Template-Type: ReDIF-Article 1.0 Author-Name: K. Batu Tunay Author-Email: kbatutunay@hotmail.com Author-Workplace-Name: Marmara Üniversitesi Finansal Bilimler Fakültesi Author-Name: Necla TUNAY Author-Email: necla.tunay@marmara.edu.tr Title: RISK SPILLOVER AMONG FINANCIAL MARKETS: AN ANALYSIS ON TURKEY USING DYNAMIC CONDITIONAL VARIANCE AND CORRELATIONS Abstract: The importance of risk or volatility spillover in financial markets has increased following the global crisis of 2008. It can be stated that this process becomes more pronounced, especially during periods of financial turbulence, such as shocks or crises. Given its significant impact on investor decisions and financial stability, risk spillover has been the subject of an increasing number of empirical studies in recent years. In this study, the spillover of risk or volatility in financial markets is examined through the case of Turkey. The DCC-GARCH method, which incorporates the modeling of dynamic conditional variance and correlations and is believed to yield more effective results, particularly during financial turbulence periods, has been preferred for the analyses. The study utilizes a sample covering the period 2006-2025, composed of weekly data. This dataset includes data compiled from the stock, bond, credit, foreign exchange, and gold markets. The analysis results indicate that there are strong interdependencies among financial markets in Turkey, which facilitate the transfer of not only risks but also shocks between markets. The conditional correlations gradually decline over time, suggesting that long memory effects might be strong. It has been determined that the impact of shocks on conditional correlations is weak in the short term but strong and persistent in the long run. These empirical findings demonstrate that shocks will spread rapidly and remain persistent among financial markets, regardless of whether they originate from external or local sources. In such a process, the specific financial market from which the risk or shock originates is of little importance. Regardless of the market type, the same mechanism will operate, and the transmitted shock will swiftly affect the entire system. Journal: Eurasian Econometrics Statistics & Emprical Economics Journal Pages: 68-86 Volume: 26 Issue: 26 Year: 2026 Month: Feb DOI: 10.17740/eas.stat.2025-V25-05 File-URL: https://eurasianacademy.org/index.php/econstat/article/view/1596 File-Format: Application/pdf Handle: RePEc:eas:econst:v:26:y:2025:i:26:p:68-86 Template-Type: ReDIF-Article 1.0 Author-Name: Sevilay Dinçer Author-Email: svlybstnc@gmail.com Author-Workplace-Name: EGO Genel Müdürlüğü Author-Name: Prof.Dr.Yeşim Tanrıvermiş Author-Email: yesim06@gmail.com Title: Abstract: As the population rises in Turkey, traffic density in cities increases, resulting in accidents and environmental pollution.  As a solution, new urban rail system projects are being developed in some cities.  It is essential to perform investment analysis of metro lines while transportation planning.  In this study, investment analysis of metro lines in Ankara was made.  Operated rail systems; Batıkent-Kızılay Metro Line (M1), Çayyolu-Kızılay Metro Line (M2), Batıkent-Sincan-Törekent Metro Line (M3), Keçiören-AKM Metro Line (M4), Aşti-Dikimevi (A1) lines.  The stations which planned to be done is; The metro line between Keçiören Kuyubaşı Station and YHT Terminal Station, Yıldırım Beyazıt University Çubuk Rail System Line, A1 Line (Ankaray) Dikimevi-Nato Road Rail System Extension Line.  Within the scope of the study, net present values ​​with net cash flows (NPV), payback period based on cash flow (DRS), internal rate of return (IRR) and profitability index (CI) were calculated.  KE highest metro lines; Ankaray Aşti Line (2.65 to 0.61) and M3 Line (2.16 to 0.49).  NPV highest line is M2 Line ($658,089,676.93 to -518,930,118.60$) and M2 Line ($658,089,676.93 to -518,930,118.60$), the lowest and the highest metro line payback is seen between GÖS (highest) with M1 Line (41.4 years to 46.3 years) and (lowest) Ankaray AŞTİ Line (33.3 years to 62.6).  Investment costs are high and payback periods are very long.   