Quantile Levels Research Articles

Financial technology as a heterogeneous driver of carbon emission reduction in China: Evidence from a novel sparse quantile regression

China is facing tremendous pressure to reduce carbon emissions. This paper investigates the impact of financial technology (fintech) on carbon emissions reduction across Chinese provincial regions using annual data from 2011 to 2021. To comprehensively reflect the development level of fintech, we utilize web crawler technology and word frequency analysis to create new variables for measuring the technology level and innovation capability, with fintech-related keywords sourced from the Baidu index. Subsequently, we construct a fintech index for each province in China. Given the heterogeneous relationship between fintech and carbon emissions reduction, we employ an innovative sparse support vector quantile regression to explore the impact of fintech on carbon emissions reduction at different quantile levels. This method employs an Lp-norm regularization term to identify the key variables influencing carbon emission reduction. The empirical results indicate that fintech significantly reduces carbon emissions in all Chinese provincial regions. Furthermore, the positive effects of fintech on carbon emissions reduction are much stronger in high-carbon provinces than in low-carbon provinces. Consequently, it is imperative for policy-makers and practitioners to focus on increasing investment in digital infrastructure and enhancing digital services, particularly in Shanxi, Hebei, Henan, Shaanxi, Ningxia, and Xinjiang provinces. The mechanism analysis reveals that fintech contributes to carbon emission reduction by promoting industrial structure upgrading in more economically developed provinces, while its mediating role is absent in less developed provinces.

Impacts of carbon market and climate policy uncertainties on financial and economic stability: Evidence from connectedness network analysis

We use the conditional network connectedness approach of Stenfors et al. (2022) along with its frequency-domain, and tail and dynamic extensions to explore complex linkages among the carbon market, climate policy uncertainty, economic stability, and financial market stability. We focus on whether the carbon market and climate policy uncertainty impact US economic and financial market stability, and how these impacts occur. We identify a dynamic impact of the carbon market on economic and financial market stability, with these impacts varying in different frequency-domains and different quantile levels. Energy consumption and production structures as well as carbon emissions are found to be important mediators for carbon market to affect economic stability, while renewable energy equity is found to be a mediator through which the carbon market affects financial market stability. Further, while the impact of climate policy uncertainty on economic and financial market stability is typically weak, it strengthens under some circumstances, particularly in the long-term frequency-domain, right-tailed, prior to 2020. In these cases, carbon market, energy production structure and financial market stability are found to be weak mediators for climate policy uncertainty to impact economic stability.

Morning administration enhances humoral response to SARS-CoV-2 vaccination in kidney transplant recipients

Although severe acute respiratory syndrome coronavirus 2 messenger ribonucleic acid (SARS-CoV-2 mRNA) vaccines are effective in kidney transplant recipients (KTRs), their immune response to vaccination is blunted by immunosuppression. Other tools enhancing vaccination response are therefore needed. Interestingly, aligning vaccine administration with circadian rhythms (chronovaccination) has been shown to boost immune response. However, its applicability in KTRs, whose circadian rhythms are likely disrupted by immunosuppressants, remains unclear. To assess the impact of vaccination timing on seroconversion in the KTRs population, we analyzed data from 553 virus-naïve KTRs who received 2 doses of messenger ribonucleic acid (mRNA) vaccine. Bayesian logistic regression was employed, adjusting for previously identified predictors of seroconversion, including allograft function, maintenance immunosuppressants, or time since transplantation. SARS-CoV-2 immunoglobulin G (IgG) levels were measured with a median of 47 days after the second dose. The results did not reveal a reliable effect of timing of the first dose but did indicate that earlier timing for the second dose brings a notable benefit—every 1-hour delay in the application was associated with a 16% reduction in the odds of seroconversion (OR 0.84, 95% CI 0.71, 0.998). Similar results were obtained from quantile regression modeling IgG levels. In conclusion, morning vaccination is emerging as a promising and easily implementable strategy to enhance vaccine response in KTRs.

A Bayesian quantile joint modeling of multivariate longitudinal and time-to-event data.

