages 52-91

https://doi.org/10.22054/joer.2024.77481.1191

Abstract

Financing remains one of the most critical aspects of business growth and sustainability. The Initial Coin Offering (ICO) method, a novel approach to financing leveraging blockchain technology, has garnered attention due to its ability to attract significant capital globally within a short period ... Read More Financing remains one of the most critical aspects of business growth and sustainability. The Initial Coin Offering (ICO) method, a novel approach to financing leveraging blockchain technology, has garnered attention due to its ability to attract significant capital globally within a short period and without requiring intermediaries. Understanding and analyzing the factors that influence the success or failure of ICOs is thus valuable for businesses and investors alike. This paper examines the factors impacting ICO success using logistic regression analysis, focusing on 307 completed ICO projects from 2016 to 2018. We consider two target success variables: "Total funds collected" and "Hard cap achievement percentage." Factors related to the project, campaign, social networks, and team characteristics were analyzed in separate models. Through model selection based on performance and feature prioritization using the Permutation Importance (PI) technique, the findings highlight that having a well-defined "Business model available" significantly contributes to ICO success across both models. Additionally, the top features in the first selected model under the categories of project, campaign, and social network are "White paper pages," "Token share for presale investors," and "GitHub account," respectively. In the second model, the most impactful factors are "Use of proceeds mentioned" and "Length of crowdsale" under the project and campaign categories. Introduction Creating a stable foundation for business growth requires sustained financial support, as financing is a central pillar of business continuity. The Initial Coin Offering (ICO) method represents an innovative financing avenue, leveraging blockchain technology to enable rapid capital acquisition from a global investor base without intermediary involvement. By examining the mechanics of ICOs and analyzing factors that influence their success or failure, this study aims to expand theoretical insights into ICOs, prevent inefficient trends, and promote the optimal utilization of this financing method. Methods and Material To identify factors impacting the success of ICO campaigns, this study collected data on 307 ICO projects from 2016 to 2018, categorizing them into four dimensions: project, campaign, social network, and team characteristics. Two target variables were used to assess ICO success: "Total raised capital during ICO" for the first set of four models, and "Percentage of hard cap (the maximum capital set by project founders) raised" for the second set. The logistic regression algorithm, a supervised machine learning technique for binary classification, was employed to predict the probability of success or failure. A cumulative process approach was used to create and analyze the research model. Results and Discussion After optimizing logistic regression structures across eight research models, the highest prediction accuracy was observed among the first four models using "Total raised capital during ICO" as the dependent variable. Models 1-3, with independent variables focusing on project, campaign, and social network characteristics, achieved 70% accuracy based on the weighted average of Precision for both success and failure groups and 72% for Recall. This result indicates the model’s high effectiveness in predicting unsuccessful ICOs. In the second set of models, using "Percentage of hard cap raised" as the dependent variable, model 2-2 (with independent variables for project and campaign characteristics) showed the best performance, achieving 67% and 74% accuracy for precision in both groups and 93% Recall accuracy for unsuccessful groups (31% for successful groups). Consequently, models 1-3 and 2-2 were selected for their high accuracy in predicting ICO campaign success or failure. The Permutation Importance (PI) technique identified the top influential features in each model. For model 1-3, the most critical factors were "White paper pages," "Certainty of presale token share for investors," and "Project business model accessibility." In model 2-2, the leading features included "Mentioned use of proceeds," "Availability of business model," and "Crowdsale duration." Notably, in model 1-3, the most effective features by category were "White paper pages" (project), "Token share presale investors" (campaign), and "GitHub account" (social network). For model 2-2, the prominent features were "Use of proceeds mentioned" and "Crowdsale length" within project and campaign categories, respectively. Conclusion The findings reveal that the most influential variables across models 1-3 and 2-2 are: Project category: "White paper page count" and "Use of proceeds mentioned." Campaign category: "Token share presale investors" and "Crowdsale length." Social network category: "Active GitHub account availability." Key features, such as "White paper pages," "Token share presale investors," and "GitHub account" in model 1-3, along with "Use of proceeds mentioned" and "Crowdsale length" in model 2-2, emerged as the most significant factors for ICO success. The prominence of campaign characteristics in both optimal models underscores their critical role in ICO outcomes. This research suggests that addressing informational gaps and identifying success factors can facilitate the responsible and effective adoption of ICOs. By focusing on these pivotal features, businesses can enhance their likelihood of success while streamlining their financing strategies through ICOs.

