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A high level of imports indicates robust domestic demand and a growing economy, and trade facilitation as one of the important trade methods that insure efficiency of trade movement between the borders. 11 trade facilitation indicators were used from 40 selected developing countries from different regions over a period from 2015 to 2019, GMM was used to examine how trade facilitation impacts the economy and imports of a country. In our study, 10 trade facilitation indicators recorded a positive impact on imports. The findings of this study indicate that it would be progressive for developing nations to adopt a well-functioning trade facilitation measures and must work on reviewing their law and policies to adopt the new knowledge of facilitating things in line with current best practices and conduct a reformation of existing policies and national development strategies for trade to further their economic enhancement agenda, which can shorten clearance time and increase the import volume. [ABSTRACT FROM AUTHOR]

The lack of precise and comprehensive information about the health of bridges, and in particular long span ones, can lead to incorrect decisions regarding maintenance, repair, modernization, and reinforcement of the structure itself. While the consequences of inadequate interventions are quite apparent, incorrect decisions can also result in unnecessary or misdirected actions. For example, an inadequate assessment of the structural health can lead to the modernization and replacement of some components that are still sound. Structural Health Monitoring (SHM) involves the use of time series derived from periodic measurements of the structure's behavior, considered in its operational and load environment. The goal is to determine its response to various solicitations and, in particular, to highlight any critical issue in the structure's behavior that may affect its reliability and safety due to anomalies and deterioration. This paper proposes an SHM method applied to the Valgadena bridge, one of the tallest viaducts in Italy and Europe (maximum height 160 m), located on the Altopiano dei Sette Comuni in the Province of Vicenza. Despite the fact that the viaduct itself had already been monitored during its construction using classical geometric leveling techniques, the methodology proposed here is based instead on the use of affordable dual-frequency GNSS (Global Navigation Satellite System) receivers to determine static and dynamic components of the bridge movements. Specifically, an effective combination of time series analysis methods and machine learning techniques is proposed in order to determine the vibration modes of the monitored viaduct. Monitoring is performed in regular operation conditions of the bridge (operational modal analysis (OMA)), and the use of certain machine learning methods aims at supporting the development of an effective automatic OMA procedure. To be more specific, the random decrements technique is used in order to make the vibration characteristics of the collected signals more apparent. Time-domain-based subspace identification is applied in order to determine a proper model of the collected measurements. Then, clustering methods, namely DBSCAN (Density-Based Spatial Clustering of Applications with Noise) and GMMs (Gaussian Mixture Models), are used in order to reliably estimate the system poles, and hence the corresponding vibration characteristics. The performance of the considered methods is compared on the Valgadena bridge case study, showing that the use of GMM clustering reduces, with respect to DBSCAN, the impact of the choice of certain parameter values in the considered case. [ABSTRACT FROM AUTHOR]

This study aims to examine how female CEOs influence green innovation and whether firm size moderates this connection. Our paper focuses on CEOs, who are considered the strategic leaders of corporations, because of their crucial role in making important decisions. This research paper examines how female CEOs influence green innovation (GI) in the Asian industrial sector. The primary goal is to address these research questions: Do Asian industrial firms with female and male CEOs differ in their GI efforts? Is there a positive moderating influence of Asian industrial enterprises' size on the nexus between women in CEO positions and eco-innovation? Based on our research questions, firm size is likely a determining factor in the GI of female CEOs. This research employs rigorous econometric modeling to analyze a substantial dataset of listed Asian industrial companies from 2013 to 2022. We have found a significant positive correlation between female CEOs and GI in Asian industrial firms. It has been proven that female CEOs in the industrial sector are more inclined to promote environmentally friendly practices. Furthermore, the size of an industrial firm amplifies the beneficial influence of a female CEO on the firm's chances of engaging in GI initiatives. Regarding the moderating effect of size, the size of companies significantly magnifies the impact of female CEOs on GI. The effectiveness of female CEOs on environmentally friendly practices is more prominent in large corporations than in smaller ones. Our outcomes remain robust with respect to endogeneity issues using two-step GMM estimators. This study proposes that stakeholders, particularly in Asian countries, should promote the increased representation of females in CEO roles, particularly within large corporations. This is because women-led companies demonstrate superior performance in GI endeavors. Hence, regulators must establish policies that facilitate the participation of women in CEO positions within large-scale enterprises. These policies may strengthen the private sector's capacity to foster sustainable innovation. [ABSTRACT FROM AUTHOR]

The study aims to empirically verify the hypotheses that the increase in the share of women legislator in politics has the potential to reduce levels of corruption within the Parliament and overall political corruption levels. Using data from the Varieties of Democracy (V-Dem) platform, fixed effects, and generalised method of moments (GMM-System) models are estimated for a set of 154 countries between 1995 and 2018. The results show that when considering corruption within the legislative house, the greater share of women in politics is associated with lower levels of corruption. Even after controlling for various societal factors, the results confirm the positive effect of women's participation in parliament on the decline of corruption. Yet, this effect does not seem to spill over into other branches of government and sectors of the public sphere. When considering the level of more general corruption, it responds much more to variations in structural aspects, as democratic maturity and equality in the distribution of political power among different socioeconomic strata, than to women's participation in parliamentary seats. [ABSTRACT FROM AUTHOR]