Linear mixed models are traditionally used for jointly modeling (multivariate) longitudinal outcomes and event-time(s). However, when the outcomes are non-Gaussian a quantile regression model is more appropriate. In addition, in the presence of some time-varying covariates, it might be of interest to see how the effects of different covariates vary from one quantile level (of outcomes) to the other, and consequently how the event-time changes across different quantiles. For such analyses linear quantile mixed models can be used, and an efficient computational algorithm can be developed. We analyze a dataset from the Acute Lymphocytic Leukemia (ALL) maintenance study conducted by Tata Medical Center, Kolkata. In this study, the patients suffering from ALL were treated with two standard drugs (6MP and MTx) for the first two years, and three biomarkers (e.g. lymphocyte count, neutrophil count and platelet count) were longitudinally measured. After treatment the patients were followed nearly for the next three years, and the relapse-time (if any) for each patient was recorded. For this dataset we develop a Bayesian quantile joint model for the three longitudinal biomarkers and time-to-relapse. We consider an Asymmetric Laplace Distribution (ALD) for each outcome, and exploit the mixture representation of the ALD for developing a Gibbs sampler algorithm to estimate the regression coefficients. Our proposed model allows different quantile levels for different biomarkers, but still simultaneously estimates the regression coefficients corresponding to a particular quantile combination. We infer that a higher lymphocyte count accelerates the chance of a relapse while a higher neutrophil count and a higher platelet count (jointly) reduce it. Also, we infer that across (almost) all quantiles 6MP reduces the lymphocyte count, while MTx increases the neutrophil count. Simulation studies are performed to assess the effectiveness of the proposed approach.

Quantile mortality modelling of multiple populations via neural networks

Quantiles of the mortality rates are relevant in life insurance to control longevity risk properly. Recently, Santolino (2020) adapts the framework of the popular Lee-Carter model to compute the conditional quantiles of the mortality rates. The parameters of the quantile Lee-Carter model are fitted on the mortality data of the population of interest, ignoring the information related to the others. In this paper, we show that more robust parameter estimates can be obtained exploiting the mortality experiences of multiple populations. A neural network is employed to calibrate individual quantile Lee-Carter models jointly using all the available mortality data. In this setting, some common network parameters are used to learn the age and period effects of multiple quantile LC models. Numerical experiments performed on all the countries of the Human Mortality Database validate our approach. The predictions obtained considering the median level appear more accurate than those obtained with the mean models; moreover, those at the tail quantile levels capture the future mortality evolution of the populations well.

Impact of digitization on carbon productivity: an empirical analysis of 136 countries

Enhancing carbon productivity (CP) is key to achieving carbon reduction goals while maintaining economic growth. Digital technology plays a significant role in improving CP. Based on panel data from 136 countries worldwide from 2000 to 2020, this study empirically examines the impact of digitalization on CP and its mechanisms using fixed-effects and mediation models. The conclusions are as follows: (1) Overall, digitalization significantly enhances CP. (2) In terms of the mechanism, digitalization primarily improves CP through technological innovation and mitigating income inequality. (3) In terms of the quantile regression results, as the quantile level of CP increases, the promoting effect of digitalization on CP gradually strengthens. (4) From the perspective of heterogeneity among regions, income levels and human capital levels, digitalization has the greatest promotion effect on carbon productivity in European countries, high-income countries and high human capital countries. This study provides a reference for policymakers worldwide to use digital technology in achieving carbon emission reduction targets.

Quantile Correlation‐Based Sufficient Variable Screening by Controlling False Discovery Rate

AbstractSufficient variable screening (SVS) with the false discovery rate (FDR) controlled rapidly reduces dimensionality with high probability in high dimensional modeling. By using quantiles, this paper proposes a new SVS procedure by controlling the FDR based on two‐stage Pearson's goodness testing with Chi‐square statistics for high dimensional data, abbreviated as QC‐SVS‐FDR. Without any specified distribution of the actual model, the QC‐SVS‐FDR method screens important predictors by a series of testing procedures combined with the adaptive composite of Pearson's chi‐square statistics. The quantile correlation‐based sufficient utility is sensitive to capture the subtle correlations under different quantile levels and is easy to implement with computational efficiency. Asymptotic results and sufficient screening properties of the proposed methods are obtained under mild conditions. Numerical studies including simulation studies and real data analysis demonstrate the advantages of the proposed method in practical settings.

