Regression Estimates Research Articles

A new reading of the relationship between financial development, trade openness, vulnerability and economic growth in Africa: New perspectives from method of moment’s quantile regression

One of the most frequently debated subjects in international forums is economic growth, which is regarded as a global priority. Consequently, researchers have turned their attention from conventional economic growth at a single average coefficient to divisible economic growth at levels of its value. Although the existing literature has discussed several determinants of economic growth, our article contributes to examining the sources of economic growth in African countries during the generations of reforms from 1990 to 2019 and in the context of economic vulnerability. The variables used in the analysis are gross domestic product, trade openness, financial development, and economic vulnerability. The study uses a quantile regression econometric model to examine these variables at different stages of reform. Quantile regression (QR) estimates for quantiles 0.05 to 0.95 showed mixed results: financial development is favorable to African economic growth at all quantile levels. However, economic vulnerability is a major impediment to economic growth at all quantile levels. In addition, it was found that a high degree of trade openness has a detrimental effect on African economic growth from quantile 0.5 of the dependent variable. Finally, another important result proves that financial development is a remedy for decision-makers against economic vulnerability.

Exposure to household air pollution and childhood multimorbidity risk in Jimma, Ethiopia

BackgroundChildhood multimorbidity, characterized by the simultaneous occurrence of multiple medical conditions in children, is a global concern. Notably, exposure to household air pollution has been linked to various health issues, particularly affecting vulnerable segments of the population residing in poorly ventilated homes. However, evidence regarding the impact of household air pollution on the risk of multimorbidity in low-income settings remains scarce. Therefore, this study aims to investigate the association between household air pollution and childhood multimorbidity in Jimma, Ethiopia.MethodsA comparative cross-sectional study was conducted to collect data from 280 children under the age of five who lived in households using solid fuel (n = 140) and clean fuel (n = 140). The Demographic Health Survey morbidity questionnaire was used to collect information from mothers about common childhood illnesses. Multiple logistic regression analysis was employed to explore the relationship between the use of solid fuel for cooking in households and the likelihood of childhood multimorbidity. In addition, Poisson regression estimation was used to determine if exposure to solid fuel could increase the number of morbidities.ResultsThe overall prevalence of childhood multimorbidity was 34.3% [95% CI: 0.29–0.40]. Among these cases, 23.9% were among children from solid fuel user households, whereas about 10.4% were from clean fuel user households. Adjusted for all possible socioeconomic, demographic, water, sanitation, hygiene, and health care covariates, children living in solid fuel user households had more than three times the odds of childhood multimorbidity compared to children living in clean fuel user households (AOR = 3.14, 95% CI [1.42–6.95], p < 0.001). Moreover, household air pollution from solid fuel use was positively associated with an increased number of individual morbidity conditions, with an adjusted β coefficient of 0.46 (IRR = 1.58, 95% CI [1.17–2.13], p = 0.003).ConclusionSolid fuel use was an independent predictor of childhood morbidity risk. Efficient policies and strategies, such as the integration of environmental regulation policies into the healthcare system aimed at the reduction of harmful air pollutants and their adverse health effects on children, need to be implemented.

Cost-utility analysis of provision of e-cigarette starter kits for smoking cessation in emergency departments: An economic evaluation of a randomized controlled trial.