Errors-in-variables (EIV) biases plague asset pricing tests. We offer a new perspective on addressing the EIV issue: instead of viewing EIV biases as estimation errors that potentially contaminate next stage risk premium estimates, we consider them to be return innovations that follow a particular correlation structure. We factor this structure into our test design, yielding a new regression model that generates the most accurate risk premium estimates. We demonstrate the theoretical appeal as well as the empirical relevance of our new estimator. This paper was accepted by Victoria Ivashina, finance. Supplemental Material: The supplemental appendix and data files are available at https://doi.org/10.1287/mnsc.2023.4966. [ABSTRACT FROM AUTHOR]

Pursuing sustainable development has emerged as a paramount global objective, with the United Nations' Sustainable Development Goals (SDGs) serving as a guiding framework. Increasing environmental degradation is associated with increased greenhouse gas emissions, deforestation, industrial waste, fossil fuel depletion, and loss of biodiversity. This study has analyzed the effect of financial inclusion on ecological sustainability while controlling the effect of financial development, energy consumption, economic growth, urbanization, and trade openness in the Next‐11 economies from 1995 to 2019. The study develops the financial inclusion index through principal components analysis by inculcating three demand‐side and three supply‐side factors of financial inclusion. To analyze the developed model, various first‐generation and second‐generation techniques are applied. The results of Westerlund co‐integration reveal significant long‐run co‐integration among the series. The long‐run elasticities are estimated through dynamic common correlated effect estimation and generalized method of moments where results reveal that FI helps to secure environmental sustainability by reducing the ecological footprint, hence it works on the SDG framework. Economic growth and financial development are found to be the root causes of increasing ecological footprint. So, it is the penetration of excessive credit, not the financial inclusivity which needs to be directed toward sustainability. It is suggested that the governments of Next‐11 countries should steer the capital toward sustainable usage and should continue facilitating financial services to curtail environmental degradation. Our findings have significant repercussions for Next‐11 countries, giving decision makers fresh perceptions of financial inclusion and development implications. [ABSTRACT FROM AUTHOR]

This research investigates how female CEOs and board gender composition (BGC) influence environmental innovation. Using a panel dataset of 237 energy companies, the study reveals that female CEOs are more committed to the environment than males. Interestingly, the findings also show that the BGC positively moderates the female CEO and environmental innovation nexus. Additionally, it reveals that female CEOs have a more significant influence on promoting environmentally friendly innovation in profitable energy companies than males. The findings remain strong after various robustness tests, contributing to the ongoing debate on gender equality and offering novel insights into the green footprint. Article Highlights: The study reveals that female CEOs significantly drive environmental innovation strategies in energy companies. The presence of women on corporate boards significantly magnifies the effect of female CEOs on environmental innovation. Practical implications for ecological innovation highlight the necessity of female leadership in both positions. [ABSTRACT FROM AUTHOR]

Attaining explicit hazard estimates is a challenging task for data sparse regions such as the Peninsular India. Physics based probabilistic seismic hazard analysis (Pb-PSHA) has gained momentum in recent years as a viable solution to this issue. While performing a site-specific analysis in data-sparse regions, instead of incorporating ground motion models (GMMs) from other regions in the hazard methodology, the Pb-PSHA involves obtaining physics-based numerical simulations. In the present study, Pb-PSHA is carried out for the entire southern Peninsular India, with a detailed demonstration for the Kalpakkam site, Tamilnadu. Due to absence of any data on local fault characteristics and past rupture models, simulations are derived using the spectral element method, for several source rupture scenarios. Further, the stochastic seismological model is used to simulate for high frequency (1-100 Hz) ensemble ground motions. Broadband ground motions are then obtained by combining the results from the deterministic model i.e., low frequency (0.01-1 Hz) simulations and the stochastic model. Further, PSHA based on elliptical gridded seismicity is carried out to obtain hazard curves for spectral accelerations. The ensuing uniform hazard response spectra are compared against the outcome of traditional PSHA involving a global GMM. The results indicate that the PGA values obtained from the Pb-PSHA are slightly higher than that of the global GMM-based PSHA. [ABSTRACT FROM AUTHOR]

Anwar Shaikh has proposed the classical theory of inflation in his recent book Capitalism, Competition, Conflict, Crisis. Even though it is a relevant and well-founded heterodox theory, the empirical literature on the subject is scanty. In this article, we empirically evaluate the explanatory capabilities of Shaikh's theory of inflation for the case of Europe. We constructed GMM and Fixed Effects models for the panel of 23 European countries over the period 2001–20. The overall results demonstrated that Shaikh's classical theory of inflation generated empirically successful results in explaining the supply dynamics of European inflation, while it produced no statistically significant effect on the demand dynamics of inflation due to the European inflation level, as expected. [ABSTRACT FROM AUTHOR]

Machine learning (ML) has been used in human gait data for appropriate assistive device prediction. However, their uptake in the medical setup still remains low due to their black box nature which restricts clinicians from understanding how they operate. This has led to the exploration of explainable ML. Studies have recommended local interpretable model‐agnostic explanation (LIME) because it builds sparse linear models around an individual prediction in its local vicinity hence fast and also because it could be used on any ML model. LIME is however, is not always stable. The research aimed to enhance LIME to attain stability by avoid the sampling step through combining Gaussian mixture model (GMM) sampling. To test performance of the GMM‐LIME, supervised ML were recommended because study revealed that their accuracy was above 90% when used on human gait. Neural networks were adopted for GaitRec dataset and Random Forest (RF) was adopted and applied on HugaDB datasets. Maximum accuracies attained were multilayer perceptron (95%) and RF (99%). Graphical results on stability and Jaccard similarity scores were presented for both original LIME and GMM‐LIME. Unlike original LIME, GMM‐LIME produced not only more accurate and reliable but also consistently stable explanations. [ABSTRACT FROM AUTHOR]