The Effect of Age and Gender on Social Preferences in Iran

habib morovat

Volume 23, Issue 90 , October 2023, Pages 92-134

https://doi.org/10.22054/joer.2024.77385.1187

Abstract

A substantial body of research highlights the presence of social preferences, their economic and political implications, and the varied conditions that influence their effects on the equilibrium and outcomes of human interactions. This study utilizes data from the Global Preferences Survey (GPS) to explore ... Read More A substantial body of research highlights the presence of social preferences, their economic and political implications, and the varied conditions that influence their effects on the equilibrium and outcomes of human interactions. This study utilizes data from the Global Preferences Survey (GPS) to explore how age and gender impact social preferences across Iran at both national and provincial levels. The findings from a multivariate regression analysis reveal that age positively and significantly influences trust and altruism, while it negatively and significantly impacts negative reciprocity, with no significant effect on positive reciprocity. Gender, on the other hand, shows a significant negative effect on trust, indicating that women tend to exhibit lower levels of trust. Additionally, income and education negatively and significantly impact trust but positively affect altruism and positive reciprocity, with no notable effect on negative reciprocity. The study also presents a provincial ranking within Iran based on key elements of social preferences: trust, altruism, positive reciprocity, and negative reciprocity. According to the research findings, Iran scores above the global average in terms of altruism, trust, and reciprocity. Notably, Markazi, Lorestan, and Ilam provinces rank highest in trust, altruism, and reciprocity, respectively. Introduction An influential set of laboratory experiments has questioned the conventional wisdom among economists and the validity of Stigler's (1981) position that "when self-interest and moral values conflict, most of the time, self-interest theory will win." These studies are complemented by a whole body of theoretical research that examines the nature and economic consequences of "social preferences". The most important of these theories consider social preferences as a result of altruism, reciprocity, fairness, and inequality-avoidance, maintaining social image or other motives. Studies based on field experiments also confirm the results of laboratory studies (List, 2006 and Falk et al., 2018). Ignoring social preferences makes economists unable to understand basic economic questions. Without considering social preferences, it is impossible to understand questions such as the effects of competition on market outcomes, the rules governing cooperation and collective action, the effects and determinants of material incentives, contracts and optimal property rights arrangements, and the important forces shaping social norms and market failure (Fehr and Fischbacher, 2002). Determining factors of social preferences can be different according to demographic, geographic and cultural characteristics. Studies have shown that older people tend to prioritize fairness and reciprocation behavior (Falk et al., 2018). Also, studies have shown that women are typically more cooperative and tend to be more than men (Crawson & Gnezzi, 2009 and Falk & Hermel, 2018). Research has shown that older people and women typically have more trust (Crowson Vegnizi, 2009 and Duhman et al., 2008). In Iran, due to the diversity of culture and geographical conditions, it is important to investigate the role of demographic variables on social preferences. In this research, we examined the causal relationship between age, gender, income, and education with each of the components of social preferences at the national and provincial levels with a standard model and technique. Methods and Material In order to investigate the factors affecting social preferences, the required data and information have been collected using a questionnaire. The information related to the dependent and independent variables of this research was extracted from the valid questionnaire available in Falk et al.'s article (2018) and the GPS website. Information related to Iran has been extracted from more than 2500 participants from all provinces of the country. In order to investigate the