Does US monetary policy uncertainty affect returns of Asian Developed, emerging, and frontier equity markets? Empirical evidence by using the quantile-on-quantile approach.

This paper examines the impact of the Monetary Policy Uncertainty (MPU) of the United States on Asian developed, emerging, and frontier stock markets using a Quantile-on-Quantile (QQR) approach by using monthly data from January 2006 to December 2022 of 14 Asian countries. The study finds that US monetary policy significantly negatively influences Asian stock markets. This is primarily due to the widespread use of the US dollar as a universal currency, resulting in substantial ripple effects on other nations through trade relationships. In Asian developed markets, MPU is negatively related to Australia and New Zealand. At the same time, it has a positive relationship with Hong Kong and Japan at the upper quantiles. Among Asian emerging markets, MPU negatively impacts Taiwan's, India's, and China's returns, increasing this negative relationship at higher MPU quantiles. Additionally, MPU has a significant negative relationship with Thailand, Indonesia, Korea, and Malaysia returns. In contrast, higher quantiles of MPU have no discernible impact on the Philippines stock returns. In Asian frontier markets, MPU negatively impacts Pakistan's and Sri Lanka's returns. The implications of these findings are twofold: for investors, this study provides valuable insights for hedging activities, allowing for more informed decisions based on the MPU of other countries to identify profitable stocks. For policymakers, this research aids in formulating effective monetary policy strategies. Furthermore, future studies can build upon these results by exploring other markets and comparing their outcomes with the findings presented in this study.

A mark-specific quantile regression model.

Quantile regression has become a widely used tool for analysing competing risk data. However, quantile regression for competing risk data with a continuous mark is still scarce. The mark variable is an extension of cause of failure in a classical competing risk model where cause of failure is replaced by a continuous mark only observed at uncensored failure times. An example of the continuous mark variable is the genetic distance that measures dissimilarity between the infecting virus and the virus contained in the vaccine construct. In this article, we propose a novel mark-specific quantile regression model. The proposed estimation method borrows strength from data in a neighbourhood of a mark and is based on an induced smoothed estimation equation, which is very different from the existing methods for competing risk data with discrete causes. The asymptotic properties of the resulting estimators are established across mark and quantile continuums. In addition, a mark-specific quantile-type vaccine efficacy is proposed and its statistical inference procedures are developed. Simulation studies are conducted to evaluate the finite sample performances of the proposed estimation and hypothesis testing procedures. An application to the first HIV vaccine efficacy trial is provided.

Environmental exposures to organophosphorus flame retardants in early pregnancy and risks of gestational diabetes mellitus: a nested case-control study.

OPFRs are emerging environmental pollutants with reproductive and endocrine toxicity. This study aimed to examine the association between environmental exposure to OPFRs during early pregnancy and GDM. This nested case-control study was based on a birth cohort that was constructed at a maternal and child health hospital, including 74 cases of GDM among 512 pregnant women. The OPFRs, including TBP, TBEP, TCEP, TDCPP, TMCP, TOCP, and TPHP during 10-14 weeks of pregnancy were determined using GC-MS. The association between the OPFRs and GDM was assessed using WQS and BKMR models. The levels of OPFRs were significantly elevated in GDM patients (60) compared with the controls (90). The WQS analysis showed that mixtures of the OPFRs were significantly associated with GDM (OR 1.370, 95% CI 1.036-1.810, P = 0.027), and TBP, TPHP, and TMCP were the major contributors to the mixed exposure effect. In the BKMR model, individual exposure to TBP, TPHP, and TMCP, and the interaction of TMCP with TBP and TPHP were significantly associated with GDM. Environmental exposure to OPFRs is positively associated with GDM. These findings provide evidence for the adverse effects of OPFR exposure on the health of pregnant women.

Iodine Status of Infants and Toddlers under 2 Years of Age and Its Association with Feeding Behaviors and Maternal Iodine Status in Shanghai: A Quantile Regression Analysis.