The rapid development of China's digital economy has enabled China to lead the world in financial technology (FinTech). In this context, it is imperative to study the impact of FinTech at the macro level on the sources of R&D financing for micro-enterprises. Using the data of A-share listed companies on the main boards of China's Shanghai and Shenzhen cities and the municipal-level FinTech development index from 2011 to 2020, this paper conducts an empirical test by applying the system generalized method of moments estimation (system GMM). Fintech facilitates firms' external financing of R&D. There is significant heterogeneity across different types of firms, with fintech facilitating R&D financing more strongly for young and non-state firms. This study not only complements the literature on the impact of fintech on R&D financing but also has essential practical guidance significance, which can provide valuable guidance and assistance to different types of enterprises in their R&D financing decision-making process. [ABSTRACT FROM AUTHOR]

This study investigates the effect of financial inclusion (FI), considering micro and macro indicators as well as micro- and macro-FI separately, on the stability of Asian banks and examines the moderating effect of bank competition (BC) on this relationship. Using data from 2011 to 2021, this study examines the relationship between FI, BC, and bank stability (BS). The hypotheses were tested using a "two-step system-GMM framework". The findings were also authenticated using the panel OLS approach. The results indicate that FI (considering micro- and macro-indicators) and micro- and macro-FI have significant positive effects on the stability of Asian banks. However, the impact of micro-FI is greater than that of macro-FI on the BS in Asia. Furthermore, the results manifest that BC has a significant positive impact on BS and positively moderates the relationship between micro-FI and BS, whereas it negatively moderates the relationship between macro-FI and BS. The findings of this study have practical implications for regulators, bankers, and policymakers involved in formulating strategies to enhance Asian banks' stability. [ABSTRACT FROM AUTHOR]

This study analyzed data from the Korea Welfare Panel Study to identify changing patterns in social exclusion among South Korean youth and determine the influencing factors. The study examined 345 youth from 2012 to 2021. A fuzzy set theory and growth mixture model was used to measure social exclusion and identify patterns and the associated factors during this timeframe. The main findings are as follows. First, the social exclusion gap among South Korean youth that emerged a decade ago has continued, with high, middle, and low levels of exclusion identified. Second, the analysis revealed that looking at the demographics, being female, having less education and lower personal and household income increased the likelihood of falling into a relatively higher level of social exclusion. Based on the results, policy recommendations are suggested to mitigate the social exclusion gap among Korean youth. [ABSTRACT FROM AUTHOR]

With the widening saving-investment gap and the limited domestic financial resources to drive development imperatives in Sub-Saharan Africa (SSA), foreign direct investment (FDI) is considered a viable and sustainably promising option for boosting employment generation and closing gender gaps in employment. This paper provides critical insights into the gender and age-based employment effect of FDI in SSA and the role of institutional quality in shaping the relationship. The two-step generalized method of moments modelling framework was adopted to analyse relevant data of 29 SSA countries over the 2000-2021 period. The results revealed that FDI has a significant employment-enhancing effect irrespective of gender and age considerations. We also find that institutional quality amplifies this effect. Efforts should, therefore, be concentrated on improving institutional quality, the success of which will appeal to foreign investors and attract more foreign investments. [ABSTRACT FROM AUTHOR]

This paper uses dynamic panel data estimations based on annual data from 26 European Union countries to evaluate the driving factors of household savings dynamics. Alongside conventional determinants, such as household income and age dependency, the study also includes a less traditional variable, consumer con- fidence, which is often neglected in existing findings. This research extends previous empirical studies in three dimensions. First, it conducts sensitivity analysis using several estimation techniques to support the robustness of baseline results. Second, the investigation is expanded by including an extended set of potential savings drivers. Lastly, it explores variations in saving behaviour among different country groups (Euro Area, Central and Eastern European countries, and Croatia) as well as the crisis periods (Global Financial Crisis and Covid-19 pandemic). The findings highlight the importance of overlooked determinants, shed light on the ambiguous effect of classic variables, and partially confirm earlier research. [ABSTRACT FROM AUTHOR]

This paper investigates the relationship between democracy and economic growth in the context of a linear index threshold regression model. We first introduce the baseline model with endogeneity and propose a two-step smoothed generalised method of moments estimation method. We establish the consistency and derive the asymptotic distributions of the proposed estimators. We then extend the approach to a dynamic panel context and employ the model to explore the impact of democratisation on economic growth. Our findings reveal that democratisation's impact on growth is nonlinear and depends on the country's current institutional quality level. Furthermore, democracy's impact on economic growth is more pronounced in countries with higher education levels than others, suggesting that education also plays a crucial role in enhancing the positive effects of democracy. Our proposed estimator can be used in other situations that require the use of more than one threshold variable. In these cases, our hybrid estimator has less stringent data requirements than an alternative model where the thresholds would enter separately, especially when the threshold variables are correlated. [ABSTRACT FROM AUTHOR]

