Set Of Control Variables Research Articles

Electrical discharge machining process investigation and optimization of parameters for machining of aluminum silicon dioxide (Al7075–SiO2) composite

AbstractThis research paper explores the impact of electrical discharge machining process parameters, including current, pulse‐on time (Ton), and pulse‐off time (Toff), on the material removal rate (MRR) and surface roughness (SR) during the fabrication of aluminum silicon dioxide (Al7075–SiO2) composites. The Al7075–SiO2 composites were produced through stir casting, incorporating 10% by weight of SiO2 reinforcements with an average particle size of 54 µm. To systematically investigate the process parameters' effects, Taguchi's L9 orthogonal array was implemented in the design of experiments. Subsequently, the experimental data underwent analysis of variance (ANOVA) for thorough analysis. The response table for MRR and SR demonstrated that the current significantly influenced both responses (MRR and SR). The ANOVA results provided valuable insights into the percentage contribution of each parameter to the responses. Through the analysis, the optimal control variable settings for achieving the highest MRR were identified as a current of 3 A, a pulse‐on time of 1500 µs, and a pulse‐off time of 1 µs. Conversely, to attain the lowest SR, the ideal control variable settings were determined to be a current of 1 A, a pulse‐on time of 500 µs, and a pulse‐off time of 1 µs. Results also suggest that the error was within admissible range of 5% and the percentage contribution of the current (70.19%) had the largest effect on MRR.

The Impact of Economic Integration on Income Inequality in the EU: A Panel Data Analysis of the EU Members from 2002-2020

This paper investigates the relation between economic integration and income inequality for the 27 current EU members from 2002 to 2020. The Gini coefficient is the dependent variable in a panel data regression analysis, and is expressed as a function of indicators related to economic integration (total trade, intra-EU trade, foreign direct investment (FDI) inflow and outflow) and a set of control variables. Although much research points to a significant relationship between inequality and integration, there is no consensus on the sign and magnitude of the effect. In this paper, four random effects panel models are estimated with robust standard errors to uncover this relationship. The results show that in the context of the EU, intra-EU trade is associated with decreased income inequality, while overall trade and FDI in- or outflow seem to have no direct effect on the income gap. In addition, the level of economic development seems to moderate the effect of intra-EU trade: for countries with a below-average gross domestic product (GDP) per capita, the reducing effect of integration on Gini is weaker than for richer countries. Additionally, market capitalisation, the presence of natural resources and government spending on social benefits are associated with reduced inequality, while unemployment and population size seem to drive income disparities. These results are consistent with a major part of the existing literature and lead to interesting conclusions for policy makers. The originality of this work differentiating it from prior research is twofold: (1) the region of examination is the EU, which is not often the subject of similar analyses, and (2) an interaction effect is examined that differs from the conventional measures for less developed economies.

Toward unique electrical ladder network model synthesis of a transformer winding high-frequency modeling using K-means and metaheuristic-based method

PurposeAlthough, numerous optimization algorithms have been devoted to construct an electrical ladder network model (ELNM), they suffer from some frail points such as insufficient accuracy as well as the majority of them are unconstrained, which result in optimal solutions that violate certain security operational constraints. For this purpose, this paper aims to propose a flexible-constraint coyote optimization algorithm; the novelty lies in these points: penalty function is introduced in the objective function to discard any unfeasible solution, an advanced constraint handling technique and empirical relationship between the physical estimated parameters and their natural frequencies.Design/methodology/approachFrequency response analysis (FRA) is very significant for transformer winding diagnosis. Interpreting results of a transformer winding FRA is quite challenging. This paper proposes a new methodology to synthesize a nearly unique ELNM physically and electrically coupled for power transformer winding, basing on K-means and metaheuristic algorithm. To this end, the K-means method is used to cluster the setting of control variables, including the self-mutual inductances/capacitances, and the resistances parameters. Afterward, metaheuristic algorithm is applied to determine the cluster centers with high precision and efficiency.FindingsFRA is performed on a power transformer winding model. Basing on the proposed methodology, the prior knowledge in selecting the initial guess and search space is avoided and the global solution is ensured. The performance of the abovementioned methodology is compared using evaluation expressions to verify its feasibility and accuracy.Originality/valueThe proposed method could be generalized for diagnosis of faults in power transformer winding.

Are the Environment, Social, and Government Performance Affecting the Firm’s Value?