To assess the cost-effectiveness of the Cessation of Smoking Trial in Emergency Department (COSTED) intervention compared with signposting to local stop smoking service (SSS) from the National Health Service (NHS) and personal social services (PSS) perspective. This was a two-group, multi-centre, pragmatic, individually randomized controlled trial set in six Emergency Departments (EDs) in urban and rural areas in the United Kingdom. Adult (≥ 18 years) daily smokers (at least one cigarette or equivalent per day) but not daily e-cigarette users, with carbon monoxide reading ≥8parts per million, attending the ED (n= 972) were included. The intervention consisted of provision of an e-cigarette starter kit plus brief smoking cessation advice and referral to a local SSS. Control was an information card on how to access local SSS. Intervention costs included costs of training and delivery. Control costs included costs of printing information cards. Costs of smoking cessation and health-care services were estimated based on quantities reported by participants and unit costs extracted from secondary sources. The effects were measured by quality-adjusted life years (QALYs) derived from EQ-5D-5L. Other outcomes were smoking cessation measures. The primary outcome was incremental cost-effectiveness ratio (ICER), which was calculated by dividing the difference in costs by the difference in QALYs between groups. The mean intervention costs were £48 [standard error (SE) = £0] per participant and the mean control costs were £0.2 (SE = £0) per participant. Using regression estimates, total costs were £31 [95% confidence interval (CI) = -£341 to £283] higher and 6-month QALYs were 0.004 (95% CI = -0.004 to 0.014) higher in the intervention group than in the control group. The ICER was calculated at £7750 (probability of cost-effective at range £20 000-30 000: 72.2-76.5%). The UK Cessation of Smoking Trial in Emergency Department (COSTED) intervention (provision of an e-cigarette starter kit plus brief smoking cessation advice) was cost-effective compared with signposting to local stop smoking services under the current recommendations of the maximum acceptable thresholds.

Asymptotic normality of kernel functional regression estimator based upon twice censored data

Asymptotic normality of kernel functional regression estimator based upon twice censored data

Spline Estimator in Nonparametric Ordinal Logistic Regression Model for Predicting Heart Attack Risk

In Indonesia, one of the main causes of death for both young and elderly people is heart attacks, and the main cause of heart attacks is non-communicable diseases such as hypertension. Deaths due to heart attacks caused by non-communicable diseases, namely hypertension, rank first in Indonesia. Therefore, predictions of the risk of having a heart attack caused by hypertension need serious attention. Further, for determining whether a patient is experiencing a heart attack, an effective method of prediction is required. One efficient approach is to use statistical models. This study discusses predicting risk of heart attack via modeling and classifying hypertension risk based on factors that influence it, namely, age, cholesterol levels, and triglyceride levels by using the spline estimator of the Nonparametric Ordinal Logistic Regression (NOLR) model. In this study, we assume an ordinal scale response variable with q categories to have an asymmetric distribution, namely, a multinomial distribution. The data used in this study are secondary data from medical records of cardiac poly patients at the Haji General Hospital in Surabaya, Indonesia. The results show that the proposed model approach has the greatest classification accuracy and sensitivity values compared to NOLR model approach using GAM, and the classical model approach, namely the Parametric Ordinal Logistic Regression (POLR) model. This means that the NOLR model approach is suitable for predicting hypertension and heart attack risks. Also, the NOLR model estimated using the LS-Spline estimator obtained is valid for predicting the risk of heart attack with accuracy value of 85% and sensitivity value of 100%.

Sea‐Level Rise, Drinking Water Quality and the Economic Value of Coastal Tourism in North Carolina

AbstractWe estimate the economic benefits of avoiding reductions in drinking water quality due to sea level rise accruing to North Carolina (NC) coastal tourists. Using stated preference methods and responses from recent coastal visitors, we find that tourists are 2%, 8%, and 11% less likely to take an overnight trip if drinking water tastes slightly, moderately, or very salty at their chosen destination. The majority of those who decline a trip would take a trip to another NC beach without water quality issues, others would take another type of recreational trip, with a minority opting to stay home. Willingness to pay for an overnight beach trip declines with the salty taste of drinking water. We find evidence of attribute non‐attendance in the stated preference data, which impacts the regression model and estimates of the willingness to pay for trips. Combining economic and hydrological models, annual aggregate benefit losses due to low drinking water quality could be as high as $232 million by 2040.

An investigation of the impact of energy performance certificate (EPC) ratings on residential property prices in Oxfordshire: a hedonic study

ABSTRACT This paper investigates whether energy performance ratings measured by Energy Efficiency Certificates (EPCs) affect the sales prices of residential properties in Oxfordshire, UK. The study makes use of 186,913 sales transactions that took place between 1995 and 2023. Using a hedonic regression estimation, a nonlinear pattern between energy efficiency levels and the sales prices of dwellings, are observed. The results also show that there is evidence of a significant positive relationship between EPC ratings and detached, semi-detached, terraced houses and flats. In the second part of this study, we examine the effects of age, county and type of dwelling on price premiums. To the best of the authors’ knowledge, this study is the only study, using the most comprehensive literature; and the largest, most up to date available data in analysing the Oxfordshire real-estate market, in terms of energy efficiency and price premiums.