This paper attempts to identify the determinants of foreign direct investment (FDI), focusing especially on terrorism and keeping in mind that FDI is one of the key economic growth engines. The main goal of this paper was to determine the correlation between terrorism and investment activities. The method used is a dynamic panel data model (System 2 step-GMM estimator), based on a sample covering a total of 36 OECD economies in the period from 2005 to 2018. The findings indicate that terrorist incidents and economic, institutional, and natural variables have different impacts on FDI in the OECD Member countries. The research found a statistically significant impact of terrorist incidents and natural disasters and a strong impact of economic and institutional variables. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to empirically investigate the moderating role of corporate governance (CG) in the capital structure-performance relationship. Design/methodology/approach: The analysis is based on top Business Today-500 companies and covers a time span of 10 years. The fixed effect panel regression model is used to examine the impact of CG mechanisms on the relationship between capital structure and firm performance. Findings: The core findings of the study indicate significant positive moderating role of board independence, board size and family ownership on the relationship between leverage and performance. Practical implications: The results enable the managers of Indian firms to comprehend the significance of CG framework while taking financing decisions. The findings encourage managers to raise debt funds in those firms that adhere to good governance norms. Originality/value: Unlike extant studies that emphasize on the moderating impact of single CG variable in leverage-performance relationship, the current work comprehensively examines the role of many CG factors that moderate the relationship between capital structure and firm performance. To the best of the authors' knowledge, the present study is the first of its kind with respect to India. [ABSTRACT FROM AUTHOR]

Purpose: Extensive evidence shows that consumption-based asset pricing models (CCAPM) proposed by Lucas (1978) and Breeden (1979) have failed to explain average stock returns in cross-sectional data. In this context, we can refer to the studies of Breeden, Gibbons and Litzenberg (1989), Letas and Ludwigson (2001), and Jacobs and Wong (2004). In response to this failure, several studies used other variables than consumption growth in a single-factor model to improve the performance of the mentioned structure (such as Parker and Julliard (2005), Jaganthan and Wong (2007), Savo (2011) and Kroenke (2017)). In none of the domestic studies, inflation has been used as a risk factor. Therefore, this study aims to fill this gap by focusing on the impact of monetary illusion on the desirability of Iranian households in the period under review. In fact, this research is of novelty compared to the previous studies conducted inside the country. Firstly, with the inclusion of the inflation variable in the household preferences function, the CCAPM model has been developed in such a way that the inflation variable can be included in the household preferences function. Secondly, reversible preferences and non-reversible power utility have been used to estimate the monetary illusion parameter. Thirdly, in this research, the system of equations includes the return of various assets such as bank interest rate, stock return, housing return and labor wage return, and the parameters of the equations have been estimated by using different appropriate tools. Methodology: In order to include inflation as a risk factor and define a parameter that shows the degree of monetary illusion of brokers, Mayo (2018) specified a threefactor macro model for asset pricing including inflation rate, consumption growth and asset yield in the CCAPM structure. The underlying framework of the model includes a recursive inter-period utility presented by Epstein-Zine and Weil (1989). This framework made use of an intra-period utility function that corresponds to both real consumption growth and nominal consumption growth (with the specification a CobbDouglas function). Intra-period utility is appropriate for a case where the investor faces a partial monetary illusion, which is because he cannot fully distinguish real consumption from nominal consumption in his consumption/asset allocation decision. The degree of monetary illusion is represented by the monetary illusion parameter (ϵ), which varies from zero to one. Therefore, the assumption of monetary illusion allows the researcher to create a model in which the inflation variable is used as an endogenous risk factor in the pricing kernel. In this regard, there are three preferences parameters in the created model, including relative risk aversion coefficient, monetary illusion parameter, and inter-period substitution elasticity. In this study, inflation is included in the function of households' preferences in the form of consumption capital asset pricing model (CCAPM) so as to estimate the impact of money illusion on the utility of Iranian households in the period of 1978- 2021 To this end, the recursive preferences function provided by Epstein-Zin and a non-recursive power utility function with constant relative risk aversion are used in such a way that the inflation growth variable appears as a risk factor in the stochastic discount factor of the derived Euler equations. In fact, inflation arises endogenously in the pricing kernel by assuming an intra-temporal utility that depends on both real and nominal consumption. This suits an investor with partial money illusion. Then, the generalized moments method (GMM), MAE and MSE criteria are used to estimate the systems of equations and select the most appropriate model. Findings and discussion: After the mentioned models are estimated, the mean absolute magnitude of errors (MAE) and mean squared errors (MSE) criteria are used to select the best model among the fitted ones. The results show that the model with recursive preferences has the lowest values for the two mentioned statistics. Therefore, this model is chosen as the best one, based on which the effect of monetary illusion on the utility function of Iranian households has been 18% during the period under review. The criteria prove the superiority of recursive preferences. The results of the research also indicate that the money illusion parameter is statistically significant, and Hansen's J statistics confirm the appropriateness of the instruments. In the superior model, the effect of money illusion on the desirability of households is 0.18. The significance of the coefficients and the fit statistics of the models show that the inclusion of the data related to inflation growth in capital asset pricing models as a risk factor alongside the risk factor of consumption growth and asset return portfolio is significant. Conclusions and policy implications: The findings show that, in the first model where return preferences are used, the effect of monetary illusion on consumers' desirability is 0.18. Also, in the second model, which is bounded by the first model and involves non-reversibility and ability utility, the effect of monetary illusion on the utility of Iranian households is 0.03. In both estimates, all the coefficients are statistically significant, and the diagnostic tests for the remaining phrases confirm the correctness of the estimates. After the models are estimated, the mean absolute magnitude of errors (MAE) and the mean squared errors (MSE) criteria are used to select the best model among the fitted ones. The results show that the model with recursive preferences has the lowest values in the two types of statistics. Therefore, this model is chosen as the best one. Based on it, the effect of monetary illusion on the utility function of Iranian households is found to have been 18% during the period under review. Considering the relative impact of monetary illusion on the utility of households, it is necessary for policy makers and planners to reduce and control prices in order to better adapt the utility of households to economic realities. [ABSTRACT FROM AUTHOR]

