Semnan UniversityJournal of Econometric Modelling2345-654X10220250723The Threshold Effect of Military Spending on Economic Growth: A Panel Smooth Transition Regression ModelThe Threshold Effect of Military Spending on Economic Growth: A Panel Smooth Transition Regression Model929952610.22075/jem.2025.36105.1955FAMohammadHassanzadehProfessor of Economics, Department of Economics, Faculty of Social Scinces, University of Mohaghegh ArdabiliAbdolrahimHashemiAssociate Professor of Economics, Department of Economics, Faculty of Social Scinces, University of Mohaghegh ArdabiliZeynabFutoohiMaster of Economics, Department of Economics, Faculty of Social Scinces, University of Mohaghegh ArdabiliJournal Article20241201This study utilized a panel smooth transition regression model to investigate the effect of military spending on economic growth in selected oil-producing countries from 1980 to 2022. The findings reveal that the coefficient for military spending in the first regime is -0.1787, suggesting that increases in military expenditure are associated with a decrease in economic growth. In the second regime, the coefficient rises sharply to -6.3539, indicating a significantly greater negative effect on economic growth. Additionally, the lagged military spending variable in the first regime shows a positive coefficient, implying that military spending from the previous year positively influences economic growth in the current year. However, in the second regime, this effect turns negative, exacerbating the adverse impact of rising military spending on economic growth. Overall, the results demonstrate that crossing the threshold limit of $575 million leads to an intensified negative effect of military spending on economic growth, with a transition speed of 40.22.This study utilized a panel smooth transition regression model to investigate the effect of military spending on economic growth in selected oil-producing countries from 1980 to 2022. The findings reveal that the coefficient for military spending in the first regime is -0.1787, suggesting that increases in military expenditure are associated with a decrease in economic growth. In the second regime, the coefficient rises sharply to -6.3539, indicating a significantly greater negative effect on economic growth. Additionally, the lagged military spending variable in the first regime shows a positive coefficient, implying that military spending from the previous year positively influences economic growth in the current year. However, in the second regime, this effect turns negative, exacerbating the adverse impact of rising military spending on economic growth. Overall, the results demonstrate that crossing the threshold limit of $575 million leads to an intensified negative effect of military spending on economic growth, with a transition speed of 40.22.https://jem.semnan.ac.ir/article_9526_d4d8d1ac7e00e9105775a6b660dd3cbb.pdfSemnan UniversityJournal of Econometric Modelling2345-654X10220250622Modeling the Factors Affecting Uncertainty in the Capital Market Using BMA-PTVPFAVAR ApproachesModeling the Factors Affecting Uncertainty in the Capital Market Using BMA-PTVPFAVAR Approaches3171972610.22075/jem.2025.36231.1970FAAmirKalantar HormoziPh.D. Student in Economics, Department of Economics, Is.C., Islamic Azad University, Isfahan, Iran.AbdolmajidAhangariAssociate Professor of Economics, Department of Economics, Shahid Chamran University of Ahvaz, Ahvaz, Iran.Sayed AminMansouriAssociate Professor of Economics, Department of Economics, Shahid Chamran University of Ahvaz, Ahvaz, Iran.BaharHafeziAssistant Professor of Economics, Department of Economics, Is.C., Islamic Azad University, Isfahan, IranJournal Article20241231Traditional models lack sufficient capability in predicting investors’ portfolio returns due to model specification errors; therefore, this study aims to address model uncertainty issues. Accordingly, the objective of this research is to model the factors affecting uncertainty in the capital market. This is an applied research study. The statistical sample includes 171 listed companies on the stock exchange over the period 2011–2023. In this study, 62 risks affecting stock returns were incorporated into nonlinear Bayesian models. Based on the results of the BMA models, 13 non- fragile risks influencing stock returns were identified. According to the findings, interest rate lags, liquidity ratios, and oil revenues—having the highest probability of impact—are the most important risks affecting stock returns. The PTVPFAVAR model results indicate that the long-term elasticity between stock returns and the research variables is higher than the short-term elasticity, suggesting that these risks have a stronger influence on stock returns in the long run compared to the short term.Traditional models lack sufficient capability in predicting investors’ portfolio returns due to model specification errors; therefore, this study aims to address model uncertainty issues. Accordingly, the objective of this research is to model