Search Results (302)

This study investigates the determinants and predictability of stock return volatility by integrating firm-specific financial characteristics with advanced econometric and volatility modeling techniques. Using an unbalanced panel dataset comprising 1596 firms and 19,752 firm-year observations from MENA stock markets over the period 2010–2024, the analysis employs fixed-effects panel regression models, conditional volatility models, and machine learning-based forecasting approaches. Following extensive diagnostic testing, including tests for heteroskedasticity, serial correlation, cross-sectional dependence, and model specification, a two-way fixed-effects model with Driscoll–Kraay standard errors is adopted as the preferred estimation framework. The results indicate that liquidity ratio, cash ratio, sales growth, firm age, lagged volatility, and lagged returns are significant determinants of stock return volatility, whereas leverage, tangibility, board independence, firm size, Tobin’s Q, and profitability do not exhibit statistically significant effects after controlling for firm-specific and time-specific heterogeneity. The volatility analysis reveals substantial persistence in stock return volatility, with the EGARCH-t specification providing the best fit among the competing GARCH-family models according to the Akaike Information Criterion. The estimated asymmetry parameters indicate that volatility responds differently to positive and negative shocks, supporting the presence of asymmetric volatility dynamics and the suitability of asymmetric volatility models. The forecasting analysis shows that advanced machine learning and deep learning models achieve competitive predictive performance; however, differences in predictive accuracy across models are generally modest. Full article

This study examines the determinants of carbon dioxide (CO₂) emissions across 25 European Union countries over the period 2000–2021, with particular emphasis on the roles of environmental taxation and green innovation in shaping environmental sustainability. The analysis is grounded in ecological modernization theory, endogenous growth theory, and the Environmental Kuznets Curve hypothesis, which collectively explain the long-run and dynamic interactions between environmental policy, economic activity, structural transformation, and environmental outcomes. To ensure robust empirical inference, this study applies a comprehensive econometric framework that accounts for cross-sectional dependence, heterogeneity, non-stationarity, cointegration, and endogeneity. The empirical strategy begins with Pesaran cross-sectional dependence tests and slope heterogeneity diagnostics, followed by second-generation panel unit root tests (Pesaran CADF/CIPS) and Westerlund cointegration tests to establish the existence of long-run equilibrium relationships among the variables. Long-run coefficients are estimated using Fully Modified Ordinary Least Squares (FMOLS), Dynamic Ordinary Least Squares (DOLS), Canonical Cointegrating Regression (CCR), and Common Correlated Effects Mean Group (CCEMG) estimators. In addition, the Panel Autoregressive Distributed Lag (ARDL) model is employed to capture both short-run dynamics and long-run adjustment processes, while the System Generalized Method of Moments (System GMM) estimator addresses potential endogeneity, reverse causality, omitted variable bias, and dynamic persistence in CO₂ emissions. The empirical results indicate that environmental taxation has a positive and statistically significant association with CO₂ emissions, suggesting that current fiscal environmental policies in EU-25 countries may not yet be sufficiently effective in discouraging pollution-intensive activities. In contrast, green innovation is found to significantly reduce CO₂ emissions, underscoring the critical role of innovation-driven environmental investment and technological progress in improving environmental quality. Economic growth, exports, and urbanization are associated with higher emissions, while imports contribute to emission reductions, reflecting differences between domestic production-based effects and trade-related structural adjustments. The System GMM results further confirm the persistence of CO₂ emissions over time and validate the robustness of the long-run relationships identified by alternative estimators. Likewise, the CCEMG and Panel ARDL results support the stability and consistency of the findings under conditions of cross-sectional dependence and heterogeneous country dynamics. Taken together, the results highlight the importance of integrating environmental taxation with green innovation policies, innovation-driven investment, and sustainable trade policies to achieve long-term emission reductions in the European Union. This study contributes to the environmental economics literature by providing robust empirical evidence using second-generation panel econometric techniques that explicitly address cross-sectional dependence, heterogeneity, and endogeneity in the analysis of environmental sustainability. Full article