This article analyzes the link between urbanization and the process of structural transformation (ST) in developing countries, taking into account the mediating role that FDI can play. We use secondary data from 2000 to 2023 to determine if FDI positively or negatively influences the ST process in these countries, thereby, altering urbanization patterns. By estimating the Generalized Method of Moments (GMM), the study explores the relationship between urbanization, the ST process, and the mediating role played by FDI. In addition, it takes into account control variables such as gross fixed capital formation (GFCF), gross domestic product per capita (GDPPC), and diversification of production structure (DPS) to analyze their effect on urbanization and, more specifically, on the ST process. The econometric results showed a negative correlation between FDI flows and urbanization, demonstrating that FDI could hinder the success of the ST process in developing countries. On the contrary, econometric results showed that the control variables, GFCF, GDPPC, and DPS, positively influence urbanization in developing countries, indicating that these variables are more conducive to a successful ST process than FDI. Based on the econometric results, policymakers in developing countries are called upon to strengthen urbanization in these countries, by encouraging local investment and the diversification and sophistication of production and export structures, rather than relying solely on FDI. Over the period studied, FDI has not contributed as expected to advancing the urbanization process, a crucial element in the ST process in developing countries. [ABSTRACT FROM AUTHOR]

This study used the panel data from 15 emerging markets to examine the impact of restrictive macroprudential policies on income inequality from 2000–2019 using Bayesian panel vector autoregression and Bayesian panel dynamics generalised method of moments models. The chosen models are suitable for addressing multiple entity dynamics, accommodating a wide range of variables, handling dense parameterisation, and optimising formativeness and heterogeneous individual-specific factors. The empirical analysis utilised various macroprudential policy proxies and income inequality measures. The results show that when the central banks tighten systems using macroprudential policy instruments to sticker debt-to-income and financial instruments for lower-income borrowers (the bottom 40% of the income distribution), they promote income inequality in these countries while reducing income inequality for high-income borrowers (the high 1 percent of the income distribution). The impact of loan-to-value ratios was found to be insignificant in these countries. Fiscal policy through government expenditure and economic development reduces income inequality, while money supply and oil-price shocks exacerbate it. The study suggests implementing a progressive debt-to-income (DTI) ratio system in emerging markets to address income inequality among lower-income borrowers. This would adjust DTI thresholds based on income brackets, allowing lenient credit access for lower-income borrowers while maintaining stricter limits for higher-income borrowers. This would improve financial stability and reduce income disparities. Additionally, targeted financial literacy programs and a petroleum-linked basic income program could be implemented to distribute oil revenue to lower-income households. A monetary supply stabilisation fund could also be established to maintain financial stability and prevent excessive inflation. [ABSTRACT FROM AUTHOR]

This study examines how GDP, renewable energy, population, and industrialization affect ecological footprints in six Balkan nations from 1990 to 2022. The six Balkan countries are Albania, Bulgaria, Greece, Croatia, Romania, and Slovenia. The research applied the normality test, unit roots, and cointegration tests to conduct stationary testing. The study used three econometric tools: Pooled-OLS, Fixed-OLS, and D-GMM methods to get robust results. Findings show that GDP and squared coefficients support EKC. This means that the ecological footprint initially rises because of the rise in GDP; after specific points, the ecological footprint declines. Balkan countries fit the reversed U-shaped EKC hypothesis after achieving economic development. Surprisingly, renewable energy shows a positive coefficient, challenging the anticipated positive environmental impact. This underscores the necessity for comprehensive assessments of renewable technologies to minimize unintended consequences. Similarly, fossil fuel consumption exhibits a positive coefficient, affirming its detrimental impact on ecological resilience. While contributing to economic growth, industrialization demonstrates a positive coefficient on environmental resilience, suggesting the need for sustainable industrial practices. Furthermore, the population displays a negative coefficient, affirming its potential role in curbing ecological vulnerability and emphasizing the significance of responsible demographic management. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to provide an empirical investigation into rising capital flight and the role of institutional quality to mitigate its effect on the real sector in sub-Saharan Africa (SSA). Design/methodology/approach: The study uses the system generalized method of moments and uses data spanning from 1989 to 2020 from 26 SSA countries. Findings: The findings show that capital flight has no direct impact on the real sector while institutional quality adversely impacted the agricultural and industrial sectors. The study also found that institutional quality is unable to mitigate the effect of capital flight on the industrial sector. Originality/value: This study investigates if institutional quality mitigates the impact of capital flight on the real sector proxied by industrial value-added and agriculture value-added. [ABSTRACT FROM AUTHOR]

