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This chapter revisits the Hausman (1978) test for panel data. It emphasizes that it is a general specification test and that rejection of the null signals misspecification and is not an endorsement of the fixed effects estimator as is done in practice. Non-rejection of the null provides support for the random effects estimator which is efficient under the null. The chapter offers practical tips on what to do in case the null is rejected including checking for endogeneity of the regressors, misspecified dynamics, and applying a nonparametric Hausman test, see Amini, Delgado, Henderson, and Parmeter (2012, chapter 16). Alternatively, for the fixed effects die hard, the chapter suggests testing the fixed effects restrictions before adopting this estimator. The chapter also recommends a pretest estimator that is based on an additional Hausman test based on the difference between the Hausman and Taylor estimator and the fixed effects estimator.

Our study was designed to investigate the longitudinal trajectories of student leader development capacities in a sample of students enrolled in multiple leadership-focused courses across several semesters. Our goal was to assess the degree to which course enrollment was associated with growth over the time that students engage as undergraduates in academic leadership programs, and if so, to assess the shape and speed of capacity change.

We utilized a multilevel intra-individual modeling approach assessing students’ motivation to lead, leader self-efficacy, and leadership skills across multiple data collection points for students in a campus major or minor focused on leadership studies. We compared an unconditional model, a fixed effect model, a random intercept model, a random slope model, and a random slope and intercept model to determine the shape of score trajectories. Our approach was not to collect traditional pre-test and post-test data – choosing to collect data only at the beginning of each semester – to reduce time cues typically inherent within pre-test and post-test collections.

Our results strongly suggested that individual students differ greatly in the degree to which they report the capacity to lead when initially enrolling in their first class. Surprisingly, the various models were unable to predict a pattern of longitudinal leader development through repeated course enrollment in our sample.

Our investigation employed statistical methods that are not often utilized in leadership education quantitative research, and also included a data collection effort designed to avoid a linear pre-test/post-test score comparison.

Hydraulic slide valve failure often results from competing failure modes, termed competitive failure. To enhance prediction accuracy for hydraulic slide valve remaining useful life, the authors propose a method incorporating competitive failure and Monte Carlo simulation. This method allows for more accurate prediction of hydraulic slide valve remaining useful life.

In this paper, the competitive failure mode of the hydraulic slide valve is analyzed by studying the two failure modes of the hydraulic slide valve, and the prediction of the remaining useful life of the hydraulic slide valve is studied by using the sample set generated by Monte Carlo simulation and the competitive failure joint model.

The results show that the proposed prediction method based on competitive failure and Monte Carlo simulation is more accurate than the traditional Bayesian joint model prediction method when dealing with the failure mode competition phenomenon of hydraulic slide valve.

In this paper, the remaining useful life prediction of hydraulic slide valve with competitive failure characteristics is studied, which provides a new idea for the remaining useful life prediction method.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-11-2023-0361/

This study aims to price the risk contribution of general Takaful at the level of tariff cells, considering a spatial dependency framework.

Three different models, including a generalized linear model, a generalized linear mixed model (GLMM) and a spatial generalized linear mixed model (SGLMM), according to the actuarial modeling of general Takaful, are used to price pure risk contribution (PRC).

The results reveal that the SGLMM yields more accurate predictions of the PRC compared to the other models, emphasizing the significance of spatial modeling in this context. Following the estimation of the PRC, the gross contribution according to the mechanism of Takaful models is calculated considering the spatial model.

Considering the similarities between Takaful and insurance, this study addresses the pricing of general Takaful within different Takaful models through a spatial dependency framework, such that the practical implications of the study are applicable for running Takaful's business in both Islamic and non-Islamic countries.

Most studies consider only the social or practical view of Takaful. This study contributes to the broader knowledge and understanding of Takaful by presenting a conceptual understanding of Takaful and then investigates the practical application of pricing risk contribution using innovative modeling of claim frequency and severity at the level of tariff cells.

This research aims to examine the macro determinants that significantly affect financial development in the Middle East and North Africa (MENA) region, which could be used furtherly to play a major role in economic sustainability since one of the major driving forces for economic development is the financial development.

The significant determinants of financial development should be efficiently used by the MENA region countries for creating huge financial sector development and innovation, stimulating economic development in turn and leading to the completion of the cycle of development and sustainability. To achieve this study's objective, the researcher employed a quantitative method to develop an econometric model.