In an increasingly digital global economy, cybersecurity capacity has become a key determinant of national resilience, economic competitiveness, and digital trust. However, preparedness remains uneven across the Middle East and North Africa (MENA), where levels of economic integration, governance quality, and institutional stability vary significantly. This paper examines the relationship between cybersecurity capacity, governance indicators, and international trade in selected MENA countries over the period 2010–2023. It evaluates whether rule of law and political stability are associated with cybersecurity capacity, whether trade openness predicts cybersecurity development, and whether cybersecurity capacity is dynamically associated with trade openness. The empirical analysis applies panel-data techniques, including panel unit-root tests, Pedroni cointegration tests, and the Toda–Yamamoto predictive causality framework within a multivariate VAR structure. Panel fixed-effects regressions with Driscoll–Kraay robust standard errors are also estimated to capture contemporaneous relationships while accounting for heteroskedasticity, serial correlation, cross-sectional dependence, and country-specific heterogeneity. The findings provide indicative evidence of a statistically significant bidirectional predictive relationship between trade openness and cybersecurity capacity. Greater trade integration appears to stimulate investment in secure digital infrastructure, while enhanced cybersecurity capacity may support trade expansion by strengthening digital trust and reducing transaction risks. In contrast, governance indicators do not exhibit consistent dynamic predictive relationships within the causality framework. The absence of cointegration indicates that cybersecurity capacity, governance indicators, and trade openness do not evolve within a stable long-run equilibrium relationship during the sample period. This finding may reflect the heterogeneous and policy-sensitive nature of digital infrastructure development across MENA countries. Full article

The rapid expansion of digital payment systems has reshaped household consumption dynamics, yet their interaction with financial development remains insufficiently understood. While digital infrastructure and financial deepening are both associated with improved consumption-related activity, their joint effects may vary across economic environments. Using an unbalanced panel of G20 countries over the period 2005–2023, this study examines the direct and conditional effects of digital infrastructure and financial development on household consumption dynamics. The empirical analysis employs second-generation panel techniques, including the Cross-sectionally Augmented IPS (CIPS) unit root test, the Westerlund cointegration approach, and the Common Correlated Effects Mean Group (CCE-MG) estimator, which accounts for cross-sectional dependence and heterogeneity. The results indicate that both internet usage and financial development are positively associated with household consumption. However, the interaction term is negative and statistically significant, suggesting that the marginal effect of digital infrastructure weakens as financial development increases. Robustness checks further indicate that this relationship is primarily associated with domestic consumption dynamics and does not extend to trade openness. These findings highlight the conditional relationship between digital infrastructure and financial development, suggesting that the economic implications of digital transformation depend on the broader financial environment. Full article

This study investigates the determinants of economic growth in the Next-11 economies over the period 1996–2024, with particular emphasis on the roles of structural, institutional, and human capital factors. Using a comprehensive panel dataset for eleven emerging economies, the analysis employs three robust estimation techniques—Driscoll–Kraay Standard Errors (DKSEs), Feasible Generalized Least Squares (FGLSs), and Panel-Corrected Standard Errors (PCSEs)- to address common econometric issues such as heteroskedasticity, serial correlation, and cross-sectional dependence. The empirical results reveal that industrial output, energy consumption, human capital, institutional quality, and foreign direct investment significantly contribute to economic growth. Among these factors, industrial output and energy consumption exhibit particularly strong and consistent positive effects across all estimation methods, highlighting the importance of structural transformation and energy availability in supporting economic expansion. In contrast, trade openness shows a negative and statistically significant relationship with economic growth in most model specifications, suggesting that structural constraints, import dependence, and limited domestic productive capacity may restrict the growth benefits of external integration in these economies. The study also explores the conditional effects of foreign direct investment through interaction terms with human capital and institutional quality. The findings indicate that the growth-enhancing impact of foreign investment depends significantly on domestic absorptive capacity, particularly the availability of skilled labor and effective governance structures. These results emphasize the importance of complementary policies aimed at strengthening education systems, improving institutional quality, and enhancing regulatory effectiveness. From a policy perspective, the findings suggest that the Next-11 economies should prioritize industrial development, energy infrastructure expansion, human capital investment, and institutional reforms to maximize the benefits of globalization and foreign investment. Overall, the study contributes to the literature by providing robust empirical evidence on the interconnected roles of structural, institutional, and human capital factors in shaping economic growth in emerging economies. Full article