Purpose – The study aims to evaluate the influence of capital structure decisions and asset structure on firms' performance for East African listed nonfinancial firms. Design/methodology/approach – The research is descriptive and employs secondary data from the East African capital markets' websites. The generalized method of moments approach is used to estimate the relationship due to its ability to account for endogeneity problems. Findings – The result shows that capital structure decisions and asset structure strongly influence the firms' performance. When long-term debts, short-term debts and tangible fixed assets increase, the return on total assets increases. An increase in the total debt ratio raises the return on equity (ROE). However, the increase in long-term debt lowers the ROE. Practical implications – The results will help investors and potential investors decide on a financing policy that maximizes performance. Likewise, governments and other policymakers review the capital markets' frameworks to attract institutional and individual investors to the markets for financial availability and to increase profitability. Originality/value – The research provides evidence on the influence of capital structure decisions and asset structure on firms' performance. Furthermore, its results contribute to firms' financing policy formulation and the corporate finance literature.

This study makes a valuable contribution to the existing literature by examining the impact of structural transformation on poverty reduction in 13 emerging economies during the period 2008-2018. The research utilizes a generalized method of moments (GMM) dynamic panel regression technique to identify the key drivers of poverty and inequality reduction. The findings reveal that structural change significantly contributes to poverty and income inequality reduction in the emerging economies under consideration. Specifically, the service sector and industry sector play pivotal roles in eradicating poverty and income inequality in these countries. However, the results suggest also that the agricultural sector may not be the most efficient means of reducing poverty and inequality in emerging economies. To increase its impact on poverty reduction, it is crucial to modernize and transform the agricultural sector into an agribusiness.

In the automatic reassembly of fragments acquired using laser scanners to reconstruct objects, a crucial step is the matching of fractured surfaces. In this paper, we propose a novel local descriptor that uses the Gaussian Mixture Model (GMM) to fit the distribution of points, allowing for the description and matching of fractured surfaces of fragments. Our method involves dividing a local surface patch into concave and convex regions for estimating the k value of GMM. Then the final Gaussian Mixture Descriptor (GMD) of the fractured surface is formed by merging the regional GMDs. To measure the similarities between GMDs for determining adjacent fragments, we employ the L 2 distance and align the fragments using Random Sample Consensus (RANSAC) and Iterative Closest Point (ICP). The extensive experiments on real-scanned public datasets and Terracotta datasets demonstrate the effectiveness of our approach; furthermore, the comparisons with several existing methods also validate the advantage of the proposed method. [ABSTRACT FROM AUTHOR]

Motivated by the increasing exposition of decision makers to both statistical and judgemental based sources of demand information, we develop in this paper a fuzzy Gaussian Mixture Model (GMM) for the newsvendor permitting to mix probabilistic inputs with a subjective weight modelled as a fuzzy number. The developed framework can model for instance situations where sales are impacted by customers sensitive to online review feedback or expert opinions. It can also model situations where a marketing campaign leads to different stochastic alternatives for the demand with a fuzzy weight. Thanks to a tractable mathematical application of the fuzzy machinery on the newsvendor problem, we derived the optimal ordering strategy taking into account both probabilistic and fuzzy components of the demand. We show that the fuzzy GMM can be rewritten as a classical newsvendor problem with an associated density function involving these stochastic and fuzzy components of the demand. The developed model enables to relax the single modality of the demand distribution usually used in the newsvendor literature and to encode the risk attitude of the decision maker. [ABSTRACT FROM AUTHOR]

This study aimed to establish the nexus between CEO power and bank risk. Previous studies on how CEO power affects risk-taking have produced mixed results. Some studies show that CEO power reduces risk, while others show the reverse. This lack of conclusive findings motivated this study. This study used secondary data from a sample of 14 commercial banks in Uganda covering a period from 2010 to 2020. System GMM was used to establish the relationship between variables, while ARDL was used to infer causality. Findings show that commercial banks with powerful CEOs have lower risk. Such powerful CEOs have prestige power, are internally hired, have ownership, and have served for more than 4 years up to 7 years, and hence possess expert power. We further found a long-run positive relationship between previous bank risk and current bank risk, as well as a causal relationship between CEO power and bank risk. In case there is a need to reduce bank risk in Uganda, making adjustments in CEO power will help. It may also be necessary for persistent adjustment and implementation of decisions and policy actions, if bank risk is to be minimized.

Abstract The microarray gene expression data poses a tremendous challenge due to their curse of dimensionality problem. The sheer volume of features far surpasses available samples, leading to overfitting and reduced classification accuracy. Thus the dimensionality of microarray gene expression data must be reduced with efficient feature extraction methods to reduce the volume of data and extract meaningful information to enhance the classification accuracy and interpretability. In this research, we discover the uniqueness of applying STFT (Short Term Fourier Transform), LASSO (Least Absolute Shrinkage and Selection Operator), and EHO (Elephant Herding Optimisation) for extracting significant features from lung cancer and reducing the dimensionality of the microarray gene expression database. The classification of lung cancer is performed using the following classifiers: Gaussian Mixture Model (GMM), Particle Swarm Optimization (PSO) with GMM, Detrended Fluctuation Analysis (DFA), Naive Bayes classifier (NBC), Firefly with GMM, Support Vector Machine with Radial Basis Kernel (SVM-RBF) and Flower Pollination Optimization (FPO) with GMM. The EHO feature extraction with the FPO-GMM classifier attained the highest accuracy in the range of 96.77, with an F1 score of 97.5, MCC of 0.92 and Kappa of 0.92. The reported results underline the significance of utilizing STFT, LASSO, and EHO for feature extraction in reducing the dimensionality of microarray gene expression data. These methodologies also help in improved and early diagnosis of lung cancer with enhanced classification accuracy and interpretability.