This model consisted of two Panel EGLS Cross-Section Random Effects Models (REMs) in which Domestic credit to the private sector as a percentage of GDP (?PCGDP?_it) and stock market capitalization ratio (?SMC?_it) were taken as the dependent variables. In addition, the independent variables included the corruption perception index, financial freedom (FF), political stability (PS) and trade openness (TO). The researcher extracted the data for the analysis from different databases including the World Bank, the Organization for Economic Cooperation and Development and the International Monetary Fund. Throughout the first – Panel EGLS Cross-Section Random Effects Model, it turned out that, while FF, TO and corruption index had a positive relationship with ?PCGDP?_it, PS had an adverse effect on ?PCGDP?_it. The second – Panel EGLS Cross-Section Random Effects Model showed that, while PS and TO had a positive effect on stock market performance, the corruption index and FF had an adverse effect on stock market performance.

Throughout the first – Panel EGLS Cross-Section Random Effects Model, it turned out that, while FF, TO and corruption index had a positive relationship with ?PCGDP?_it, PS had an adverse effect on ?PCGDP?_it. The second – Panel EGLS Cross-Section Random Effects Model showed that, while PS and TO had a positive effect on stock market performance, the corruption index and FF had an adverse effect on stock market performance.

The purpose of this study is to verify all factors that promote renewable energy (RE) consumption. Past studies have shown that financial development (FD) and economic growth (EG) are the major drivers toward RE development, while oil prices had mixed outcomes in different regions by different studies.

Global warming effects have been the major reason of the transition by nations from fossil fuel use to RE sources that are considered as friendly to the environment. This research uses the fixed effects and random effects techniques, to ascertain the factors which impact RE development. The generalized linear model is also used to check the robustness of the Fixed Effects and Random Effects models’ results, while the Kao, Pedroni and Westerlund tests are used to check cointegration in the specified model.

The major findings of this study show the importance of EG and FD in promoting RE development. Oil prices, inflation rate and public sector credit present a negative effect on RE development, while foreign direct investment does not significantly impact RE development.

This research recommends the use of FD in promoting RE sources, as well as the stabilization of oil prices and consumer prices.

This research is important because it specifies the three proxies of FD, together with foreign direct investment inflation rate, EG and oil prices, in modeling RE. By investigating the impact of oil prices on RE in the emerging seven economies, this research becomes one of the few studies done in this region, as per the authors’ knowhow.

In this chapter, the authors evaluated the financial security of “Kvarts” Joint-Stock Company (JSC) and “Kattakurgan oil” JSC operating in Uzbekistan, considering external factors. The authors tested two main hypotheses. According to the first hypothesis, the statistical panel model, which considers fixed effects and random effects in the cross section, is estimated using the least squares method of the cumulative model. Regression analysis is used in the panel data model. It is desirable to perform the calculation using the method of least squares in this model as well. According to the alternative hypothesis, it is a dynamic panel model. This model studies the dependence of the independent variable in the previous period. If there is an autocorrelation of the residual between the predicted independent variable and the actual independent variable in the regression line of the statistical panel model, then the dynamic panel model is used by the lagged independent variable. Based on the official reports of “Kvarts” JSC and “Kattakurgon oil” JSC, the authors created panel regression model indicators based on financial security indicators using the Eviews-9.0 program. Moreover, the authors developed forecast indicators of their financial security status indicators until 2025.

The purpose of this paper is to investigate the influence of macroeconomic cyclical indicators and country governance on bank non-performing loans in African countries.

Data was collected from the 53 African countries covering 2005–2021. The paper develops an empirical model to examine the impact of country governance in reducing macroeconomic cycle-induced adverse effects on bank credit risk. This research estimates Random Effects models and the General Method of Moment to examine the link between microeconomic and governance factors on bank non-performing loans. Stata version 15.1 was used to conduct panel regression analysis.

The findings of the study revealed that the generalized method of moments findings contributes valuable insights into the persistence of NPLs over time and the specific effects of variables on NPL levels. The study findings highlight that the debt-to-GDP ratio, unemployment, regulatory quality, government effectiveness and inflation have significant relationships with NPLs, shedding light on their specific contributions to credit risk dynamics.

The focus on a specific set of determinants for NPLs, which may not capture all the factors that influence NPL levels. Thus, the study did not consider the impact of macroeconomic shocks, such as natural disasters or global economic crises, which can have a significant impact on NPLs.

Policymakers should prioritize maintaining sustainable debt levels, promoting employment growth and controlling inflation rates to mitigate credit risk and reduce nonperforming loans. Also, enhancing regulatory quality and government effectiveness is crucial in ensuring financial stability and minimizing non-performing loans in Africa.