On using the penalized regression estimators to solve the multicollinearity problem

The paper compares coefficient parameter estimation efficiency using penalized regression approaches. Five estimators are employed: Ridge Regression, LASSO regression, Elastic Net (ENET) Regression, Adaptive Lasso (ALASSO) regression, and Adaptive Elastic Net (AENET) regression methods. The study uses a multiple linear regression model to address multicollinearity issues. The comparison is based on average mean square errors (MSE) using simulated data with varying sizes, numbers of independent variables, and correlation coefficients. The results are expected to be useful and will be applied to real data to determine the best-performing estimator.

Multi-task Learning for Gaussian Graphical Regressions with High Dimensional Covariates

Gaussian graphical regression is a powerful approach for regressing the precision matrix of a Gaussian graphical model on covariates, which permits the response variables and covariates to outnumber the sample size. However, traditional approaches of fitting the model via separate node-wise lasso regressions overlook the network-induced structure among these regressions, leading to high error rates, particularly when the number of nodes is large. To address this issue, we propose a multi-task learning estimator for fitting Gaussian graphical regression models, which incorporates a cross-task group sparsity penalty and a within-task element-wise sparsity penalty to govern the sparsity of active covariates and their effects on the graph, respectively. We also develop an efficient augmented Lagrangian algorithm for computation, which solves subproblems with a semi-smooth Newton method. We further prove that our multi-task learning estimator has considerably lower error rates than the separate node-wise regression estimates, as the cross-task penalty enables borrowing information across tasks. We examine the utility of our method through simulations and an application to a gene co-expression network study with brain cancer patients.

Some unified results on isotonic regression estimators of order restricted parameters of a general bivariate location/scale model

Some unified results on isotonic regression estimators of order restricted parameters of a general bivariate location/scale model

Bayesian Empirical Likelihood Regression for Semiparametric Estimation of Optimal Dynamic Treatment Regimes.

We propose a semiparametric approach to Bayesian modeling of dynamic treatment regimes that is built on a Bayesian likelihood-based regression estimation framework. Methods based on this framework exhibit a probabilistic coherence property that leads to accurate estimation of the optimal dynamic treatment regime. Unlike most Bayesian estimation methods, our proposed method avoids strong distributional assumptions for the intermediate and final outcomes by utilizing empirical likelihoods. Our proposed method allows for either linear, or more flexible forms of mean functions for the stagewise outcomes. A variational Bayes approximation is used for computation to avoid common pitfalls associated with Markov Chain Monte Carlo approaches coupled with empirical likelihood. Through simulations and analysis of the STAR*D sequential randomized trial data, our proposed method demonstrates superior accuracy over Q-learning and parametric Bayesian likelihood-based regression estimation, particularly when the parametric assumptions of regression error distributions may be potentially violated.

Macroeconomic volatility and terrorism incidents in Africa

AbstractThis article investigates the impact of macroeconomic volatility on terrorism in 38 African economies spanning the period from 1980 to 2012, using available data. It examines four categories of terrorism markers: domestic, transnational, uncertain, and total, and their correlation with the volatility of four macroeconomic indicators—inflation, output growth, domestic credit to the private sector, and government expenditures. The study employs the negative binomial regression estimator on both the conditional variance structures of the generalized autoregressive conditional heteroskedasticity (GARCH) and exponential GARCH (EGARCH) and the unconditional measure of a 3‐year interval time varying of measuring macroeconomic variables' volatilities on other regressors. I find, first, that inflation volatility is a significant predictor of all terrorism markers, except for transnational terrorism. This implies that high inflation volatility is linked to increased domestic terrorism. Second, the role of domestic credit to the private sector in mitigating transnational terrorist activities highlights the importance of stable private sector access to credit in reducing such incidents. Third, government consumption expenditure volatility has a dual effect on terrorism: it amplifies domestic terrorism but serves as a mitigating factor for total terrorism. This indicates that stable government expenditure can reduce overall terrorism, but might increase domestic terrorism. Last, other variables—such as population, ethnicity, conflicts, surface areas, and lagged terrorism values—are also found to influence the examined terrorism measures. For robustness, I employ alternative statistical techniques, including Poisson Pseudo‐maximum likelihood (PPML) estimation with multiple high‐dimensional fixed effects (HDFE). In summary, this study emphasizes the critical role of addressing inflation volatility in macroeconomic policies as a primary measure to counter terrorism. Stable economic conditions, particularly in relation to inflation, can significantly contribute to reducing terrorism in African economies.