the factors affecting uncertainty in the capital market. This is an applied research study. The statistical sample includes 171 listed companies on the stock exchange over the period 2011–2023. In this study, 62 risks affecting stock returns were incorporated into nonlinear Bayesian models. Based on the results of the BMA models, 13 non- fragile risks influencing stock returns were identified. According to the findings, interest rate lags, liquidity ratios, and oil revenues—having the highest probability of impact—are the most important risks affecting stock returns. The PTVPFAVAR model results indicate that the long-term elasticity between stock returns and the research variables is higher than the short-term elasticity, suggesting that these risks have a stronger influence on stock returns in the long run compared to the short term.https://jem.semnan.ac.ir/article_9726_5fcb7f9c321885a69a4f91cb553fa04d.pdfSemnan UniversityJournal of Econometric Modelling2345-654X10220250723Analysis of economic inequalities using bivariate total time on test transform and copula functionAnalysis of economic inequalities using bivariate total time on test transform and copula function73108972710.22075/jem.2025.36678.1986FAMojtabaEsfahaniAssistant Professor in statistics, Department of Statistics,
Velayat University of IranshahrGholam RezaMohtashami BorzadaranProfessor in Statistics, Department of Statistics, Ferdowsi University of MashhadMohammadAminiProfessor in Statistics, Department of Statistics, Ferdowsi University of MashhadJournal Article20250125This study investigates the relationship between two key economic indicators: income and wealth. Despite apparent similarities in the behavior of these two variables, numerous instances highlight their differences and lack of complete correlation. The main objective of this paper is to analyze the dependency structure between individuals’ income and wealth using advanced statistical tools. To model the dependency between income and wealth, the copula function—an innovative tool in probability theory—has been employed. In this regard, various copula functions are examined, and the Clayton copula family along with the Farlie-Gumbel-Morgenstern (FGM) copula family are identified as the most suitable choices for the studied data. Additionally, the study introduces a new bivariate index based on the Total Time on Test (TTT) transform in the bivariate case, utilizing copula functions. This index is applied to real-world data on the income and wealth of Iranian households, and the results demonstrate a significant relationship between the two variables at the societal level. Therefore, it can be concluded that using this index in economic data reveals that the proposed bivariate TTT index can effectively represent the dependency structure between income and wealth. Furthermore, the use of Clayton and FGM copulas also shows a good fit with the empirical data.This study investigates the relationship between two key economic indicators: income and wealth. Despite apparent similarities in the behavior of these two variables, numerous instances highlight their differences and lack of complete correlation. The main objective of this paper is to analyze the dependency structure between individuals’ income and wealth using advanced statistical tools. To model the dependency between income and wealth, the copula function—an innovative tool in probability theory—has been employed. In this regard, various copula functions are examined, and the Clayton copula family along with the Farlie-Gumbel-Morgenstern (FGM) copula family are identified as the most suitable choices for the studied data. Additionally, the study introduces a new bivariate index based on the Total Time on Test (TTT) transform in the bivariate case, utilizing copula functions. This index is applied to real-world data on the income and wealth of Iranian households, and the results demonstrate a significant relationship between the two variables at the societal level. Therefore, it can be concluded that using this index in economic data reveals that the proposed bivariate TTT index can effectively represent the dependency structure between income and wealth. Furthermore, the use of Clayton and FGM copulas also shows a good fit with the empirical data.https://jem.semnan.ac.ir/article_9727_73a1c863a54653d5e184b790fee14754.pdfSemnan UniversityJournal of Econometric Modelling2345-654X10220250723Examining the Impact of the Shadow Economy and Institutional Quality on Sustainable Development in Selected Countries: A Moment-Based Quantile Regression Approach (MM-QR)Examining the Impact of the Shadow Economy and Institutional Quality on Sustainable Development in Selected Countries: A Moment-Based Quantile Regression Approach (MM-QR)109147982610.22075/jem.2025.36294.1966FAAliHasanvandAssistant in Economics, Department of Economics, University of LorestanBaharSalarvandPh.D. Student in Economics, Department of Economics,