Balancing Acts: Unveiling the Dynamics of Revitalization Policies in China’s Old Revolutionary Areas of Gannan

A series of revitalization policies (RPs) have been implemented in China’s Old Revolutionary Areas (ORAs). Evaluating the impact of these RPs is of paramount importance for refining policy design and achieving the goal of common prosperity. This study focuses on the ORAs in Gannan (ORAG) and employs the Propensity Score Matching Difference-in-Differences (PSM-DID) method to assess the effects of the RPs from two perspectives: stimulating economic growth and increasing farmers’ income, utilizing county-level data spanning from 2006 to 2019. The findings of this study reveal that while the RP restrains the growth of per capita GDP in ORAG, it significantly promotes the growth of farmers’ income. Moreover, it plays a crucial role in reducing the income gap between ORAG and Jiangxi Province, thus promoting the common prosperity of farmers in ORAG. A detailed examination using quantile regression shows that the RP has a significant and consistent negative impact on GDP per capita GDP at different quantile points. At the same time, it has a significant positive effect on increasing farmers’ income at the 25% quantile point, effectively reducing income inequality among farmers at all quantile levels. The mechanism analysis shows that the RP has stimulated increased government investment in ORAG, leading to an increase in farmers’ incomes and a reduction in income disparities. However, the study also highlights the existence of a “policy trap” that has hindered the RP’s effectiveness in ORAG. Drawing upon these findings, this paper offers policy recommendations to enhance the impact of RP in ORAs.

Learnable quantile polynomial chaos expansion: An uncertainty quantification method for interval reliability analysis

Various uncertainties like random input and data noise in aerospace engineering affect flight vehicle safety, making uncertainty quantification important for improving system reliability. Polynomial chaos expansion (PCE) is a versatile method for quantifying stochastic systems’ uncertainty caused by random input, while it cannot quantify data uncertainty caused by noise. To solve this problem, a novel uncertainty quantification method, learnable quantile PCE (LQPCE), is proposed for interval reliability analysis. Unlike the traditional PCE, the multi-dimensional orthonormal basis is constructed for combination input rather than only random variables, and the expansion coefficients are parameterized to learnable weights, ensuring the LQPCE model can capture data noise’s features. The LQPCE Pinball loss function is constructed to learn parameterized expansion coefficients by quantile level iterative sampling, achieving accurate prediction and data uncertainty quantification. The quantified data uncertainty is used for interval reliability analysis, calculating more reliable results about the influence of input uncertainty on stochastic systems’ reliability. Three numerical examples and one engineering application validate the LQPCE method’s effectiveness. The results show that the LQPCE method can quantify data uncertainty accurately and obtain credible interval reliability.

Renewable energy consumption and CO2 emissions nexus in the USA: the role of technical innovation.

Using the QARDL approach and data from January of 2010 to May of 2022, we explore how renewable energy consumption affects CO2 emissions in the USA. Long-term analysis reveals a negative link between these variables, while only lower quantile levels show short-term statistical significance. Integrating technical innovation (measured by patents) in our QARDL model shows substantial reduction in CO2 emissions, with varying effects over time. Interestingly, only renewable energy consumption, not technical innovation, significantly impacts CO2 emissions at lower quantile levels. These findings emphasize the crucial role of renewable energy in reducing both short-term and long-term CO2 emissions and offer policymakers valuable insights for shaping effective energy strategies to combat emissions and promote sustainability in the USA.

Russian Currency's external shocks and domestic policy effectiveness amid geopolitical tensions

This study examines the dynamics of the Russian currency against the US dollar, exploring its responses to geopolitical risk, domestic policies, and oil and gas price shocks. Based on our quantile and time-frequency analyses from January 1998 to July 2022, focused on a subsample that covers the Russia-Ukraine conflict (January–July 2022), our research reveals that the Russian currency has significant vulnerability to geopolitical risk. Domestic policies have a moderate impact on currency valuation, whereas oil prices play a pivotal role, exceeding the impact of domestic policies. Our findings offer strategic insights for hedging, identifying safe-haven assets, and diversification strategies based on Russian-foreign currency dynamics, oil, and gas prices. These considerations are especially relevant amid geopolitical tensions and uncertainty, across various time periods and quantile levels.

Does the resumption of international tourism heighten COVID-19 transmission?

Reopening countries also carries the risk of another wave of infections in many parts of the world, raising the question of whether we are ready to reopen our countries. This study examines the impact of reopening countries to receive foreign tourists on the spread of COVID-19 in 2022, encompassing 83 countries worldwide. We employ spatial quantile models capable of analyzing the spatial impact of tourism on the spread of the virus at different quantile levels. The research categorizes countries into three groups: low infection rate (10th-30th quantiles), moderate infection rate (40th-60th quantiles), and high infection rate (70th-90th quantiles). This allows for a more comprehensive and detailed comparison of the impacts. Additionally, considering the spatial dimension enables the explanation of both the direct and indirect effects of tourists on the country itself and neighboring countries. The findings reveal that the number of international tourists has a significant effect on the COVID-19 infection rate, particularly in countries with high initial infection rates. However, countries that effectively controlled their infection rates at a low level could maintain a low infection rate even after reopening to foreign tourists. It is also observed that reopening a country's borders negatively impacts the infection rate of neighboring countries. These important findings imply that governments of highly infected countries should shift their focus towards bolstering their economy by promoting domestic tourism and should delay reopening until the number of infections decreases.