Journal: Eurasian Econometrics Statistics & Emprical Economics Journal Pages: 1-19 Volume: 26 Issue: 26 Year: 2026 Month: Feb DOI: 10.17740/eas.stat.2025-V25-01 File-URL: https://eurasianacademy.org/index.php/econstat/article/view/1626 File-Format: Application/pdf Handle: RePEc:eas:econst:v:26:y:2025:i:26:p:1-19 Template-Type: ReDIF-Article 1.0 Author-Name: YUNUS BUDAK Author-Email: hzlylcn.0434@outlook.com Author-Workplace-Name: AĞRI İBRAHİM ÇEÇEN ÜNİVERSİTESİ Title: THE EFFECTS OF PERCEIVED POVERTY ON SOCIAL EXCLUSION AND LIFE SATISFACTION: AN ANALYSIS USING THE STRUCTURAL EQUATION MODEL Abstract: This study analyzes the effects of individuals’ perceptions of poverty on social exclusion and life satisfaction through a Structural Equation Model (SEM). The research population consists of individuals residing in Ağrı Province who benefit from publicly supported social assistance programs. Based on data collected from 650 participants, the findings indicate that an increase in the perception of poverty significantly decreases life satisfaction while increasing social exclusion. According to the model results, a one-unit increase in the perception of poverty leads to a 0.682-unit decrease in life satisfaction and a 0.451-unit increase in social exclusion. These results demonstrate that poverty negatively affects not only individuals’ economic conditions but also their psychosocial well-being and social integration. The structural model reveals that the impact on life satisfaction is particularly stronger, emphasizing that anti-poverty policies should not be limited to income support alone but should instead adopt a holistic perspective encompassing individuals’ psychosocial well-being. In this context, it is recommended that social policies be designed with a multidimensional approach aimed at improving quality of life and strengthening social capital and participation levels. Journal: Eurasian Econometrics Statistics & Emprical Economics Journal Pages: 20-32 Volume: 26 Issue: 26 Year: 2026 Month: Feb DOI: 10.17740/eas.stat.2025-V25-02 File-URL: https://eurasianacademy.org/index.php/econstat/article/view/1683 File-Format: Application/pdf Handle: RePEc:eas:econst:v:26:y:2025:i:26:p:20-32 Template-Type: ReDIF-Article 1.0 Author-Name: Oğuz Alperen Kaya Author-Email: psk.alperenkaya@gmail.com Author-Workplace-Name: İstanbul Ticaret Üniversitesi Author-Name: Yasemin Kuş Author-Email: ybozkurt@ticaret.edu.tr Title: The Mediating Role of Workplace Fear of Missing Out in the Relationship Between Social Media Fear of Missing Out and Psychological Well-Being Abstract: While social media use among employees is an important tool for accessing information and fostering social interaction, excessive and uncontrolled use can negatively impact psychological well-being and increase the fear of missing out (FoMO). This study examines the mediating role of workplace fear of missing out and presenteeism in the relationship between social media FoMO and psychological well-being. The research sample consists of 450 white-collar employees. Correlation analysis revealed that social media FoMO was positively associated with workplace FoMO and negatively associated with psychological well-being. In addition, a negative relationship was found between workplace FoMO and psychological well-being. Results from the structural equation model showed that workplace FoMO was a significant mediator in the relationship between social media FoMO and psychological well-being. The findings indicate that workplace FoMO and presenteeism can adversely affect employees’ psychological well-being, and these relationships should be taken into account in human resources practices. Journal: Eurasian Econometrics Statistics & Emprical Economics Journal Pages: 53-67 Volume: 26 Issue: 26 Year: 2026 Month: Feb DOI: 10.17740/eas.stat.2025-V25-04 File-URL: https://eurasianacademy.org/index.php/econstat/article/view/1727 File-Format: Application/pdf Handle: RePEc:eas:econst:v:26:y:2025:i:26:p:53-67 Template-Type: ReDIF-Article 1.0 Author-Name: Ayşe Nur ADIGÜZEL TÜYLÜ Author-Email: ayse.adiguzeltuylu@iuc.edu.tr Author-Workplace-Name: Istanbul University-Cerrahpaşa Title: INTERPRETABLE CAUSAL MACHINE LEARNING EVIDENCE ON THE IMPACT OF RENEWABLE ENERGY ON CO₂ EMISSIONS Abstract: This study examines the impact of renewable energy share on per capita CO₂ emissions using a combination of machine learning-based causal inference and explainable artificial intelligence methods. The relationship between renewable energy and carbon emissions has mostly been addressed in the literature using correlation-based approaches. However, the magnitude, direction, and inter-country variability of the causal effect between renewable energy and carbon emissions remain largely unclear. This study aims to fill this gap. Analyses were conducted using a global panel dataset covering the post-1995 period. In this study, the causal effect of renewable energy share on CO₂ emissions was estimated using the Causal Forest method within the Double Machine Learning framework. Furthermore, the mechanisms behind the obtained heterogeneous treatment effects were interpreted using SHapley Additive exPlanations-based explainability analysis. The findings show that an increase in the share of renewable energy significantly and causally reduces per capita CO₂ emissions on average. The negative conditional mean of treatment effects for all observations reveals that renewable energy transition