<p>This study aims to gather empirical data regarding how Environment, Social, and Governance (ESG) Performance affects Firm Value. This study uses two sets of control variables selected from several financial ratios to support the ESG score. The first model is the research observation in the short term with the first set of control variables. The second model is long-term with a second set of control variables. The research sample comprises publicly traded companies on the Indonesia Stock Exchange with a sustainability history and documented ESG performance.<strong> </strong>This study compiles secondary data sources for ESG company performance scores from Refinitiv This research uses EViews version 12 statistical package for econometric analysis. This study with first-step variables showed that ENV and GOV significantly positively influence firm value. In addition, variable control DAR and CR significantly positively influence fair value. In Model 2, this study’s second-step variables showed that SOC and GOV significantly positively influence fair value. Variable control ROE and ROA have a significant positive influence on fair value. During the financial crisis, ESG performance reduced financial risks; in more normal circumstances, its role grew considerably. This highlights the importance of ESG performance during the crisis, using new and optimized data sets. These findings were made based on ESG investment practices. </p><p> </p>

Salary inequality in young professors: evidence from public U.S. economic department

ABSTRACT The current literature provides little empirical evidence on the study of salaries for young faculty members in the economic department, possibly because effective recognition of their research productivity remains a difficult challenge. To address this research gap, we analyse the effects of gender, PhD graduation school rank and undergraduate major on the salaries of young economics professors with varying experience levels. We create a novel dataset by manually collecting detailed and time-varying research productivity measures and demographic information of young economics professors from the top 50 public research universities in the U.S. We use double machine learning to obtain consistent estimators, which allows us to account for non-parametric features associated with the high-dimensional control variable set. Our findings indicate that in experience years 4 to 8, which is the time most faculty tenured, the gender effect estimates are statistically significant and large enough in magnitude to have practical implications. For PhD graduation school rank and undergraduate major, the effects in experience years 7 to 9 are large in magnitude but do not have statistical significance.

Developing a pharmaceutical fluid bed granulation & drying process via design of experiments based on a multivariate model

This manuscript presents a lean industrial workflow to establish an operating design space for a commercial scale fluid bed granulation and drying process. The workflow involves confirming the operating design space by demonstrating that the drug product critical quality attributes are met at the most extreme processing conditions within the granulation design space. This eliminates the need to execute a large experimental design, since any processing condition bound by the adequately demonstrated extreme conditions should also be acceptable, assuming linearity and continuity. A PLS (partial least squares) model was used to illustrate the multivariate relationship among the important process outputs and inputs (material properties and operating control variable settings). Since the moisture content of the fluidized bed influences granulation, the design space was evaluated based on the moisture accumulation and removal rates (output of the PLS models). The PLS model identified the specific experiments needed to confirm the operating design space. These specific experiments were executed, and the design space was confirmed experimentally. The PLS-model guided experimentation resulted in a lean experimental study while delivering a robust drug product manufacturing process.

Exploring the Interplay between Religion, Health, and Subjective Well-Being in Turkey

Religion, a primary part of human life, has long been granted as an influential drive shaping individuals' beliefs, values, and behaviors. It permeates numerous aspects of life, granting support, guidance, and a sense of purpose to billions of adherents around the world. This paper investigates the intricate link between subjective well-being and religion by focusing on the influence of religious beliefs and practices on happiness, life satisfaction, and subjective health outcomes. While previous findings have provided inconsistent evidence about this relationship, most of it has been conducted in Western and Judeo-Christian contexts. Even though there are few studies addressing religion and subjective well-being in Turkey this study presents a different perspective by surveying national-level data from Turkey. Existing literature on religion and subjective well-being presents a divided narrative; while some studies suggest that religion positively affects well-being, others claim that it has negative or neutral effects. However, this paper enhances the existing literature by exploring these relationships within the framework of Islamic beliefs and practices examining how religious beliefs and practices influence subjective well-being outcomes in the Muslim framework. The study analyzes subjective well-being, including happiness and life satisfaction, in both cognitive and emotional dimensions. It also explores the connections between religion and subjective mental and physical health investigating the possible moderating effect of religion on Subjective wellbeing and health outcomes. First, it explores the moderating role of religion in the association of subjective well-being and subjective health. In addition, from an Islamic viewpoint, this study focuses on the multiple dimensions of religiosity in a Muslim-populated nation where religious membership and participation concepts vary from Western Christian societies. Islamic beliefs emphasize the importance of living a disciplined and devoted life by focusing on inner virtues and moral behavior. Islam advances a perception of happiness that transcends hedonism and highlights eudaimonic well-being by prioritizing the hereafter over worldly pleasures. The impact of religion on the well-being of Turkey, which has a unique cultural and political structure, is complex. This dichotomy of being secular and religious at the same time allows for significant religious differences, making Turkey an interesting subject of study. The research uses a series of hypotheses exploring the links between religion, happiness, life satisfaction, and health. Its aim is to reveal the relationships between these variables and to determine how religiosity directs these relationships. In addition, the research also investigates how subjective well-being results differ depending on the level of religiosity in Turkey. The data of this study are based on the well-known World Values Survey (WVS) 2012. Three dependent variables were used in the study: happiness, life satisfaction, and subjective health. Three religious determinants measure religious beliefs and practices, and a set of control variables accounts for sociodemographic factors. The findings of this study will contribute to a more nuanced understanding of the complex interaction between religion and health and well-being in different cultural and religious settings. The results present a series of multinomial logistic regression models designed to explore the relationships between independent variables and dependent outcomes. These models discover complex patterns in the data and offer insights into direct and indirect effects, as well as the moderating role of religion in mental health outcomes.