It is crucial to provide adequate iodine nutrition to infants and toddlers for proper thyroid function and subsequent brain development. Infants are particularly vulnerable to iodine deficiency during the transition from a milk-based diet (breast milk and/or infant formula) to solid food. This study examines the current iodine levels of children during their first two years of life and investigates the association between these levels and feeding behaviors and the iodine status of their mothers in Shanghai, a city located in eastern China. A hospital-based cohort study was conducted to enroll mother-child pairs, where the child is aged 6-23 months, who visited community health service centers in the 16 districts of Shanghai, China. Data on socio-demographic factors and feeding behavior data were collected from the participants. The urinary iodine concentration (UIC) in both the young children and their mothers were analyzed. A total of 2282 mother-child pairs were included in this analysis. The median (p25-p75) UIC for lactating women, weaning women, and children were 121.3 µg/L (68.1-206.4 µg/L), 123.4 µg/L (58.4-227.2 µg/L), and 152.1 µg/L (75.8-268.3 µg/L), respectively. The UIC in children was found to be higher than that in their mothers (p < 0.001). Children who consumed less than 500 mL per day of formula milk in the last week had lower UICs compared with those who consumed 500 mL per day or more (p = 0.026). Furthermore, the children's UIC was positively correlated with the maternal UIC (rs = 0.285, p < 0.001). Multiple quantile regression analysis revealed a statistically significant positive association between maternal UIC and children's UIC between the 0.1 and 0.9 quantiles (all p < 0.001). We found that the iodine status of infants and toddlers, as well as of mothers, was sufficient. However, a large minority of children and mothers may be at risk of iodine deficiency. Furthermore, no associations between children's UIC and feeding behaviors were observed. Moreover, there was a positive correlation between the UIC of young children and their mothers.

A new family of quantile regression models applied to nutritional data.

This paper introduces a new family of quantile regression models whose response variable follows a reparameterized Marshall-Olkin distribution indexed by quantile, scale, and asymmetry parameters. The family has arisen by applying the Marshall-Olkin approach to distributions belonging to the location-scale family. Models of higher flexibility and whose structure is similar to generalized linear models were generated by quantile reparameterization. The maximum likelihood (ML) method is presented for the estimation of the model parameters, and simulation studies evaluated the performance of the ML estimators. The advantages of the family are illustrated through an application to a set of nutritional data, whose results indicate it is a good alternative for modeling slightly asymmetric response variables with support on the real line.

Sustainable development through green transition in EU countries: New evidence from panel quantile regression.

Sustainable development addresses global challenges by promoting practices that balance economic, social, and environmental considerations. Key factors include the shifting to green energy and the integrating of green technology in the sustainable development process. This study investigates the heterogenous effects of green technology development, green energy, R&D expenditures, FDI, economic growth, and urbanization on CO2 emissions in 25 European Union (EU) countries using panel quantile regression over the period 2000-2021. The results, based on panel quantile regression, indicate that green energy decreases CO2 emissions from the 10th to the 90th quantiles, while green technology development increases CO2 emissions at the lower quantiles (10th to 60th) and then turns negative. The robustness of the fixed effect model also confirms the findings of the study. Additionally, panel causality tests indicate no causal link between green technology development and CO2 emissions, but there is bidirectional causality between green energy and CO2 emissions. Therefore, the findings highlight that policymakers should thoroughly evaluate measures and strategies to encourage the development of green technologies and green energy sources to reduce high levels of CO2 emissions. One strategy is to provide financial aid and support technological advances to produce green energy at reduced costs.

Testing the EKC hypothesis for ecological and carbon intensity of well-being: The role of forest extent.

The G-20 countries represent a considerable percentage of the global economy and are crucial in matters to do with support for environmental sustainability. The uniqueness of this study lies in determining the effects of forests on human well-being and environmental degradation with respect to G20, offering a unique perspective regarding the efforts to battle climate change. The study analyzed the impact of income, forest extent and education on ecological and carbon intensity of well-being following the Environmental Kuznets Curve (EKC) hypothesis. Based on annual data from 1990 to 2022 and employing the Method of Moments Quantile Regression, the results validate the presence of an inverted U-shaped relationship between GDP and environmental well-being which refers to the existence of EKC. Our results connect improved ecological and carbon intensity of well-being with expanding forest extent and improving education levels. Forest management combined with educational management work as an effective mechanism reducing environmental degradation while also positively contributing to human well-being. In addition, through these informed and rational decisions, policy makers can promote the environmental stability of forests.