Economic Diverification in Nigeria: Agriculture in Perspective

This study examines economic diversification: agricultural in perceptive. In particular the study examines the impact of investment in agricultural sector in achieving economic diversification between periods spanning from the years 2000 to year 2022. Time series secondary data were sourced from Central Bank Statistical Bulletin (2022) and World Bank Development Indicators (2024). Autoregressive Distributed Lag Regression Estimate (ARDL) analysis was conducted with the aid of E-view 10. Findings of this study reveals that agriculture credit guarantee scheme fund for cash crops and livestock have negative and significant impact on economic diversification. More so, agriculture credit guarantee scheme fund for food crops equally has negative and insignificant impact on economic diversification. However, grand total of agriculture credit guarantee scheme fund on agricultural commodities has positive and insignificant impact on economic diversification. Based on study findings, the following recommendations are suggested; that governments at all levels should as a matter of urgency should sustain funding of agriculture sectors and provide necessary infrastructure in the rural areas where majorities of agricultural activities take place in order to diversify her economy. More so, stakeholders in the agricultural sectors mainly the farmers should take advantage of loanable fund available to them and channelled it to appropriate crops that will yield investment return. In addition, it is also recommended that monetary policy authority should look inward to determine why agricultural credit guarantee scheme fund has negative significant impact on all the three categories of agricultural commodities identified. This can be achieved through close monitoring and adequate supervision of the loan disburse to the direct beneficiaries. Lastly, investment in human capacity in agricultural sector is away forward for economic diversification.

Evolutionary origins, macroevolutionary dynamics, and climatic niche space of the succulent plant syndrome in the Caryophyllales.

The succulent plant syndrome is defined by the coordination of traits that enhance internal water storage within plant tissues. Although distributed globally in different habitats, succulent plants are thought to have evolved to avoid drought in arid regions, due to trait modifications that decrease tissue water deficits. We evaluated the evolution and the ecological significance of the succulent strategy at a global scale by comparing the climatic niche of species displaying succulence within the Core Caryophyllales with their non-succulent relatives. We assembled and curated a worldwide dataset of 201K georeferenced records belonging to 5447 species within 28 families, and analyzed the climatic niche of species along with their origin and evolutionary trajectories using ecological niche modeling, phylogenetic regression, divergence dates and ancestral state estimation. Results indicate the Core Caryophyllales have inhabited drylands since their origin in the Early Cretaceous. However, the succulent syndrome appeared and diversified during later geologic periods. The climatic niche space of succulents is narrower than non-succulent relatives, but no climate niche separation was detected between groups. Our results support alternative interpretations on the environmental and ecological forces that spurred the origin and diversification of the succulent plant syndrome and the radiation of rich succulent lineages.

Does participation in a court-based trauma-focused evaluation program influence permanency outcomes for children involved in the child welfare system?