The purpose of this study is to investigate the effects of ownership by institutional investors on the financial performance of a sample of 15 French firms that were listed between 2015 and 2022 on the CAC40 index. To achieve the study’s objective, the percentage of shares owned by foreign and domestic institutional investors was used as a measure of institutional investors’ ownership, while the return on assets was used as a measure of financial performance. The study employed a statistical approach using Dynamic Panel Data Models with the Generalised Method of Moments (GMM). The study found a statistically significant negative impact between the ownership percentages of foreign and domestic institutional investors and financial performance. Regarding control variables, agency costs had a positive and statistically significant impact on financial performance, while liquidity had no significant effect.

The shadow economy poses a significant threat to government revenue and the effectiveness of economic policies. This paper investigates the causes of the shadow economy and its influence on foreign direct investment (FDI). Our study employs the currency demand approach, a component of the indirect method, to identify the determinants of the shadow economy in a dataset covering 105 countries from 2001 to 2017. These countries are categorized into four income groups: high-income, upper-middle-income, lower-middle-income, and low-income. Parameter estimation is conducted using the Generalized Method of Moments (GMM) model, with robustness tests incorporating reference estimates from Partial Least Squares (PLS) and Fixed Effects Model (FEM). Our findings indicate that a higher GDP and lower interest rates are associated with reduced shadow economy activity. Elevated market interest rates increase the cost of funds in the informal sector, discouraging engagement in shadow economic activities due to reduced profitability. Furthermore, higher tax revenues correlate with intensified regulatory enforcement, increasing the risks associated with shadow economy involvement. A larger workforce and lower unemployment rates similarly diminish shadow economy activity. In the context of foreign direct investment (FDI), the shadow economy positively affects FDI flows when formal institutions, including legal frameworks, property rights protection, and regulatory systems, are either weak or overly burdensome. In such scenarios, economic actors may opt for informal channels like the shadow economy, offering a flexible and cost-effective alternative to the formal sector, a crucial consideration for foreign investors.

The present world is on the good track to achieve economic growth though it results in huge environmental degradation. Hence, such economic growth poses serious detrimental impacts on human health, and it causes to increase in healthcare spending. Therefore, the present study aims to depict the relationship among carbon dioxide (CO2) emission, healthcare spending and economic growth for the South Asian countries (SAARC member countries) covering the period 1980-2014. The Dynamic Simultaneous-equation Model is fitted with the data set which is estimated by Generalized Method of Moment for investigating the causal relationship among these variables. The empirical results reveal bidirectional causality between carbon dioxide (CO2) emission and economic growth; and between economic growth and health spending. The results of the study also indicate unidirectional causality from carbon dioxide (CO2) and health spending in case of many SAARC member countries.

Abstract This study contributes to income equality (IE) literature by examining four important issues. First, the study examines the effects of foreign bank presence (FBP) on IE. Second, the paper identifies the minimum threshold level of FBP which can lead to IE. Third, the effect of economic freedom on IE was investigated. Fourth, the paper determines whether economic freedom interacts with FBP to minimise IE. The findings are based on macro data for 33 African countries from 1995 to 2020. The findings from the two-stage system generalised method of moment indicate that unconditionally, FBP reduces income inequality. Also, results from the threshold effect reveal that whilst FBP reduces income inequality, if it exceeds 52%, it may contribute to it. Additionally, the study reveals that economic freedom dampens IIE. Furthermore, economic freedom conditions FBP to reduce IE. Based on these findings, policymakers are advised to exercise caution in attracting foreign banks and to promote local financial institutions. Policymakers are also advised to implement policies to promote economic freedom.

Background This paper examines the impact of financial inclusion on bank stability within Ethiopian context, using panel data from 17 commercial banks over the period 2015-2023. Given the scarcity of research focused on the relationship between financial inclusion and bank stability in Ethiopia, this paper seeks to address a crucial gap by analyzing both conventional and digital aspects of financial inclusion in relation with bank stability. Methods A two-stage principal component analysis (PCA) was conducted to construct a composite financial inclusion index, integrating 10 conventional and 5 digital indicators. The study applied a two-step robust system generalized method of moments (GMM) to examine the effects of financial inclusion on bank stability, complemented by Granger causality testing to examine the directionality of this relationship. Results The result reveals a significant positive effect of financial inclusion on bank stability and Granger causality tests confirms a bi-directional relationship between financial inclusion and stability, indicating that improvements in financial inclusion foster greater stability and vice versa. Our results also highlight that while bank efficiency and GDP growth rate positively effect stability, total assets and income diversification exhibit detrimental effects. Conclusions It is essential to capitalize policy synergies to promote bank stability and to enhance financial inclusion through conventional and digital channels, while carefully managing the implications of risks associated with income diversification and asset distribution. Ensuring inclusive financial system is vital for maintaining bank stability, thus positioning it as a key priority for financial institutions.