Distance-decay equations of antibiotic resistance genes across freshwater reservoirs.

Distance-decay (DD) equations can discern the biogeographical pattern of organisms and genes in a better way with advanced statistical methods. Here, we developed a data Compilation, Arrangement, and Statistics framework to advance quantile regression (QR) into the generation of DD equations for antibiotic resistance genes (ARGs) across various spatial scales using freshwater reservoirs as an illustration. We found that QR is superior at explaining dissemination potential of ARGs to the traditionally used least squares regression (LSR). This is because our model is based on the 'law of limiting factors', which reduces influence of unmeasured factors that reduce the efficacy of the LSR method. DD equations generated from the 99th QR model for ARGs were 'Sall = 90.03e-0.01Dall' in water and 'Sall = 92.31e-0.011Dall' in sediment. The 99th QR model was less impacted by uneven sample sizes, resulting in a better quantification of ARGs dissemination. Within an individual reservoir, the 99th QR model demonstrated that there is no dispersal limitation of ARGs at this smaller spatial scale. The QR method not only allows for construction of robust DD equations that better display dissemination of organisms and genes across ecosystems, but also provides new insights into the biogeography exhibited by key parameters, as well as the interactions between organisms and environment.

[Spatiotemporal Interaction Characteristics and Transition Mechanism of Carbon Intensity in China's Transportation Industry].

This research was conducted using many spatial analysis approaches to dissect the spatiotemporal interactive characteristics of carbon emission intensity within the transportation sector from 2002 to 2020. An in-depth exploration of their transition mechanisms was conducted by nesting the obtained timewarp types with the panel quantile model. Finally, the geodetector model aligned with different transition mechanisms was employed to investigate and analyze the interaction effects among various factors influencing carbon intensity in the transportation sector. The results indicated that:① The carbon emission intensity of the transportation sector in 30 provinces and regions of China showed an overall downward trend with fluctuations, and the spatial clustering level was relatively stable. ② The spatiotemporal interactive features of ESTDA revealed that the relationship between the northwest region and its adjacent spatial units was unstable, with significant variations and fluctuations. In contrast, economically developed areas such as coastal cities in the eastern part had established mature transportation networks, resulting in a relatively stable local spatial pattern, though a few areas still exhibited spatiotemporal competitiveness. ③ The spatiotemporal transition of carbon intensity in the transportation sector could be categorized into four driving or constraining modes(the population economy urbanization constraint model, population economy urbanization facility constraint model, technology consumption industry-driven model, and technology industry regulation-driven model). Most provinces were influenced by the low quantile constraint and high quantile drive modes, with only a few affected by the high quantile constraint and low quantile drive modes, the majority of which were located in the northwest or southwest regions. ④ Further, we introduced the geographical detector model based on the identified mechanism of carbon emission intensity transition in the transportation sector, emphasizing the coordinated development of multiple factors and strengthening inter-regional collaborative governance.

The association between prenatal oxidative stress levels measured by isoprostanes and offspring neurodevelopmental outcomes at 36 months.

Oxidative stress during pregnancy has been a mechanistic pathway implicated in autism development, yet few studies have examined this association directly. Here, we examined the association of prenatal levels of 8-iso-PGF2α, a widely used measure of oxidative stress, and several neurodevelopmental outcomes related to autism in children. Participants included 169 mother-child pairs from the Early Autism Risk Longitudinal Investigation (EARLI), which enrolled mothers who had an autistic child from a previous pregnancy and followed them through a subsequent pregnancy and until that child reached age 3 years. Maternal urine samples were collected during the second trimester of pregnancy and were later measured for levels of isoprostanes. Child neurodevelopmental assessments included the Mullen Scales of Early Learning (MSEL), the Social Responsiveness Scale (SRS), and the Vineland Adaptive Behavior Scale (VABS), and were conducted around 36 months of age. Primary analyses examined associations between interquartile range (IQR) increases in 8-iso-PGF2α levels, and total composite scores from each assessment using quantile regression. In adjusted analyses, we did not observe statistically significant associations, though estimates suggested modestly lower cognitive scores (ß for MSEL = -3.68, 95% CI: -10.09, 2.70), and minor increases in autism-related trait scores (ß for SRS T score = 1.68, 95% CI: -0.24, 3.60) with increasing 8-iso-PGF2α. These suggestive associations between decreased cognitive scores and increased autism-related traits with increasing prenatal oxidative stress point to the need for continued investigation in larger samples of the role of oxidative stress as a mechanistic pathway in autism and related neurodevelopmental outcomes.