Quantile Regression for Longitudinal Functional Data with Application to Feed Intake of Lactating Sows

AbstractThis article focuses on the study of lactating sows, where the main interest is the influence of temperature, measured throughout the day, on the lower quantiles of the daily feed intake. We outline a model framework and estimation methodology for quantile regression in scenarios with longitudinal data and functional covariates. The quantile regression model uses a time-varying regression coefficient function to quantify the association between covariates and the quantile level of interest, and it includes subject-specific intercepts to incorporate within-subject dependence. Estimation relies on spline representations of the unknown coefficient functions and can be carried out with existing software. We introduce bootstrap procedures for bias adjustment and computation of standard errors. Analysis of the lactation data indicates, among others, that the influence of temperature increases during the lactation period.Supplementary materials accompanying this paper appear on-line.

Analysing the impact of comorbid conditions and media coverage on online symptom search data: a novel AI-based approach for COVID-19 tracking

Background Web search data have proven to bea valuable early indicator of COVID-19 outbreaks. However, the influence of co-morbid conditions with similar symptoms and the effect of media coverage on symptom-related searches are often overlooked, leading to potential inaccuracies in COVID-19 simulations. Method This study introduces a machine learning-based approach to estimate the magnitude of the impact of media coverage and comorbid conditions with similar symptoms on online symptom searches, based on two scenarios with quantile levels 10–90 and 25–75. An incremental batch learning RNN-LSTM model was then developed for the COVID-19 simulation in Australia and New Zealand, allowing the model to dynamically simulate different infection rates and transmissibility of SARS-CoV-2 variants. Result The COVID-19 infected person-directed symptom searches were found to account for only a small proportion of the total search volume (on average 33.68% in Australia vs. 36.89% in New Zealand) compared to searches influenced by media coverage and comorbid conditions (on average 44.88% in Australia vs. 50.94% in New Zealand). The proposed method, which incorporates estimated symptom component ratios into the RNN-LSTM embedding model, significantly improved COVID-19 simulation performance. Conclusion Media coverage and comorbid conditions with similar symptoms dominate the total number of online symptom searches, suggesting that direct use of online symptom search data in COVID-19 simulations may overestimate COVID-19 infections. Our approach provides new insights into the accurate estimation of COVID-19 infections using online symptom searches, thereby assisting governments in developing complementary methods for public health surveillance.

Energy transition, ecological governance, globalization, and environmental sustainability: Insights from the top ten emitting countries

The global environmental landscape has evolved from regional and localized pollution concerns to a pervasive worldwide issue, posing a formidable challenge for countries worldwide, especially for high-emission countries. Therefore, this study focuses on the role of energy transition, ecological governance, globalization, and economic development in promoting environmental sustainability in the top ten carbon-emitting countries. The findings suggest that energy transition, ecological governance, and globalization collectively contributed to the reduction of carbon emissions in these major emitting nations. Conversely, economic development has had a positive impact on carbon emissions. Moreover, this study employs the method of moments quantile regression to obtain the coefficients in heterogenous quantiles. The results depict that the emission reduction effects of energy transition and globalization is predominantly observed at low to mid-quantile levels, while the decarbonization effect of ecological governance is observed primarily at mid to high-quantile levels. The impact of economic development on carbon emissions exhibits a suppressing effect at the 0.1–0.2 quantile levels, while at the 0.6–0.9 quantile levels, it exacerbates carbon dioxide emissions. Finally, the research findings offer insights into the emission reduction initiatives of the top ten emitting countries, ensuring the sustainable development of the environment.

A scalar-on-quantile-function approach for estimating short-term health effects of environmental exposures.

Environmental epidemiologic studies routinely utilize aggregate health outcomes to estimate effects of short-term (eg, daily) exposures that are available at increasingly fine spatial resolutions. However, areal averages are typically used to derive population-level exposure, which cannot capture the spatial variation and individual heterogeneity in exposures that may occur within the spatial and temporal unit of interest (eg, within a day or ZIP code). We propose a general modeling approach to incorporate within-unit exposure heterogeneity in health analyses via exposure quantile functions. Furthermore, by viewing the exposure quantile function as a functional covariate, our approach provides additional flexibility in characterizing associations at different quantile levels. We apply the proposed approach to an analysis of air pollution and emergency department (ED) visits in Atlanta over 4 years. The analysis utilizes daily ZIP code-level distributions of personal exposures to 4 traffic-related ambient air pollutants simulated from the Stochastic Human Exposure and Dose Simulator. Our analyses find that effects of carbon monoxide on respiratory and cardiovascular disease ED visits are more pronounced with changes in lower quantiles of the population's exposure. Software for implement is provided in the R package nbRegQF.