Rooftop photovoltaic (PV) potential assessments have advanced significantly through high-resolution geospatial methods. However, most studies remain focused on well-planned urban environments and primarily consider geometric or radiative factors, often neglecting material constraints and deployment realism in heterogeneous cities of the Global South. This study addresses these gaps by developing an automated LiDAR- and GIS-based methodology to estimate rooftop PV potential in Cartagena, Colombia, explicitly integrating cadastral constraints, geometric feasibility, and roof material exclusion. The workflow combines LiDAR-derived elevation data, parcel-based segmentation, slope and aspect filtering, and post-processing techniques to identify PV-suitable rooftops, validated against 482 manually delineated polygons. The optimal configuration (45° slope threshold; 0.25 m buffer) achieved RMSE values of 6.79° (slope) and 20.95° (aspect). A geometry-constrained panel fitting algorithm estimated 3,599,631 panels across 146,091 rooftops, representing 7.06 km² of suitable area. Compared to simple area-based methods, this approach reduced capacity estimates by approximately 15.3%, demonstrating the importance of geometric realism. A key contribution is the integration of asbestos-cement (AC) roof exclusion, which reduced suitable rooftop area by ~65%, resulting in a final capacity of 1,281,202 panels. Estimated annual generation decreased from 1891.9 GWh/year to 673.4 GWh/year, equivalent to supplying 53.4–126.8% of Cartagena’s households. The proposed methodology provides a scalable framework for realistic urban PV assessment and introduces a dual-purpose planning tool that enables authorities to both prioritize solar deployment and identify areas requiring roof remediation, supporting safer and more controlled energy transitions in developing-country cities. Full article

In the preliminary design of aircraft structures, efficient modelling techniques are essential to balance accuracy and computational cost. Shear Panel Theory (SPT) offers a simple yet effective idealisation of thin-walled, stiffened structures such as wings. It captures more structural detail—like ribs, sweep and taper—than traditional beam idealisation and would otherwise require detailed finite element analysis. However, compared to a finite element model, the degrees of freedom of the structure as well as the meshing effort are significantly reduced, as SPT idealisation uses a structural element approach. This improves mass estimation and structural response calculation and makes SPT particularly well-suited for optimisation tasks in early design phases. This work presents a methodology to derive structural properties of wing segments based on NACA airfoils using SPT. This offers adjustment of the wing’s geometry for use in aeroelastic analysis and enables fast evaluation of structural behaviour and gradient computation, supporting integration into multidisciplinary design optimisation frameworks. The proposed methodology advances the use of idealised structural models in aircraft design by bridging the gap between high-fidelity analysis and system-level aeroelastic simulations, supporting faster and more informed early design iterations. Full article

The NEET category refers to the proportion of young people who are neither employed nor in education or training. The success of Sustainable Development Goal 8 largely depends on reducing the number of NEETs, one of its sub-goals. This study examines the long-term impact of gross domestic product, human development, social globalization, and patent applications on NEET in eight EU countries during 1991–2021, within the framework of SDG 8. For long-run estimation, the study employs panel data techniques that account for cross-sectional dependence and heterogeneity, specifically the Augmented Mean Group (AMG) and Regularized Common Correlated Effects (RCCE) estimators. According to country-specific findings, PA has a statistically significant effect in reducing NEET rates in France and Spain, while human development has a similar effect in Portugal. In contrast, economic growth and social globalization do not exhibit statistically significant effects on NEET rates at the country level. The results underscore that, in high-income EU countries, policies designed to simultaneously enhance human development and innovation capacity are central to tackling the NEET issue, consistent with the objectives of Sustainable Development Goal 8. The study contributes to the literature by providing a comparative empirical assessment of NEET determinants within a framework that accounts for cross-country heterogeneity and multiple structural factors. Full article

This study rigorously examines the impact of political instability (POI) on firm performance (FPER) using high-dimensional panel data from 2006 to 2022, drawn from the World Bank Enterprise Survey (WBES) for 60 economies. Using advanced machine learning-based econometric techniques, including Double-Selection LASSO Regression (DSLR) and Partialing-Out LASSO Regression (POLR), the analysis reveals that POI significantly reduces FPER across the sampled countries. These findings remain robust across a series of validation tests, including alternative estimation approaches such as Cross-Fit Partialing-Out LASSO Regression (CF-POLR) and Bayesian Model Averaging (BMA), the use of alternative FPER proxies—employment growth (FEMG), innovation (IINN), and labor productivity (FLP)—and the substitution of POI with government regulation (REG). Mediation analysis further indicates that operational costs (OCOST) and firm investment (FINV) significantly and partially mediate the total effect of POI on FPER. In contrast, financial constraints (FCST) do not emerge as a significant mediator. The moderation analysis shows that political connections (PC) substantially attenuate the negative impact of POI on FPER. Heterogeneity analyses demonstrate that small, young, and capital-intensive firms are more severely affected by POI than medium and large, older, technology-intensive, and labor-intensive firms. Additionally, firm ownership-based heterogeneity indicates that state-owned enterprises experience slightly stronger adverse effects from fluctuations in POI than non-state-owned firms. Based on these empirical insights, policymakers need to promote institutional stability and provide direct support for vulnerable young and small firms to reduce the adverse effects of POI on FPER. Ultimately, this boosts economic flexibility in politically unstable markets by managing key growth drivers. Full article