does not lead to an increase in emissions under any economic or structural conditions. However, the magnitude of the effect differs significantly between countries. Explainable causality analysis shows that energy intensity is the most dominant determinant of this heterogeneity; per capita income and industrial structure play nonlinear and context-sensitive roles. The analysis conducted for Turkey reveals that structural constraints limit the effectiveness of renewable energy transition in middle-income and energy-intensive economies. Overall, this study demonstrates the causal effect of renewable energy policies on emission reduction not only at the average level but also in a heterogeneous and explainable manner. By combining causal inference with explainable machine learning, the study offers a new and powerful empirical framework for evaluating energy and climate policies. Journal: Eurasian Econometrics Statistics & Emprical Economics Journal Pages: 126-137 Volume: 26 Issue: 26 Year: 2026 Month: Feb DOI: 10.17740/eas.stat.2025-V25-07 File-URL: https://eurasianacademy.org/index.php/econstat/article/view/1728 File-Format: Application/pdf Handle: RePEc:eas:econst:v:26:y:2025:i:26:p:126-137 Template-Type: ReDIF-Article 1.0 Author-Name: Yakup Çelikbilek Author-Email: yakup.celikbilek@istanbul.edu.tr Author-Workplace-Name: İstanbul Üniversitesi Title: BENCHMARKING FUZZY-BASED MCDM APPROACHES IN RENEWABLE ENERGY SOURCES SELECTION: A NEW INTERVAL-VALUED NEUTROSOPHIC FUZZY DEMATEL-ANP-TOPSIS FRAMEWORK Abstract: Assessing renewable energy resources requires robust multi-criteria decision-making tools capable of handling uncertainty, vagueness, and the complex interactions among sustainability-related criteria. This study provides a comprehensive comparison of several widely used fuzzy-based multi-criteria decision-making methods applied to renewable energy source evaluation, including Fuzzy DEMATEL, Fuzzy AHP, Fuzzy ANP, Fuzzy TOPSIS, Fuzzy VIKOR, Fuzzy COPRAS, Fuzzy ELECTRE, etc., and also spherical, intuitionistic or neutrosophic fuzzy variants reported in the literature. By applying each method to the same dataset, the analysis highlights the similarities, divergences, and sensitivity patterns that emerge across different fuzzy modelling perspectives. Building on these comparative insights, the study introduces a novel interval-valued neutrosophic fuzzy hybrid decision-making framework integrating DEMATEL, ANP, and TOPSIS. In the proposed model, interval-valued neutrosophic fuzzy DEMATEL is employed to capture causal relationships among criteria and determine influence weights, while interval-valued neutrosophic fuzzy ANP models interdependencies within the decision network. Finally, interval-valued neutrosophic fuzzy TOPSIS is used to generate a robust and discriminative ranking of renewable energy source alternatives. The results demonstrate that the hybrid interval-valued neutrosophic framework offers enhanced consistency, stronger representation of expert hesitation, and improved prioritization stability compared with conventional fuzzy MCDM methods. Overall, this study advances the methodological landscape of renewable energy source decision-making by both benchmarking existing fuzzy techniques and proposing an innovative interval-valued neutrosophic hybrid approach that can support more reliable and sustainable energy planning. Journal: Eurasian Econometrics Statistics & Emprical Economics Journal Pages: 138-168 Volume: 26 Issue: 26 Year: 2026 Month: Feb DOI: 10.17740/eas.stat.2025-V25-08 File-URL: https://eurasianacademy.org/index.php/econstat/article/view/1729 File-Format: Application/pdf Handle: RePEc:eas:econst:v:26:y:2025:i:26:p:138-168 Template-Type: ReDIF-Article 1.0 Author-Name: Burhan DASHDAMİROV Author-Email: burhandashdamirovan@gmail.com Author-Workplace-Name: İstanbul Aydın Üniversitesi Author-Name: Necmiye Tülin İrge Author-Email: necmiyeirge@aydin.edu.tr Title: THE IMPACT OF RELATIONAL AND OPERATIONAL RESILIENCE CAPACITY AND POLITICAL SKILL ON CAREER SATISFACTION Abstract: Employees with high resilience capacity and political skills can make positive contributions to workplace culture by supporting both their individual success and the overall success of the organization. Particularly in complex and competitive work environments, understanding the effects of these factors on career satisfaction helps leaders and human resource managers make more informed decisions regarding employee development and workplace improvement plans. In this way, significant gains can be achieved in terms of both employees' personal career goals and organizational sustainability and efficiency. the aim of this study is to examine the impact of relational and operational resilience capacity and political skill on career satisfaction using the structural