BackgroundChildren involved in the child welfare system, especially those in out-of-home care, experience high rates of mental health symptoms. Mental health symptoms have been linked to worse permanency outcomes in this population. ObjectiveThe present study aimed to evaluate whether children who received a trauma-focused evaluation, compared to children who received usual care, experienced differential permanency outcomes, as well as other court-related outcomes (e.g., legal system involvement). Participants and settingOne-hundred and twenty-five children (58% female, Mage = 10.61) from a family court in the Northeastern United States participated in a trauma-focused evaluation program. MethodsData for the current study were collected via a retrospective chart. Participants were recruited at a family court of a Northeastern state. Between-groups differences on court-related outcomes were assessed using logistic regression and Kaplan Meier estimates. FindingsThere were no significant differences between children who received the trauma-focused evaluation program versus usual care in the likelihood for guardianship changes, termination of parental rights, new child welfare petitions or new juvenile legal petitions. The groups significantly differed in the likelihood of case closure; children who received the trauma-focused evaluation program were more likely to experience case closure compared to children who received usual care. Additionally, on average, children who received the trauma-focused evaluation program achieved case closure almost 8 months sooner. ConclusionsThe findings suggest that initiatives aimed at identifying mental health concerns and linking children placed in out-of-home to relevant mental health services care can have a significant and positive impact on permanency outcomes.

Deep regression learning with optimal loss function

In this paper, we develop a novel efficient and robust nonparametric regression estimator under a framework of a feedforward neural network (FNN). There are several interesting characteristics for the proposed estimator. First, the loss function is built upon an estimated maximum likelihood function, which integrates the information from observed data as well as the information from the data distribution. Consequently, the resulting estimator has desirable optimal properties, such as efficiency. Second, different from the traditional maximum likelihood estimation (MLE), the proposed method avoids the specification of the distribution, making it adaptable to various distributions such as heavy tails and multimodal or heterogeneous distributions. Third, the proposed loss function relies on probabilities rather than direct observations as in least square loss, contributing to the robustness of the proposed estimator. Finally, the proposed loss function involves a nonparametric regression function only. This enables the direct application of the existing packages, simplifying the computational and programming requirements. We establish the large sample property of the proposed estimator in terms of its excess risk and minimax near-optimal rate. The theoretical results demonstrate that the proposed estimator is equivalent to the true MLE where the density function is known in terms of excess risk. Our simulation studies show that the proposed estimator outperforms the existing methods based on prediction accuracy, efficiency and robustness. Particularly, it is comparable to the MLE with the known density and even gets slightly better as the sample size increases. This implies that the adaptive and data-driven loss function from the estimated density may offer an additional avenue for capturing valuable information. We further apply the proposed method to four real data examples, resulting in significantly reduced out-of-sample prediction errors compared to existing methods.

Women digital financial inclusion and economic growth in Nigeria

PurposeThis article analyses the trend in women digital financial inclusion in Nigeria using some digital financial inclusion indicators obtained from the global Findex database for the year 2014, 2017 and 2021. The study also analyses the relationship between women digital financial inclusion and economic growth.Design/methodology/approachThe data were analysed using the two-stage least squares (2SLS) and generalised method of moments (GMM) regression estimation methods. The women digital financial inclusion indicators are the percentage of women who (1) own a mobile money account, (2) made a digital payment, (3) received digital payments, (4) made or received a digital payment, (5) own a credit card and (6) own a debit card.FindingsThe trend analysis shows a sustained, although small, improvement in women mobile money account ownership during the period, while the other indicators witnessed a decrease in 2017 and an increase in 2021, except for women credit card ownership which remained at the same level during the period examined. There is a significant positive relationship between women digital financial inclusion and economic growth. Internet usage has a significant positive effect on women digital financial inclusion in Nigeria.Practical implicationsGreater digital financial inclusion for women can accelerate economic growth in Nigeria. Policymakers should encourage investment in fintech and broaden access to the Internet to increase women digital financial inclusion and economic growth in Nigeria. Policymakers and practitioners in Nigeria should also work collaboratively to increase digital financial inclusion for women due to its potential to increase economic growth in Nigeria.Originality/valueExisting studies did not analyse the trends in women digital financial inclusion. Existing studies did not empirically analyse the impact of women digital financial inclusion on economic growth in Nigeria. The present study fills this gap in the literature.