This study examines the methodological landscape and empirical trends in energy elasticity estimation, reviewing literature published from 2017 to 2026. It employs a hierarchical taxonomy to categorize the selected articles by functional form (production, cost, and energy demand), model specification (static and dynamic), and data structure (panel and time-series). Key findings include: First, the review distinguishes production, cost, and energy demand function approaches and synthesizes the literature by showing how these approaches are associated with different data structures and econometric modeling strategies. Second, the analysis highlights the prevalent use of econometric techniques, such as Generalized Method of Moment (GMM) and Autoregressive Distributed Lag (ARDL), to effectively address endogeneity and non-stationarity in recent energy data. Finally, the contribution of this study lies in its structured synthesis of diverse econometric methodologies based on specific data characteristics. By providing a structured roadmap that matches estimation techniques with their corresponding data structures, this review offers guidance for researchers and policymakers to ensure robust and reliable energy elasticity estimates in an era of rapid global energy transitions. Full article

Achieving sustainable development requires moving beyond pollution metrics to holistic measures, such as the load capacity factor (LCF), which balances ecological demand and supply. While recent studies have provided important insights into the determinants of LCF in OECD countries, further research is needed to incorporate additional determinants and updated estimation approaches. This study addresses this gap by examining the impacts of financial innovation, forestry, urbanization, population, and economic growth on the LCF in Organization for Economic Cooperation and Development (OECD) economies from 1990 to 2023. Using second-generation panel econometric methods, including tests for cross-sectional dependence, slope heterogeneity, second-generation unit roots, and cointegration techniques, this paper confirms a stable long-run relationship among the variables. The core analysis applies the method of moments quantile regression to uncover the heterogeneous effects across the LCF distribution. The results indicate that financial innovation consistently enhances the ratio of biocapacity to ecological footprint. In contrast, economic growth and urbanization exert significant negative pressure on the LCF, whereas population size shows a uniformly detrimental effect. Forestry has a positive but less pronounced influence. Robustness checks using fully modified ordinary least squares, dynamic ordinary least squares, and panel-corrected standard errors confirm these results. The present study concludes that targeted financial innovation and stringent urban demographic policies support OECD nations in improving ecological balance and reducing ecological deficits. Full article

Exchange rate volatility has intensified in recent decades, yet its systematic implications for banking-sector stability remain contested. This study investigates whether exchange rate volatility constitutes a meaningful source of financial fragility using a global panel of 103 countries over the period 2000–2021. Financial stability is proxied by the banking-sector Z-score, while exchange rate volatility is estimated using a EGARCH-based framework to capture time-varying uncertainty. To address cross-sectional dependence, heterogeneity, and endogeneity, the analysis employs Driscoll–Kraay fixed effects, two-step system GMM, and quantile regressions. The results reveal that exchange rate volatility exerts a statistically and economically significant negative effect on banking stability, reducing Z-scores across countries and income groups. The findings remain robust across alternative specifications and estimators. Bank-level fundamentals—capitalisation, liquidity, and credit—enhance stability, whereas higher non-performing loans and risk exposure amplify fragility. Macroeconomic conditions also matter, with stronger growth, institutional quality and external balances supporting resilience, while inflation, economic policy uncertainty and expansionary government spending weaken stability. By integrating time-varying volatility modelling with dynamic panel techniques in a large cross-country setting, this study provides new global evidence that exchange rate volatility is not merely a macroeconomic fluctuation but a structural source of banking-sector risk. The findings carry important implications for macroprudential policy, foreign-exchange management, and coordinated monetary–fiscal responses aimed at safeguarding financial stability in open economies. Full article