Age at death during the Covid-19 lockdown in French metropolitan regions: a non parametric quantile regression approach.

BACKGROUND: Lockdowns have been implemented to limit the number of hospitalisations and deaths during the first wave of 2019 coronavirus disease. These measures may have affected differently death characteristics, such age and sex. France was one of the hardest hit countries in Europe with a decreasing east-west gradient in excess mortality. This study aimed at describing the evolution of age at death quantiles during the lockdown in spring 2020 (17 March-11 May 2020) in the French metropolitan regions focusing on 3 representatives of the epidemic variations in the country: Bretagne, Ile-de-France (IDF) and Bourgogne-Franche-Comté (BFC). METHODS: Data were extracted from the French public mortality database from 1 January 2011 to 31 August 2020. The age distribution of mortality observed during the lockdown period (based on each decile, plus quantiles 1, 5, 95 and 99) was compared with the expected one using Bayesian non-parametric quantile regression. RESULTS: During the lockdown, 5457, 5917 and 22 346 deaths were reported in Bretagne, BFC and IDF, respectively. An excess mortality from + 3% in Bretagne to + 102% in IDF was observed during lockdown compared to the 3 previous years. Lockdown led to an important increase in the first quantiles of age at death, irrespective of the region, while the increase was more gradual for older age groups. It corresponded to fewer young people, mainly males, dying during the lockdown, with an increase in the age at death in the first quantile of about 7 years across regions. In females, a less significant shift in the first quantiles and a greater heterogeneity between regions were shown. A greater shift was observed in eastern region and IDF, which may also represent excess mortality among the elderly. CONCLUSIONS: This study focused on the innovative outcome of the age distribution at death. It shows the first quantiles of age at death increased differentially according to sex during the lockdown period, overall shift seems to depend on prior epidemic intensity before lockdown and complements studies on excess mortality during lockdowns.

Effects of geopolitical risk on environmental sustainability and the moderating role of environmental policy stringency.

This study investigates the impact of geopolitical risk (GPR) on consumption-based carbon (CCO2) emissions as well as the moderating role of environmental policy stringency (EPS) on the above relationship. Based on data collected from 27 countries from 1990 to 2020, the basic results from the sample of the study indicate that GPR accelerates CCO2 emissions. Quantile regression results reveal that the effect of GPR is more pronounced in countries with higher CCO2 emissions. Moreover, EPS weakens the escalating effect of GPR on CCO2 emissions. The robust test results validate the findings reported in the basic regression model. The heterogeneity test indicates that the impact of GPR on CCO2 emissions is greater in developing countries compared in developed countries. The study also proposes these policy implications based on the findings: (1) countries should ensure a stable political environment, establish a robust legal system and promote energy transition; and (2) the scope of environmental taxes should be expanded where different tax rates should be imposed in order to be useful in reducing CCO2 emissions.

Physiologically informed organismal climatologies reveal unexpected spatiotemporal trends in temperature.

Body temperature is universally recognized as a dominant driver of biological performance. Although the critical distinction between the temperature of an organism and its surrounding habitat has long been recognized, it remains common practice to assume that trends in air temperature-collected via remote sensing or weather stations-are diagnostic of trends in animal temperature and thus of spatiotemporal patterns of physiological stress and mortality risk. Here, by analysing long-term trends recorded by biomimetic temperature sensors designed to emulate intertidal mussel temperature across the US Pacific Coast, we show that trends in maximal organismal temperature ('organismal climatologies') during aerial exposure can differ substantially from those exhibited by co-located environmental data products. Specifically, using linear regression to compare maximal organismal and environmental (air temperature) climatologies, we show that not only are the magnitudes of body and air temperature markedly different, as expected, but so are their temporal trends at both local and biogeographic scales, with some sites showing significant decadal-scale increases in organismal temperature despite reductions in air temperature, or vice versa. The idiosyncratic relationship between the spatiotemporal patterns of organismal and air temperatures suggests that environmental climatology cannot be statistically corrected to serve as an accurate proxy for organismal climatology. Finally, using quantile regression, we show that spatiotemporal trends vary across the distribution of organismal temperature, with extremes shifting in different directions and at different rates than average metrics. Overall, our results highlight the importance of quantifying changes in the entire distribution of temperature to better predict biological performance and dispel the notion that raw or 'corrected' environmental (and specially air temperature) climatologies can be used to predict organismal temperature trends. Hence, despite their widespread coverage and availability, the severe limitations of environmental climatologies suggest that their role in conservation and management policy should be carefully considered.