University of LorestanJournal Article20241218Sustainable development is among the most critical concerns of policymakers and politicians, referring to the consideration of future generations. Given that one of the goals of BRICS member countries is to achieve higher and better-quality production, examining the impact of production on sustainable development is of great importance. Many developing countries, including BRICS members, follow numerous regulations to start businesses, which increases the likelihood of forming a shadow economy. This study examines the effect of the shadow economy and institutional quality on sustainable development in BRICS countries during the period 2007–2020. The study employs the Moment Quantile Regression approach. The results indicate that the impact of the shadow economy on sustainable development is negative and statistically significant across all quantiles. Furthermore, the effect of institutional quality on sustainable development is positive and statistically significant across all quantiles. The influence of other control variables, such as urbanization rate, globalization, and human development, on sustainable development is negative, negative, and positive, respectively, and statistically significant.Sustainable development is among the most critical concerns of policymakers and politicians, referring to the consideration of future generations. Given that one of the goals of BRICS member countries is to achieve higher and better-quality production, examining the impact of production on sustainable development is of great importance. Many developing countries, including BRICS members, follow numerous regulations to start businesses, which increases the likelihood of forming a shadow economy. This study examines the effect of the shadow economy and institutional quality on sustainable development in BRICS countries during the period 2007–2020. The study employs the Moment Quantile Regression approach. The results indicate that the impact of the shadow economy on sustainable development is negative and statistically significant across all quantiles. Furthermore, the effect of institutional quality on sustainable development is positive and statistically significant across all quantiles. The influence of other control variables, such as urbanization rate, globalization, and human development, on sustainable development is negative, negative, and positive, respectively, and statistically significant.https://jem.semnan.ac.ir/article_9826_197cec18dff201ab4d1966879423f50b.pdfSemnan UniversityJournal of Econometric Modelling2345-654X10220250723Asymmetric Effects of the Real Exchange Rate on Iran’s Bilateral Trade Balance with Its Trading Partners (J-Curve): An Application of the Panel CS-ARDL ApproachAsymmetric Effects of the Real Exchange Rate on Iran’s Bilateral Trade Balance with Its Trading Partners (J-Curve): An Application of the Panel CS-ARDL Approach149194995410.22075/jem.2025.38284.2019FAElaheAriya MovahedM.A. Student in Economics, Faculty of Economics, University of Tehran, Tehran, IranAminHaghnejadAssistant Professor of Economics, Faculty of Economics, University of Tehran, Tehran, IranJournal Article20250710This study investigates the impact of the real exchange rate on Iran’s bilateral trade balance with 26 trading partners over the period 1981–2023. Given the significant presence of cross-sectional dependence among the variables—supported by strong statistical evidence—second-generation panel unit root and cointegration tests, as well as the Cross-Sectionally Augmented ARDL (CS-ARDL) approach, are employed to account for this feature. Furthermore, the potential asymmetry in the trade balance’s response to positive and negative changes in the real exchange rate is examined. Results from Westerlund cointegration tests confirm the existence of a long-run relationship between the trade balance, the real exchange rate, Iran’s real income, and the real income of its trading partners. Accordingly, an error correction model based on the CS-ARDL framework is estimated using the Dynamic Common Correlated Effects Pooled Mean Group (DCCEPMG) estimator to capture both the long-run relationship and short-run dynamics. The estimates reveal that real exchange rate changes have positive and symmetric effects on the trade balance in both the long and short run. Specifically, a depreciation of the domestic currency improves the trade balance, while an appreciation worsens it. Hence, while the long-run results are consistent with the symmetric J-curve hypothesis, the short-run adjustments do not support the J-curve effect. Nevertheless, the Marshall–Lerner condition holds not only in the long run but also in the short run. Furthermore, the findings show that, in the long run, Iran’s real income has a significant negative effect, whereas the real income of its trading partners has a significant positive effect on Iran’s trade balance. The findings of this study provide useful insights for economic policymakers in formulating effective exchange rate and trade policies aimed at improving Iran's trade balance.This study investigates