causal relationship between age and gender with variables of social preferences, multivariate regressions have been used in general as follows: Results and Discussion Using Ordinary Least Squares (OLS) regression, we estimated a model to examine the significance of gender, age, and cognitive ability on various dimensions of social preferences, including trust in others, altruism, and positive and negative reciprocity, as well as an aggregated index of social preferences. The analysis was conducted both at the national level and across individual provinces. Table 1 presents the estimated coefficients and their significance, highlighting how each variable influences these social preference metrics across different contexts within the country. Table 1: Model estimation results for the whole country independent variables trust altruism Positive reciprocity Negative reciprocity Social preferences index Dependent variables age 0.0787*** 0.006*** 0.0053 -0.016** 0.0058* Squared age -0.0001 0.00005 -0.00004 -0.0003 -0.0002 gender -0.1293*** 0.049 0.0018 -0.0615 -0.1242 income -0.045** 0.029*** 0.03*** 0.018 0.015** education -0.010*** 0.022*** 0.033*** 0.012 0.014** math 0.0165** 0.0095 0.0224*** 0.0305*** 0.0818*** R-Squared 0.0451*** 0.007*** 0.0068*** 0.0582*** 0.0126*** Sample size 2406 2474 2478 2449 2373 Source: Research calculations *, **, *** are significant statistics at the level of 10, 5, and 1 percent, respectively. The models were tested for heteroskedasticity, and where it was detected in the residuals, a robust estimator was applied. Specifically, in the models for positive reciprocity and the social preferences index, the null hypothesis of homoskedasticity was rejected, necessitating the use of robust estimations. Ramsey's test was conducted to check for potential endogeneity arising from omitted variables or specification errors, and this test did not reject the null hypothesis in any of the models. Table 2 provides the goodness-of-fit test results for these models. Table 2: The results of goodness of fit tests Tests/ Models Trust Altruism Positive reciprocity Negative reciprocity Social preference index Breusch–Pagan (Prob) 1.88 (0.17) 1.75 (0.18) 11.65 (0.0006) 2.52 (0.11) 3.87 (0.0491) Ramsey Test (Prob) 1.03 (0.38) 0.26 (0.85) 0.15 (0.92) 1.81 (0.14) 1.25 0.29 Source: Research calculations Conclusion In this research, an attempt was made to investigate the effect of demographic variables such as age, gender, income, and education on social preferences for the entire country and province. The results of multivariate regression showed that, firstly, people's age has a positive and significant effect on trust in others and altruism. This finding is consistent with the findings of most studies. However, age has a negative and significant effect on negative reciprocity, which shows that negative reciprocity decreases with increasing age. And finally, age does not have a significant effect on positive reciprocity. Secondly, gender only has a negative and significant effect on trust in others and has no significant effect on other social variables. In other words, women are less devoted to others than men. The findings of this study are not consistent with the findings of most other studies on gender. Because in most other studies, women are more social, more altruistic and have significant negative countermeasures compared to men. The cause of this issue can be related to the culture and religion and the role of women in the country, which needs to be studied more in this field. Thirdly, income and education have a negative and significant effect on trust and have a significant and positive effect on altruism and positive reciprocity and do not have a significant effect on negative reciprocity. The effect of income and education is very similar. Income and education have a negative and significant effect on trust and have a significant and positive effect on altruism and positive reciprocity and do not have a significant effect on negative reciprocity. Failure to understand and identify factors influencing social preferences leads to a misunderstanding of people's economic behavior (Fehr and Fischbacher, 2002). Correct understanding and identification of factors affecting social preferences helps policymakers to act optimally in understanding the process of cooperation between economic factors, designing economic incentives, and designing social policies.