The impact of energy consumption on carbon emissions intensity in China: evidence from a dynamic panel quantile regression model

Abstract China, being the largest global emitter of carbon, faces significant challenges in mitigating carbon emissions from energy consumption. The empirical findings reveal that the impact of energy consumption intensity and energy consumption structure on carbon emissions intensity is positively correlated with quantile levels and shows heterogeneous effects in different regions. Employing an energy consumption structure as a threshold variable to examine the impact of energy consumption intensity on carbon emissions intensity, we identify a significant single threshold effect in both the full sample and the western sample. Based on empirical research findings, this paper proposes specific policy recommendations.

Fintech, financial inclusion, mineral resources and environmental quality. An economic advancement perspective from China and Vietnam

From a policy perspective, China and Vietnam have made efforts to achieve environmental sustainability. However, it is important to note that environmental issues mostly arise when certain economies heavily rely on fossil fuel to fulfil energy requirements. It is challenging to reduce such reliance because, periodically, these economies become major importers of fossil fuels. Resultantly, the carbon emission rates of China and Vietnam have been increasing over time. The untapped renewable reserves of these economies encourage academia to explore its exploitation in order to produce renewable electricity. The instant growth of fintech, rapid financial inclusion, volatility of mineral resources, and growing economic advancements in China and Vietnam have initiated various dangerous climate issues. Thus, this study investigates the relationship between fintech, financial inclusion, mineral resource rents, economic advancement, and environmental quality in China and Vietnam over the period 2013–2023. Using FEOLS, DOLS, and FMOLS models, it finds that fintech, economic advancement, and mineral resource rents deteriorate the environmental quality of the selected economies, while financial inclusion has a favourable impact on the quality of the climate. The outcomes are supported by the application of the MMQR model. All quantiles show that fintech, economic advancement, and mineral resource rents pollute the environment while financial inclusion enhances climate quality. It is further observed that the extent and magnitude of the relationship increase with the increase of quantile levels. The findings have interesting and useful policy implications.

A deep learning-based Monte Carlo simulation scheme for stochastic differential equations driven by fractional Brownian motion

Stochastic differential equations (SDEs) are widely used models to describe the evolution of stochastic processes. Among them, SDEs driven by fractional Brownian motion (fBm) have been shown to be capable of describing systems with temporal dependencies. In this paper, we develop a neural network based Monte Carlo methodology in which we can efficiently simulate SDEs that are governed by fBm. Particularly, we focus on large time step simulations. A property of fBm that complicates the development of such Monte Carlo schemes is the long-range temporal correlation. To this end, we build the network based on the encoder–decoder framework and employ the attention mechanism to learn the temporal relationships in the historical paths of such SDEs. In addition, a loss function based on the quantile loss is used, where the quantile levels to be predicted are determined by means of the stochastic collocation method. Experimental results show that this kind of loss function is superior to conventional loss functions in terms of solution accuracy, and the resulting scheme can learn and simulate SDEs driven by fBm accurately and highly efficiently.

Predicting tail risks and the evolution of temperatures

This paper explores a range of simple models to study the relationship between global temperature anomalies and climate forcings. In particular, we consider quantile regression models with potentially time-varying parameters (TVP), implemented by Bayesian methods. In its most general specification, this approach is flexible in that it models distinct regions of distribution of global temperature anomalies, while also allowing us to investigate changes in the relationship between (natural and anthropogenic) climate forcings and temperatures. Our results indicate that there is indeed considerable variation over time in the relationship between temperatures and its drivers, and that these effects may be heterogeneous across different quantiles. We then perform a long-range forecasting exercise for temperatures, which suggests that incorporating TVP or explicitly modelling quantile levels or the combination of both features can improve prediction for different parts of the temperature distribution. In addition, we produce forecasts for 2030 considering the intermediate RCP 4.5 scenario: given that no single specification dominates, we account for model uncertainty by considering forecast averaging across all specifications. Our approach allows us to make statements about the probability of temperature levels — for instance, we find that a scenario of +1.8 °C will occur with a non-negligible probability under RCP 4.5.