Least-cost decarbonization pathways for electricity generation in Finland: A convex quantile regression approach.

This study investigates the least-cost decarbonization pathways in the Finnish electricity generation industry in order to achieve the national carbon neutrality goal by 2035. Various abatement measures, such as downscaling production, capital investment, and increasing labor and intermediate inputs, are considered. The marginal abatement costs (MACs) of greenhouse gas emissions are estimated using the convex quantile regression method and applied to unique register-based firm-level greenhouse gas emission data merged with financial statement data. We adjust the MAC estimates for the sample selection bias caused by zero-emission firms by applying the two-stage Heckman correction. Our empirical findings reveal that the median MAC ranges from 0.1 to 3.5 euros per tonne of CO2 equivalent. The projected economic cost of a 90% reduction in emissions is 62 million euros, while the estimated cost of achieving zero emissions is 83 million euros.

Natural resources-environmental technology-ecological footprint nexus: Does natural resources rents diversification make a difference?

Researchers have shown a growing interest in investigating the environmental consequences of energy exploitation and green technologies, particularly in light of the escalating severity of climate change issues in recent times. However, these researches remain incomplete in terms of the various elements and mechanisms of impact. By assessing the novel facet of resource diversification, this study has assessed the direct and indirect effects of this feature on environmental quality. This study used the Moment quantile Regression technique to examine data from 31 OECD nations spanning the time frame of 2009-2019. The findings indicate that resource diversification has an adverse effect on environmental quality, however this effect is not homogeneously observed across all countries. Countries with favorable environmental conditions will encounter a more pronounced influence from the diversification of natural resources extraction. This study further demonstrates that expanding the variety of natural resource exploitation will amplify the negative effects of resource exploitation on environmental quality. Furthermore, the degree of environmental technology exerts a beneficial impact on environmental quality across various degrees of environmental quality. Our findings offer several insightful policies for natural resources management in the context of the ongoing industrial revolution.

Unleashing sustainability in uncertain times: Can we leverage economic complexity, uncertainty, and remittances to combat environmental degradation?

Rapid economic growth and human activities have seriously damaged the environment and hindered the achievement of Sustainable Development Goals (SDGs). Hence, this study aims to explore the impact of economic complexity, uncertainty, and remittance on environmental degradation in 134 countries from 2000 to 2022. In addition, it examines whether uncertainty moderates the relationship between remittance and environmental degradation. Two proxies (ecological footprint and CO2) were used to measure environmental degradation. The analysis was conducted using a cross-sectional dependency test, second-generation unit root test, and panel quantile regression. The results revealed that economic complexity significantly and positively impacted environmental degradation, while uncertainty and remittance significantly and negatively impacted environmental degradation. Furthermore, uncertainty weakened the negative relationship between remittance and environmental degradation. Accordingly, this paper discusses various recommendations and policy implications regarding economic complexity, uncertainty, remittance, and environmental degradation.

Continental-scale evaluation of downy birch pollen production: Estimating the impacts of global change.