Analysis of Russia's Asymmetric Behavior in the GECF and World Gas Market

Yaghoub Andayesh; Amir Hossein Montazer Hojat; Eshagh Qasemi

Volume 23, Issue 90 , October 2023, Pages 135-174

https://doi.org/10.22054/joer.2024.77053.1175

Abstract

Russia’s position in the world gas market and common membership of Russia with Iran have given importance to the analysis of Russia's gas production behavior in the world gas market. Russia’s position in the global gas market and its excessive capacity in gas production provide this ... Read More Russia’s position in the world gas market and common membership of Russia with Iran have given importance to the analysis of Russia's gas production behavior in the world gas market. Russia’s position in the global gas market and its excessive capacity in gas production provide this country with the possibility that in the long term, its behavior in natural gas production is aligned and symmetrical with the gas production of other members of the gas exporting countries, shale gas production, world natural gas demand, world natural gas price and the crude oil world price doesn’t change in order to stabilize the symmetric and aligned market. In this paper, Russia's gas production behavior was investigated by using seasonal data from 2001 to 2021 and by nonlinear autoregressive distributed lags (NARDL) method. The results showed that during changes (increase & decrease) in the mentioned variables; as the most important variables affecting the formation of changes in the supply and demand of the world Gas, Russia's gas production behavior in the direction of aligned market stability will not change. And in the GECF, Russia shows a similar behavior to Saudi Arabia in the OPEC. Introduction As a member of the Gas Exporting Countries Forum (GECF), Russia has excess capacity in gas production, which is similar to the oil excess production of Saudi Arabia as a member of OPEC. In addition, both countries play a significant role in their respective organizations. Using data from 2001Q1 to 2021Q4, this research applies a nonlinear autoregressive distributed lags method and modifies Griffine’s (1985) model developed by Gatly et al. (2014) to analyze Russia’s gas production behavior in response to any promotion or reduction in gas production by other members of GECF, the shale gas production, the global demand of natural gas, and the global price of natural gas and oil, which can influence the global supply and demand of natural gas. Identifying and analyzing Russia’s asymmetrical behavior in the GECF and gas global market has essential outcomes for Iran as another member of the GECF. First, it leads to more robust predictions of Russia’s decisions when global supply and demand determinants of natural gas increase or decrease. Second, Iran can take action based on the results to increase its bargaining power in GECF in line with its national interests. Methods and Material As mentioned above, this research analyzes the behavior of countries in the GECF, which was founded in 2001. This means the study has a limited sample size. Therefore, Johansson's approach may not be reliable because it requires large samples to produce trustworthy results. In contrast, the autoregressive distributed lags method is a statistically more robust and suitable choice for identifying cointegration relationships in small samples (Ghatak & Siddiki, 2001). Furthermore, to analyze Russia's asymmetric behavior in the GECF and global gas market, this research employs the expanded method of autoregressive distributed lags, known as nonlinear autoregressive distributed lags, as presented by Shin et al. (2011). Results and Discussion The results show Russia has an asymmetric behavior in response to the gas production of other GECF members ( hypothesis is rejected). In other words, Russia's response to increases and decreases in production by other members is not the same. Furthermore, the negative coefficients of and state that when there is an increase in gas production by other members, Russia decreases gas production in the opposite direction, while when there is a decrease in production by other GECF members, Russia reduces its production in the same direction but to a lesser extent. During the study period, the results showed that Russia's gas production behavior is symmetric (failure to reject the hypothesis) in the face of shale gas production. In fact, Russia's response to increases and decreases in shale gas production is the same. The positive coefficients suggest that Russia increases gas production when shale gas production increases. In other words, Russia's production is in line with shale gas production. However, in the long term, there is no significant relationship between decreases in shale gas production and Russia's gas production. Similarly, the results indicate that Russia's gas production behavior is asymmetric in response to global gas demand (rejecting the hypothesis). In fact, Russia's response to increases and decreases in global gas demand is not the same. The positive coefficients of suggest that Russia's gas production increases when global gas demand increases. However, in the long term, there is no significant relationship between decreases in global gas demand and Russia's gas production. Similarly, the results show that Russia's gas production behavior is asymmetric in response to global natural gas prices (rejecting the hypothesis). In fact, Russia's response to increases and decreases in global natural gas prices is not the same. On the one hand, the negative coefficient suggests that Russia's gas production behavior decreases when global natural gas prices decrease. On the other hand, in the long term, there is no significant relationship between increases in global natural gas prices and Russia's gas production. This behavior suggests that Russia, given its excess gas production capacity, reduces its production in order to stabilize and adjust global natural gas prices when global natural gas prices fall. The results for the study period show that there is no significant long-term relationship between Russia's gas production and changes in global crude oil prices. Conclusion To sum up, this research shows that with changes (increases/decreases) in gas production of other GECF members the production of shale gas, and the global prices of natural gas and crude oil, which are essential determinants of global supply and demand, Russia's gas production behavior is not always aligned and symmetrical with the mentioned variables. According to Guttly et al. (2014), it can be concluded that Russia, like Saudi Arabia in OPEC, has an uncoordinated production behavior with other members of the forum and other mentioned variables.

An Investigation on the Effect of Tax Avoidance on the Transparency of Financial Reporting Firms Listed in Tehran Stock Exchange

Omid Pour-heidari; Mohammad Hasan Fadavi; Meysam Amini-nia

Volume 14, Issue 52 , April 2014, , Pages 85-69

Abstract

Abstract In this paper, the impact of tax avoidance on the transparency of financial reporting of firms listed in Tehran Stock Exchange is investigated. The indexes that are used to measure tax avoidance and financial reporting transparency are respectively effective tax rate and accruals quality. The ... Read More

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Volume 2, Issue 7 , January 2003, , Pages 165-199

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Volume 5, Issue 18 , October 2005, , Pages 13-47