Investment deviation from the optimal path: does the examination of audit quality services matter? A French study

Purpose The study of the relationship between external auditing services and investment deviation in a French setting has received relatively little research attention thus far. There are insufficient indicators to measure audit quality and then have a measurable link to investment efficiency. This study is motivated by such a research gap as well as the important role of auditing services in assuring investment efficiency. The purpose of this study is to test whether a good audit quality service improves corporate investment awareness in French-listed companies and contributes to establishing a comprehensive analysis framework for inefficient investment and how audit services have become an important tool to reduce the investment deviation of listed companies in France. Design/methodology/approach Based on a sample of 89 non-financial French firms listed on the Stoxx 600 Index from 2015 to 2021, this study uses feasible generalised least squares (FGLS) regressions to study the relationship between investment deviation and auditing service quality. Findings After running an FGLS regression model for two firm groups (overinvestment and overinvestment groups) and testing for a set of control variables, especially COVID-19, the findings show a non-linear correlation between audit service and corporate investment deviation. Both underinvestment and overinvestment decisions are negatively and statistically significantly impacted by audit indicators. Furthermore, involving a high-quality specialised auditor may enhance overall monitoring and lead to a lower investment deviation level. Overall, the empirical results show that a high-quality audit service enhances the investment efficiency of French-indexed companies. Practical implications This study offers crucial information that audit regulators can use to better appreciate the advantages of high audit quality and to take seriously the policy issues that affect it. Board members are urged to provide excellent audit quality that improves investment efficiency with careful consideration. Originality/value This study contributes to the existing audit literature by illuminating the effect of audit quality services on investment deviation to show a deeper understanding of the factors that contribute to the differences in prior studies’ findings in the field of audit quality impacts.

Partisan conflict and corporate credit spreads: The role of political connection

This paper documents the positive impact of partisan conflict on corporate credit spreads for politically connected companies and industries. The effect is both economically meaningful and statistically significant, stands under an extensive set of control variables, and is stronger for speculative-grade bonds. Several approaches are adopted to resolve endogeneity issues and further establish causality. Partisan conflict affects corporate credit spreads through a discount rate channel, increases investors’ risk aversion, and leads to higher borrowing costs and widening credit spreads. Affected companies respond by reducing debt issuance and postponing investments until the conflict subsides.

The effects of class attendance on academic performance: Evidence from synchronous courses during Covid-19 at a Chinese research university

Using 13,584 observations of 4772 undergraduate students enrolled in 523 online classes offered synchronously at a research university in China, and using online-learning platform data and administrative data, this paper examines the impacts of class attendance on academic performance. Based on student-level and course-level fixed effects models with a unique set of student-course level control variables, this paper finds class attendance to have a significantly positive impact on academic performance. This effect is larger for low-performing students and smaller for students in courses with larger class sizes. The above findings survive a number of robustness checks, including a bounding technique, restricting sample to compulsory courses and limiting variation in attendance caused by whole day absence.

Resilient and constrained consensus against adversarial attacks: A distributed MPC framework

This paper proposes a distributed resilient consensus framework consisting of a distributed model predictive control (DMPC)-based consensus protocol and a new distributed attack detection mechanism, aiming to effectively handle the general linear constrained MAS under adversarial attacks. Based on the historical information from neighbors and a resilience set, we can evaluate the reliability of communication links via the attack detection mechanism. The proposed detection mechanism can significantly reduce the requirement on the network robustness compared with the well-known mean-subsequence-reduced (MSR) algorithms. The resilient consensus of general linear constrained MAS with F-locally adversarial attacks is achieved when the communication network is (F+1)-robust. Furthermore, the DMPC-based resilient consensus framework exhibits the following key feature: the constrained consensus performance is optimized by minimizing a set of control variables. We demonstrate that the recursive feasibility of the associated DMPC optimization problem and the resilient consensus convergence of the constrained MAS can be guaranteed. Finally, the effectiveness of the proposed framework is illustrated through simulation experiments.