Evaluation and Influencing Factors of Regional Green Innovation Efficiency Based on the Lasso Method

Regional green innovation efficiency is affected by multiple factors. Based on the undesirable output of 11 provinces in the Yangtze River Economic Belt from 2012–2021, this paper uses the Super-SBM model containing undesirable outputs to measure and analyze the regional green innovation efficiency and tests the variables affecting regional green innovation efficiency through lasso regression. Stepwise regression and least squares estimation are used to prove the rationality of the lasso regression model. The results show that the regional green innovation efficiency of the Yangtze River Economic Belt during 2012–2021 among different regions has differences that manifest as the efficiency of the lower, middle, and upper reaches decreases successively, and the differences decrease gradually over time. All the variables affecting the regional green innovation efficiency pass the lasso regression variable screening test, and most of the influencing factors positively promote the regional green innovation efficiency and are more explanatory than the stepwise regression and the least squares proof model.

Social support and health behaviors of older adults during the COVID-19 pandemic in China: a moderated mediation model of loneliness and economic income

BackgroundThe literature shows that social support is an important factor influencing health behaviors. This study aimed to explore the relationships and intrinsic pathways of social support, loneliness, economic income, and health behaviors among older adults during the Corona Virus Disease 2019 (COVID-19) pandemic, and to provide a theoretical basis for the implementation of health behaviors interventions for older adults.MethodsA cluster-random-sampling survey was adopted within two towns in Dongguan, China. Demographic characteristics, social support, loneliness, economic income and health behaviors were measured. The Social Support Appraisals scale (SS-A), the ULS-8 Loneliness Scale, and the Self-rated abilities for health practice scale (SRAHPS) were used to measure social support, loneliness, and health behaviors in older adults, respectively. A moderated mediation model was built to examine the relationships among social support, loneliness, economic income, and health behaviors using the SPSS PROCESS 4.0 macro. We conducted bootstrapping of regression estimates with 5000 samples and a 95% confidence interval.Results621 older adults completed the questionnaire. Most of the participants were female, accounting for 75.0%, and the average age was 81.11 years (SD = 8.11). The median (interquartile range) of the participants’ average monthly economic income was 800 (500–1000)RMB. The results of the mediation analysis showed that loneliness partly mediated the relationship between social support and health behaviors (B = 0.024, 95%CI: 0.007, 0.042), with the mediating effect accounting for 4.56% of the total effect. The moderation mediation analysis revealed a positive moderating role of economic income in the relationship between social support and loneliness (B = 0.114, 95%CI: 0.054, 0.174). Specifically, the relationship between social support and loneliness was found to be weaker for older adults with a high economic income compared to those with a lower economic income.ConclusionThe provision of enhanced social support and the alleviation of loneliness among older adults during an epidemic can facilitate the development of healthy behaviours, particularly among those who are economically disadvantaged.

Signaling trust during disruptions: perceived gender differences in trustworthy leadership traits

Purpose This study aims to examine differences in the self-perception and value of four traits associated with trustworthiness: transparency, credibility, accountability and honesty to find out whether these traits are more likely to be adopted by female leaders during episodes of disruption. Design/methodology/approach Regression estimation of perceived trustworthy leadership values using a survey of full-time employed men and women in management working for various organizations. Findings Honesty emerged as the highest rated trait overall, both in the respondents’ self-measure and in their reflected level of desirability. However, women described their honesty as higher than how men relayed their honesty score. Both genders rated their own transparency as their lowest scoring trait and treated it as the least important of the desired traits. Relative to men, not only do women espouse to greater levels of honesty and value it more but they also appear to embrace honesty in their desired behaviors more highly. Research limitations/implications A natural extension of this study is to design a longitudinal study to show causality or whether socialized gender differences, in fact, play a role in measuring the value of trustworthy leadership during disruptions. Practical implications Understanding self-perceptions of leaders’ efficacy and desired expectations helps prepare both current and future leaders to regain followers’ trust, especially during disruptions. Lessons learned to date require investments in building awareness of leaders’ perceptions of trustworthiness and developing relevant skills for dealing with disruptions. Originality/value This research adds further insight to leadership studies by establishing a linkage between characteristics of trustworthy leadership and the ability to lead in challenging times. Cultivating traits of transparency, accountability, credibility and honesty enables leaders to develop the skills needed to be viewed as trustworthy and be prepared for the unexpected.