This paper provides a new possible solution to minimise bank non-performing loans risk by examining interactions of country governance regarding the macroeconomic cycle behaviour.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-11-2022-0729

The purpose of this study is to examine the structure of the international fossil fuel trade network (IFFTN) and assess its effects on CO₂ emissions and global trade patterns. This research integrates complex network theory with econometric analysis to explore the dynamics of fossil fuel trade and its implications for environmental quality across various countries. Specifically, the study analyses the roles of different countries within this global network, examines the relationship between trade volumes and environmental impacts and evaluates how advancements in renewable energy generation could mitigate these effects. Through this comprehensive examination, the study seeks to provide an in-depth understanding of the trade-environment nexus.

The study uses data on international fossil fuel trade from 2005 to 2020, which includes 74 countries categorized as high-income, low-income and Asian economies based on their roles in the global market. This research constructs the IFFTN, where countries are depicted as nodes and trade links as edges. The authors analyse network parameters, such as degree, density and clustering coefficient, along with trade metrics like strength and centrality. These parameters are integrated into a panel fixed effects model, with the robustness of the findings confirmed through dynamic ordinary least squares (DOLS) analysis.

The study finds that the dynamic fossil fuel trade network includes key players such as the USA, China, France, India, the Netherlands and South Korea. It demonstrates increased connectivity and dependence among these countries, directly correlating with higher CO₂ emissions. However, this correlation is mitigated by the adoption of renewable energy, particularly in Asia and high-income countries. The impact on environmental quality is mediated through scale, technique and composition effects, suggesting significant environmental improvements through enhanced industry structure, technological progress and economies of scale.

This study recognizes several limitations. First, the categorization of countries into Asian economies, low-income and high-income groups may oversimplify the intricate effects of economic status on environmental impacts. Second, focusing primarily on per capita CO₂ emissions may neglect other critical environmental indicators. Future research should consider examining regional variations and including a wider range of environmental metrics. This approach would offer a more detailed perspective on the nuanced interactions between economic development and environmental sustainability, enhancing the depth and applicability of the findings.

To address the challenges of the IFFTN and CO₂ emissions, several practical policy measures are recommended. Governments should enhance international cooperation by establishing global platforms for sharing best practices and initiating technology transfer agreements to accelerate the adoption of energy-efficient technologies. Additionally, a phased transition towards more sustainable energy sources is crucial, involving increased investment in the renewable energy sector alongside incentives for adopting green technologies. On the trade front, governments should modify trade partnerships to address congestion externalities, fostering a shift towards more sustainable and environmentally friendly trade practices.

The social implications of the IFFTN are profound. As global reliance on fossil fuels continues, communities face heightened health risks due to increased pollution. Transitioning to renewable energy can alleviate these health concerns and the creation of green technologies, enhancing social well-being. Moreover, equitable access to energy-efficient solutions can reduce energy poverty, particularly in low-income countries, fostering greater societal resilience and inclusivity.

This study offers a pioneering examination of the trade-energy nexus across 74 countries, using complex network models to analyse diverse economic settings, particularly in Asian economies dominated by non-renewable energy. It identifies key market players and assesses their impact on dynamics such as congestion and market power. Additionally, the study explores the positive effects of renewable energy capacity on these relationships, highlighting its crucial role in driving sustainable energy transitions and enhancing the understanding of indirect trade-environment interactions.

The effects of integrated reporting quality (IRQ) have been debated in increasing empirical studies. Several IRQ measures, different theoretical approaches and multiple contexts have been adopted and investigated, leading to mixed results. By using the meta-analytic technique, this study aims to contribute to the accounting literature, reconciling the conflicting results on the effects of IRQ and providing objective conclusions to complement narrative literature reviews.

A sample of 45 empirical papers from 2013 to 2022, with 653 effect sizes, was used to assess the effects associated with IRQ. The papers were clustered into five groups (market reaction, financial performance, cost of capital, financial analysts’ properties and managerial decisions) based on the different consequences of IRQ investigated in the primary studies. A random-effects meta-regression model was used to explore all sources of heterogeneity together.

The meta-regression results confirm that IRQ positively influences firms’ market valuation and financial performance and hampers opportunistic managerial behaviour by improving corporate transparency, mitigating information asymmetry and encouraging accountability. Moreover, differences in the study characteristics affect the strength of the relationship object of interest.

Through meta-analysis, this study provides a broader overview of the effects of IRQ by enhancing the generalisability of the findings. The results also pave the way for additional evidence on the outcome variables affected by the quality of integrated disclosure.