the impact of the real exchange rate on Iran’s bilateral trade balance with 26 trading partners over the period 1981–2023. Given the significant presence of cross-sectional dependence among the variables—supported by strong statistical evidence—second-generation panel unit root and cointegration tests, as well as the Cross-Sectionally Augmented ARDL (CS-ARDL) approach, are employed to account for this feature. Furthermore, the potential asymmetry in the trade balance’s response to positive and negative changes in the real exchange rate is examined. Results from Westerlund cointegration tests confirm the existence of a long-run relationship between the trade balance, the real exchange rate, Iran’s real income, and the real income of its trading partners. Accordingly, an error correction model based on the CS-ARDL framework is estimated using the Dynamic Common Correlated Effects Pooled Mean Group (DCCEPMG) estimator to capture both the long-run relationship and short-run dynamics. The estimates reveal that real exchange rate changes have positive and symmetric effects on the trade balance in both the long and short run. Specifically, a depreciation of the domestic currency improves the trade balance, while an appreciation worsens it. Hence, while the long-run results are consistent with the symmetric J-curve hypothesis, the short-run adjustments do not support the J-curve effect. Nevertheless, the Marshall–Lerner condition holds not only in the long run but also in the short run. Furthermore, the findings show that, in the long run, Iran’s real income has a significant negative effect, whereas the real income of its trading partners has a significant positive effect on Iran’s trade balance. The findings of this study provide useful insights for economic policymakers in formulating effective exchange rate and trade policies aimed at improving Iran's trade balance.https://jem.semnan.ac.ir/article_9954_356dc40642abeb3a437e7e06f178701c.pdfSemnan UniversityJournal of Econometric Modelling2345-654X10220250723Economic Globalization and Sustainable Society: A Comparative Study of Nonlinear Patterns in Developed and Developing CountriesEconomic Globalization and Sustainable Society: A Comparative Study of Nonlinear Patterns in Developed and Developing Countries195241995510.22075/jem.2025.38028.2016FASamanGhaderiAssociate Professor, Department of Economics, University of Kurdistan, Sanandaj, IranAnisehAminiMaster’s graduate in Economics, Faculty of Humanities and Social Sciences, University of Kurdistan, Sanandaj, IranJournal Article20250608This study aims to analyze the nonlinear effects of economic globalization on sustainable society in developed and developing countries over the period 2000 to 2020. To this end, a composite index was first constructed to measure the state of sustainable society across countries. Subsequently, using the Panel Smooth Transition Regression (PSTR) model and considering economic globalization as the transition variable, the nonlinear relationships among variables were evaluated. The findings indicate the existence of a nonlinear relationship between economic globalization and the sustainable society index. For developed countries, the optimal model includes two thresholds and three distinct regimes, whereas for developing countries, a single threshold and two regimes were identified as the best-fitting structure. Moreover, in developed countries, economic globalization has a negative effect in the first two regimes and a positive effect in the third regime, while in developing countries, this effect is positive and significant across all regimes. The results highlight the necessity of formulating differentiated strategies tailored to the structural characteristics of each group of countries.This study aims to analyze the nonlinear effects of economic globalization on sustainable society in developed and developing countries over the period 2000 to 2020. To this end, a composite index was first constructed to measure the state of sustainable society across countries. Subsequently, using the Panel Smooth Transition Regression (PSTR) model and considering economic globalization as the transition variable, the nonlinear relationships among variables were evaluated. The findings indicate the existence of a nonlinear relationship between economic globalization and the sustainable society index. For developed countries, the optimal model includes two thresholds and three distinct regimes, whereas for developing countries, a single threshold and two regimes were identified as the best-fitting structure. Moreover, in developed countries, economic globalization has a negative effect in the first two regimes and a positive effect in the third regime, while in developing countries, this effect is positive and significant across all regimes. The results highlight the necessity of formulating differentiated strategies tailored to the structural characteristics of each group of countries.https://jem.semnan.ac.ir/article_9955_a891af9b4934fe765b5778469d1f1f45.pdf