Does social communication make investors stay in the market?

Abstract The role of online discussions in rallying investors has been highlighted by the GameStop frenzy of 2021. We know little, however, about the effect of online discussions on investor survivorship. Previous studies have emphasized the role of communication with respect to market entry, but not with respect to market survival. Survivorship studies, in turn, have largely focused on the role of financial factors in the investors’ decision to quit trading, but have not investigated so far how social interactions and communication impact the decision to quit or stay. Using a dataset covering 1.1 million observations for 4,731 traders over 18 months, we examine the role of online discussions in traders’ decisions to stay in the market. We find that traders who actively engage in them tend to be 16 per cent to 30 per cent less likely to quit trading. We identify a positive Granger-causal relationship between online discussions and the probability of survival. Our results are robust to alternative measures of communication and different sets of control variables. Based on this evidence, we draw attention to the necessity of studying the nexus between finance and social media as an important component of platform capitalism.

Agency conflicts, corporate governance, and capital structure decisions of Indian companies: evidence from new governance laws

PurposeThis study investigates the effects of elements of corporate governance along with firm specific variables on the financial leverage of listed Indian firms in the context of agency conflicts and new governance laws.Design/methodology/approachA series of panel ordinary least squares as well as fixed/random effects regression models of book and market value of financial leverage on variables of corporate governance (board size, board composition, board meeting, board attendance and board gender) along with a set of control variables (asset tangibility, firm size, growth, liquidity and profitability) were estimated by employing 113 listed Indian firms during 2010–2021. Dynamic panel generalized method of moments models were also estimated to check the robustness of empirical results. Further, the full sample of firms was divided into small and large board sized companies using the median approach to investigate differences between small and large board characteristics on financial leverage.FindingsThe evidence predominantly suggested that the governance variables have significant impact on leverage ratios of selected firms. Governance variables such as board size, composition, attendance and gender are significantly found to be reducing the financial leverage of firms indicating that in general these attributes in a way, through monitoring managers, put pressure on them to pursue lower financial leverage. Board meeting is found to be positive and significantly related with financial leverage suggesting that the frequency of meetings signals its monitoring ability that may influence lenders' risk assessment lowering borrowing cost. The results on small and large board sized companies indicate that firms with small boards relatively issue more debt compared to firms with large boards suggesting that small boards adopt high debt policy.Practical implicationsThe main policy implication of the study is that elements of internal corporate governance is a significant governance tool that has the potential to reduce agency conflict between the managers and agents through monitoring and decision making that has tangible effects on critical corporate decisions such as capital structure choices.Originality/valueThis paper contributes to the existing literature by bringing new evidence relating to agency conflicts and capital structure decisions in an emerging market like India post adoption of new regulations related to corporate governance specified in Clause 49 of Securities and Exchange Board of India and Companies Act, 2013 as there is significant dearth of such empirical work.

The road towards environmental sustainability: Investigating the role of information and communication technologies and green technology innovations

Human activity is considered to be the largest contributor to global warming and environmental deterioration. In order to limit the destruction and transition toward a more sustainable environment, governments and corporations, are increasingly turning to Information and Communication Technologies (ICT) and green technology innovations. However, no consensus on whether ICT exert a positive or negative influence on the environment appears in the literature, and solid empirical evidence linking green technologies to environmental performance is lacking. This study fills this gap by empirically testing the impact of ICT and green technology on environmental sustainability measured using the environmental performance index (EPI) and takes into account the Environmental Kuznets Curve (EKC) framework. Further, it includes a set of control variables: financial development, urbanization, trade openness, and foreign direct investment. It uses a balanced panel for 21 countries for the years between 1995 and 2019. Results show that both ICT and green technology innovations exert a significant positive impact on environmental sustainability. Financial development, urbanization, and foreign direct investment also positively influence environmental sustainability, while trade openness negatively impacts the environment. The findings offer policy implications for governments on how to exploit the potential of ICT and green technology to achieve environmental sustainability.

Leadership and Entrepreneurship: Evidence from a Chinese Household Survey

Abstract This study investigates the role of leadership in entrepreneurship. Using data from a representative household survey in China, we find that people who demonstrate leadership in their adolescence are more likely to become entrepreneurs. This finding is robust to controlling various sets of control variables and various forms of fixed effects. Better venture performance, rather than more risk seeking, seems to be a potential mechanism through which leadership is related to entrepreneurship. JEL classification numbers: L26, M13. Keywords: Leadership, Entrepreneurship, Venture Performance.