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The concept and measurement of economic Resilience

abolfazl ghiasvand; fatemeh abdolshah

Volume 15, Issue 59 , January 2016, , Pages 161-187

Abstract

In this paper, resilience of an economic system is measured by an overall index, based on the Borman et al. index, for the period of 1996-2013. The results are then compared with Briguglio index. To measure the final index, the first step is to make variables have a similar direction. The second step ... Read More

Study of Correlation between Volatility of Stock, Exchange and Gold Coin Markets in Iran with DCC-GARCH Model

Firouz Fallahi; Jafar Hghighat; Naser Sanoubar; Khalil Jahangiri

Volume 14, Issue 52 , April 2014, , Pages 147-123

Abstract

Abstract The aim of this paper is to investigate the behavior of stock, exchange and gold coin markets and their correlations structure by using the DCC-GARCH model and the daily data for the period from 23 July 2011 to 22 September 2013 in Iran. Results show that there is a high correlation between ... Read More

The impact of covid-19 and good governance on income inequality in selected oil exporting countries

parisa moghadasi; Sajjad Faraji Dizaji; Abbas Assari Arani

Articles in Press, Accepted Manuscript, Available Online from 29 January 2024

https://doi.org/10.22054/joer.2024.74874.1158

Abstract

Income inequality is one of the important and key issues in the economy, which, considering its function and consequences, can affect people's health in various ways and endanger the stability and stability of the socio-economic system. Today, most countries suffer from inequality in the distribution ... Read More

Money and Monetary Economics

Examining the Equity premium puzzle in Iran: a practical approach using a Dynamic Stochastic General Equilibrium Model

sahar zare joneghani; Bahram sahabi; hassan heydari; Mehdi Zolfaghari

Articles in Press, Accepted Manuscript, Available Online from 29 June 2024

https://doi.org/10.22054/joer.2024.76901.1171

Abstract

The equity premium is obtained from the difference between the return on the risky stock asset and the return on the risk-free asset; the failure of financial theory to explain high equity premium is known as the equity premium puzzle. This puzzle was introduced for the first time by Mehra and Prescott ... Read More

Public Sector Economics

Non-linear approach of currency market pressure and real prices of food, metals, ‎energy and raw materials in the countries that export food, metals, energy and raw ‎materials: soft panel regression approach

aso esmailpour

Articles in Press, Accepted Manuscript, Available Online from 21 July 2024

https://doi.org/10.22054/joer.2024.78536.1204

Abstract

In this article, the foreign exchange market index (EMP) in the countries that ‎export metals, spices, raw materials and energy during the period of ‎‏1990‏‎-‎‏2020‏‎ by ‎applying the simple panel transition regression model (PSTR), two regimes on the real ‎price ... Read More

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Financial Economics

Risk Modeling of Stocks of Large Companies in the Tehran Stock Exchange; A Multivariate Factor Stochastic Volatility Approach

Reza Talebloo; parisa mohajeri; Mortaza Yeganeh

Articles in Press, Accepted Manuscript, Available Online from 10 September 2024

https://doi.org/10.22054/joer.2024.77305.1184

Abstract

The aim of the current research is to present a model for the risk analysis of 30 large companies in the Tehran Stock Exchange using the multivariate factor stochastic volatility model (MFSVM) in the framework of the non-linear state-space approach. In this framework, the volatility of stock returns ... Read More

The Economy of Iran

Investigating the Role of Social Capital in the Interaction between Physical Capital and Iran's GDP using the Soft Transition Regression (STR) model

Sholeh Bagheri Pormehr; Zahra Laki; Hanieh Parnyan

Articles in Press, Accepted Manuscript, Available Online from 10 September 2024

https://doi.org/10.22054/joer.2024.77437.1190

Abstract

The basic questions of economic growth models, including the relationship between types of capital and the rate of real growth, have always been a concern of scientific and political circles. In many empirical studies, it has been shown that physical capital and social capital each affect the amount ... Read More

Public Sector Economics

The role of organizing informal street businesses in the development of food tourism with the approach of urban economy sustainability (case study: Tehran city)

Sogol Shahidi; Davood Abbasi Karjagan; Seyyed Mohsen Tabatabaei Mozdabadi

Articles in Press, Accepted Manuscript, Available Online from 17 September 2024

https://doi.org/10.22054/joer.2024.79835.1219

Abstract

The present study aims to investigate the role of organizing informal street businesses in the development of food tourism. The statistical population of the research included tourists and street food visitors from the tourist destination of Tehran, and the number of samples was calculated based on the ... Read More

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