This study investigates the determinants of GDP per capita across 26 European Union member states over the period of 2006–2024, with a particular focus on entrepreneurship, institutional quality, and macroeconomic factors. Given the presence of long-run income differences across EU countries, the analysis explicitly accounts for structural heterogeneity in economic development and institutional capacity. To ensure robust estimation in the presence of cross-sectional dependence and slope heterogeneity, the study employs advanced panel econometric techniques, including tests for cross-sectional dependence, unit roots, and cointegration. Long-run relationships and short-run dynamics are estimated using the Cross-Sectionally Augmented Autoregressive Distributed Lag (CS-ARDL) model, complemented by robustness checks based on the Augmented Mean Group (AMG) and Common Correlated Effects Mean Group (CCEMG) estimators. In addition, the Method of Moments Quantile Regression (MMQR) is applied to capture heterogeneity across different points of the income distribution, thereby reflecting long-run income disparities among EU member states. The empirical results confirm the existence of a stable long-run equilibrium relationship among the variables. The baseline CS-ARDL estimates indicate that institutional quality, entrepreneurial activity, trade openness, and government expenditure exert positive and statistically significant effects on GDP per capita, while financial development exhibits a negative effect and foreign direct investment remains insignificant. In the short run, entrepreneurship and trade openness contribute positively to GDP per capita, whereas government expenditure and credit expansion generate contractionary effects. The robustness analysis using AMG and CCEMG estimators largely supports these findings, as the direction of the coefficients remains consistent across alternative specifications, although some variation in statistical significance is observed due to differences in the treatment of cross-sectional dependence and unobserved common factors. The MMQR results further reveal substantial heterogeneity across the income distribution, indicating that the effects of key determinants vary depending on countries’ long-run income levels. In particular, trade openness and institutional quality exert stronger positive effects in lower-income quantiles, while the adverse effects of excessive financial development are more pronounced in higher-income quantiles. Overall, the findings underscore the importance of promoting productive entrepreneurship, strengthening institutional frameworks, facilitating trade integration, and ensuring efficient financial intermediation to enhance GDP per capita within the European Union. The results also highlight the need for differentiated policy approaches that explicitly account for long-run income heterogeneity, structural differences, and varying institutional capacities across EU member states. Full article

This study investigates the impact of revenue diversification on the performance and stability of listed Tunisian banks over the period 2008–2023, with the objective of assessing whether diversification strategies enhance bank performance and promote financial stability in an emerging-market context. The analysis relies on a panel dataset of Tunisian listed banks and employs a two-stage least squares (2SLS) estimation approach to address potential endogeneity issues, using ownership structure as an instrumental variable. Bank performance is measured by Return on Assets (ROA) and Net Interest Margin (NIM), while financial stability is captured by the Z-score. The empirical results show that revenue diversification has a positive and significant effect on bank performance, as measured by ROA, and on financial stability. However, it exerts a negative and significant impact on NIM, indicating that although diversification improves overall performance and strengthens stability, it may weaken traditional intermediation income. This study contributes to the limited literature on banking in emerging markets by jointly examining performance and stability effects while addressing endogeneity concerns through robust econometric techniques, and by providing new evidence from the Tunisian banking sector, which has experienced significant political and economic disruptions during the study period. Full article

This study explores the drivers of economic growth in the BRICS economies—Brazil, Russia, India, China, and South Africa—over the period 1994–2024, focusing on the roles of digital infrastructure and financial development. Using a balanced panel, we examine how internet connectivity and access to credit shape growth, both independently and in combination, while accounting for gross fixed capital formation, urbanization, and government expenditure. Given the macro-panel structure, which exhibits heteroskedasticity, serial correlation, and cross-sectional dependence, we employ robust estimation techniques, including Driscoll–Kraay standard errors (DKSE), Feasible Generalized Least Squares (FGLS), and Panel-Corrected Standard Errors (PCSE). To capture potential heterogeneity across different growth scenarios, we further apply the Method of Moments Quantile Regression (MMQR) as a robustness check. Our findings show that both internet connectivity and financial development consistently promote economic growth across all main specifications. Importantly, the interaction between these two factors is also significant, indicating that the benefits of digital infrastructure are stronger in countries with deeper financial systems, and vice versa. Among the control variables, capital accumulation and government spending positively contribute to growth, while urbanization exhibits a negative association, reflecting the structural challenges of rapid urban expansion. MMQR results confirm that these relationships hold across low-, medium-, and high-growth periods, highlighting their broad relevance. These findings highlight the synergistic role of technological and financial development and underscore the importance of integrated policies to sustain long-term, inclusive growth in the BRICS economies. This study suggests that policymakers should adopt integrated strategies that enhance digital connectivity, deepen financial development, and support productive public investment to sustain inclusive and resilient economic growth. Full article