Scalable Bayesian optimization with randomized prior networks

Several fundamental problems in science and engineering consist of global optimization tasks involving unknown high-dimensional (black-box) functions that map a set of controllable variables to the outcomes of an expensive experiment. Bayesian Optimization (BO) techniques are known to be effective in tackling global optimization problems using a relatively small number objective function evaluations, but their performance suffers when dealing with high-dimensional outputs. To overcome the major challenge of dimensionality, here we propose a deep learning framework for BO and sequential decision making based on bootstrapped ensembles of neural architectures with randomized priors. Using appropriate architecture choices, we show that the proposed framework can approximate functional relationships between design variables and quantities of interest, even in cases where the latter take values in high-dimensional vector spaces or even infinite-dimensional function spaces. In the context of BO, we augmented the proposed probabilistic surrogates with re-parameterized Monte Carlo approximations of multiple-point (parallel) acquisition functions, as well as methodological extensions for accommodating black-box constraints and multi-fidelity information sources. We test the proposed framework against state-of-the-art methods for BO and demonstrate superior performance across several challenging tasks with high-dimensional outputs, including a constrained multi-fidelity optimization task involving shape optimization of rotor blades in turbo-machinery.

Corruption and intrapreneurship

This work analyzes for the first time the impact of the perception of corruption on intrapreneurship. The Global Entrepreneurship Monitor (GEM) database and Transparency International information allowed us to implement a cross-country panel data analysis. The results show that the perception of corruption sands the wheels of intrapreneurship. The results take into account the low dispersion of the dependent variable and are robust to alternative proxies of corruption and different sets of control variables. As a further test, there is evidence of a quadratic relationship between corruption and intrapreneurship for less economically developed countries, which is indicative of differences in the aforementioned relationship for diverse levels of economic development.

Covariate selection in causal learning under non-Gaussianity.

Understanding causal mechanisms is a central goal in the behavioral, developmental, and social sciences. When estimating and probing causal effects using observational data, covariate adjustment is a crucial element to remove dependencies between focal predictors and the error term. Covariate selection, however, constitutes a challenging task because availability alone is not an adequate criterion to decide whether a covariate should be included in the statistical model. The present study introduces a non-Gaussian method for covariate selection and provides a forward selection algorithm for linear models (i.e., non-Gaussian forward selection; nGFS) to select appropriate covariates from a set of potential control variables to avoid inconsistent and biased estimators of the causal effect of interest. Further, we demonstrate that the forward selection algorithm has properties compatible with principles of direction of dependence, i.e., probing whether the causal target model is correctly specified with respect to the causal direction of effects. Results of a Monte Carlo simulation study suggest that the selection algorithm performs well, in particular when sample sizes are large (i.e., n ≥ 250) and data strongly deviate from Gaussianity (e.g., distributions with skewness beyond 1.5). An empirical example is given for illustrative purposes.

A vaccine for volatility? An empirical analysis of global stock markets and the impact of the COVID-19 vaccine

The oscillation of COVID-19 growth has had a sustaining impact on financial markets. This study investigates the asymmetric impact of COVID-19 growth and recovery on financial markets. Examining ten epicenters of the virus from 01/01/2021 to 31/12/2021, we utilize a stepwise regression methodology and a diverse set of control variables. Controlling for volatility, credit risk, liquidity risk, monetary policy, gold, and oil, our findings indicate a significant impact of COVID-19 on equity indices. Vaccination growth correlates with positive price movements in the USA, UK, China, Japan, France, and Spain. Simultaneously, negative price trends align with virus growth in the USA, UK, China, Japan, Spain, and World models. A nexus of causality between COVID-19, global oil markets, and equity prices is identified, while credit and liquidity risks emerged as significant risk factors in China. Our results highlight the pertinence of swift vaccine developments, lockdown interventions, and central bank responses, providing valuable insights to governments, regulators, and all financial market stakeholders.

Can the tone of central bankers’ speeches discourage potential bank borrowers in the Eurozone?

The discouragement of potential bank borrowers is a credit market outcome, where discouraged firms are those who need bank credit but do not apply for it due to fear of rejection. Discouragement may arise from behavioural factors shaping firms’ perceptions of the probability of being rejected. We examine whether central bank communication, captured by the tone of central bankers’ speeches, affects the observed discouragement likelihood. The results document a robust negative relationship between the tone of central bankers’ speeches and borrowers’ discouragement across Eurozone countries. This result remains intact in the presence of a wide set of firm-specific and macroeconomic control variables and robustness tests.