We provide empirical evidence concerning dynamic persistence and spillovers in public expenditures with a focus on local governments. Using a unique panel dataset consisting of the counties and independent cities of Virginia, we find both time persistence and neighbour spillovers present in the data, but we find that time persistence is much stronger and reduces the strength of spatial spillovers by an average of 40% for different spending categories. We find that a spillover model is not well suited to local government expenditures due to mandatory spending. Time persistence captures the slow pace of many government activities. [ABSTRACT FROM AUTHOR]

Electroencephalography (EEG)-based applications in brain–computer interfaces (BCIs), neurological disease diagnosis, rehabilitation, etc., rely on supervised approaches such as classification that requires given labels. However, with the ever-increasing amount of EEG data, incomplete or incorrectly labeled or unlabeled EEG data are increasing. It likely degrades the performance of supervised approaches. In this work, we put forward a novel unsupervised exploratory EEG analysis solution by clustering based on low-dimensional prototypes in latent space that are associated with the respective clusters. Having the prototype as a baseline of each cluster, a compositive similarity is defined to act as the critic function in clustering, which incorporates similarities on three levels. The approach is implemented with a Generative Adversarial Network (GAN), termed W-SLOGAN, by extending the Stein Latent Optimization for GANs (SLOGAN). The Gaussian Mixture Model (GMM) is utilized as the latent distribution to adapt to the diversity of EEG signal patterns. The W-SLOGAN ensures that images generated from each Gaussian component belong to the associated cluster. The adaptively learned Gaussian mixing coefficients make the model remain effective in dealing with an imbalanced dataset. By applying the proposed approach to two public EEG or intracranial EEG (iEEG) epilepsy datasets, our experiments demonstrate that the clustering results are close to the classification of the data. Moreover, we present several findings that were discovered by intra-class clustering and cross-analysis of clustering and classification. They show that the approach is attractive in practice in the diagnosis of the epileptic subtype, multiple labelling of EEG data, etc. [ABSTRACT FROM AUTHOR]

Low user participation and less knowledge contribution seriously threaten the sustainability of online question and answers communities. Although researchers studied the different aspects of knowledge contribution and proposed useful suggestions, there is still no thorough study on the knowledge contribution pattern of consistent geeks' that can help improve low participation. According to social cognitive and self-determination theory, peers follow credible sources or role models in their participation patterns and are influenced by the community environment. Based on social cognitive and self-determination theory, we have studied the most consistent geeks of StackOverflow for the period between 2010–2020 to employ the results to activate dormant users. Two-step system GMM results revealed that most users take a free ride and hesitate to reciprocate; knowledge-seeking negatively influences the quantity of contributed knowledge. Peer recognition and repudiation positively influence the knowledge contribution of active geeks, whereas reputation scores and badges negatively influence the contributed knowledge's quantity and quality. Social interaction's role as moderator is also different for quantity and quality of knowledge contributed. Study results improve the existing literature and provide comprehensive managerial implications to improve low participation and create a progressive knowledge contribution environment. [ABSTRACT FROM AUTHOR]

While many scholars have concentrated on the green economy and emissions, no study has yet to be conducted on the impact of the green economy on carbon emissions in European countries. This study seeks to examine the impact of the green economy on carbon dioxide emissions for European countries under the environmental Kuznets curve (EKC) hypothesis, with the gap in the literature serving as motivation. The annual data for 2012–2020 is analyzed by employing the generalized method of moments (GMM). The study's empirical results demonstrated that green economies play a significant role in lowering carbon emissions and enhancing environmental quality in European countries. These findings are robust to the addition of the interaction effects of economic growth and green economy on the EKC for carbon emission. Economic expansion can result in decreased carbon emissions, displaying the presence of EKC when it interacts with the country's green economy. Thus, the government should enhance investment in green economy projects that can aid in reducing carbon emissions. [ABSTRACT FROM AUTHOR]

The microarray gene expression data poses a tremendous challenge due to their curse of dimensionality problem. The sheer volume of features far surpasses available samples, leading to overfitting and reduced classification accuracy. Thus the dimensionality of microarray gene expression data must be reduced with efficient feature extraction methods to reduce the volume of data and extract meaningful information to enhance the classification accuracy and interpretability. In this research, we discover the uniqueness of applying STFT (Short Term Fourier Transform), LASSO (Least Absolute Shrinkage and Selection Operator), and EHO (Elephant Herding Optimisation) for extracting significant features from lung cancer and reducing the dimensionality of the microarray gene expression database. The classification of lung cancer is performed using the following classifiers: Gaussian Mixture Model (GMM), Particle Swarm Optimization (PSO) with GMM, Detrended Fluctuation Analysis (DFA), Naive Bayes classifier (NBC), Firefly with GMM, Support Vector Machine with Radial Basis Kernel (SVM-RBF) and Flower Pollination Optimization (FPO) with GMM. The EHO feature extraction with the FPO-GMM classifier attained the highest accuracy in the range of 96.77, with an F1 score of 97.5, MCC of 0.92 and Kappa of 0.92. The reported results underline the significance of utilizing STFT, LASSO, and EHO for feature extraction in reducing the dimensionality of microarray gene expression data. These methodologies also help in improved and early diagnosis of lung cancer with enhanced classification accuracy and interpretability. [ABSTRACT FROM AUTHOR]