equation model for 515 white-collar employees. As a result of the correlation analysis, relational and operational resilience capacity was found to have a positive and significant relationship with career satisfaction at a rate of 33.1% (r=0.331, p<0.01), while political skill was found to have a positive and significant relationship with career satisfaction at a rate of 31.8% (r=0.318, p<0.01). According to the results of the structural equation model, relational and operational resilience capacity has a positive and significant effect on career satisfaction (β=0.361, p<0.01). Similarly, political skill has a positive and significant effect on career satisfaction (β=0.330, p<0.01). Thus, hypotheses H1 and H2 have been accepted.  When examining the coefficient values, relational and operational resilience capacity emerged as a more influential factor on career satisfaction compared to political skill. Journal: Eurasian Econometrics Statistics & Emprical Economics Journal Pages: 169-186 Volume: 26 Issue: 26 Year: 2026 Month: Feb DOI: 10.17740/eas.stat.2025-V25-09 File-URL: https://eurasianacademy.org/index.php/econstat/article/view/1730 File-Format: Application/pdf Handle: RePEc:eas:econst:v:26:y:2025:i:26:p:169-186 Template-Type: ReDIF-Article 1.0 Author-Name: Tuğçe BÜLBÜL Author-Email: tgcebulbul41@gmail.com Author-Name: Selçuk KOÇ Author-Email: selcukkoc@kocaeli.edu.tr Title: DETERMINING THE OPTIMUM WEEKLY WORKING HOURS FOR TÜRKİYE Abstract: Working hours, in relation to work-life balance, employee health, and productivity, have been the subject of research in various fields, including economics, social policy, labor law, and health. Following the industrial revolution, technological advances led to a general decline in working hours, driven by a reduced need for labor and transformations in production processes. Technological advances and increased capital intensity have made production processes more efficient, making it possible to achieve the same or higher economic output (GDP) with shorter working hours. This transformation has led to the need to assess the economically optimal working hours.This study aims to determine the optimal weekly working hours at both general and sectoral levels for Türkiye by using the CES (Constant Elasticity of Substitution) production function model. The analysis was conducted using quarterly data from the 2009 - 2023 on GDP, average weekly working hours, and technology levels. The findings indicate that optimal working hours vary by sector and technology level, and that as the level of technology increases, the optimal working time decreases. In the analysis of the optimal weekly working hours that maximize GDP, it was determined that the parameters were significant in the industry and service sectors, and that in the service sector, which meets the model assumptions, the optimal weekly working time was approximately 46 hours and 32 minutes. Considering that the average weekly working time in the sector is 51 hours and 43 minutes, this resulthighlight the need for adopting efficiency-focused work policies to enhance productivity. Journal: Eurasian Econometrics Statistics & Emprical Economics Journal Pages: 33-52 Volume: 26 Issue: 26 Year: 2026 Month: Feb DOI: 10.17740/eas.stat.2025-V25-03 File-URL: https://eurasianacademy.org/index.php/econstat/article/view/1731 File-Format: Application/pdf Handle: RePEc:eas:econst:v:26:y:2025:i:26:p:33-52 Template-Type: ReDIF-Article 1.0 Author-Name: Kutluk Kağan Sümer Author-Email: kutluk@istanbul.edu.tr Author-Workplace-Name: İstanbul Üniversitesi Title: A NEW SPRING FOR STATISTICAL METHODS: LARGE LANGUAGE MODELS (LLMS) Abstract: Large Language Models (LLMs) are the cornerstone of modern AI systems capable of humanlike reasoning, language understanding, and text generation. Their success relies not only on deep learning architectures but also on a comprehensive statistical foundation. This article provides an extensive examination of statistical techniques underlying LLMs, including probability theory, statistical learning theory, Bayesian inference, Markov chains, the Expectation–Maximization algorithm (EM), dimensionality reduction (PCA, SVD), probabilistic graphical models, variational inference, and sampling methods such as MCMC. It further explains how these methods are integrated within the Transformer architecture and contemporary LLM training pipelines. Applications in natural language processing, healthcare, finance, and law are also explored in detail. Journal: Eurasian Econometrics Statistics & Emprical Economics Journal Pages: 87-125 Volume: 26 Issue: 26 Year: 2026 Month: Feb DOI: 10.17740/eas.stat.2025-V25-06 File-URL: https://eurasianacademy.org/index.php/econstat/article/view/1732 File-Format: Application/pdf Handle: RePEc:eas:econst:v:26:y:2025:i:26:p:87-125