The high prevalence of hay fever in Europe has raised concerns about the implications of climate change-induced higher temperatures on pollen production. Our study focuses on downy birch pollen production across Europe by analyzing 456 catkins during 2019-2021 in 37 International Phenological Gardens (IPG) spanning a large geographic gradient. As IPGs rely on genetically identical plants, we were able to reduce the effects of genetic variability. We studied the potential association with masting behavior and three model specifications based on mean and quantile regression to assess the impact of meteorology (e.g., temperature and precipitation) and atmospheric gases (e.g., ozone (O3) and carbon-dioxide (CO2)) on pollen and catkin production, while controlling for tree age approximated by stem circumference. The results revealed a substantial geographic variability in mean pollen production, ranging from 1.9 to 2.5 million pollen grains per catkin. Regression analyses indicated that elevated average temperatures of the previous summer corresponded to increased pollen production, while higher O3 levels led to a reduction. Additionally, catkins number was positively influenced by preceding summer's temperature and precipitation but negatively by O3 levels. The investigation of quantile effects revealed that the impacts of mean temperature and O3 levels from the previous summer varied throughout the conditional response distribution. We found that temperature predominantly affected trees characterized by a high pollen production. We therefore suggest that birches modulate their physiological processes to optimize pollen production under varying temperature regimes. In turn, O3 levels negatively affected trees with pollen production levels exceeding the conditional median. We conclude that future temperature increase might exacerbate pollen production while other factors may modify (decrease in the case of O3 and amplify for precipitation) this effect. Our comprehensive study sheds light on potential impacts of climate change on downy birch pollen production, which is crucial for birch reproduction and human health.

Dual-type quantile regression approach for mean estimation incorporating sampled and non-sampled data.

Drawing inspiration from recent advancements in robust mean estimation within finite sampling theory, we introduce a novel dual-type class of mean estimators in a design-based framework. The dual-type class is based on quantile regression and is specifically designed to be effective in the presence of extreme observations. Significantly, it integrates the averages of both sampled observations and non-sampled observations of auxiliary variable. In the initial discussion of this class, it is presumed that the target variable is non-sensitive, signifying its relevance to subjects that respondents do not consider embarrassing when queried directly. In this standard setting, we present specific estimators within the class and determine their theoretical properties. The class's scope broadens to include scenarios where the target variable incorporates sensitive topics, giving rise to nonresponse rates and inaccurate reporting. To alleviate these errors, one can promote respondent cooperation by employing scrambled response methods that obscure the actual value of the sensitive variable. Accordingly, the article delves into discussions on additive methods. Subsequently, a numerical study is conducted using asymmetric data to evaluate the effectiveness of the dual-type class by comparing it with several existing estimators, both in the absence and presence of scrambled responses.

Research on the spatial effect of digital economy development on urban carbon reduction.

Cities are one of the main sources of regional carbon emissions, and reducing urban carbon emission is the key to reducing emissions. The digital economy has transformed the economic operation mode, and it is a significant approach to support the "dual carbon goals" (carbon peaking and carbon neutrality). This article considers the externalities of the digital economy and carbon emissions. And we use spatial econometric models to analyze the effectiveness of digital economy in empowering carbon emissions reduction. Besides, we explore the static and dynamic spillover effects, and use spatial Durbin panel quantile model to analyze the digital economy's heterogeneity on carbon emissions. Research has shown that the digital economy has a remarkable carbon reduction effect, and the conclusion remains valid after considering robustness tests such as replacing the weight matrices, calculation methods, and proxy variables. The analysis of static and dynamic spillover effects indicates that the degree of the digital economy's impact on carbon emissions are significantly different. Heterogeneity analysis shows that as the digital economy develops from a low level to a high level, its impact on carbon emissions also shifts from positive promotion to negative suppression. This paper proposes a policy reference to help the development of digital economy and promote carbon neutrality in the face of severe environmental challenges.

Penalized weighted smoothed quantile regression for high-dimensional longitudinal data.

Quantile regression, known as a robust alternative to linear regression, has been widely used in statistical modeling and inference. In this paper, we propose a penalized weighted convolution-type smoothed method for variable selection and robust parameter estimation of the quantile regression with high dimensional longitudinal data. The proposed method utilizes a twice-differentiable and smoothed loss function instead of the check function in quantile regression without penalty, and can select the important covariates consistently using the efficient gradient-based iterative algorithms when the dimension of covariates is larger than the sample size. Moreover, the proposed method can circumvent the influence of outliers in the response variable and/or the covariates. To incorporate the correlation within each subject and enhance the accuracy of the parameter estimation, a two-step weighted estimation method is also established. Furthermore, we prove the oracle properties of the proposed method under some regularity conditions. Finally, the performance of the proposed method is demonstrated by simulation studies and two real examples.