Quantile Levels Research Articles

The Bayesian regularized quantile varying coefficient model

The quantile varying coefficient (VC) model can flexibly capture dynamical patterns of regression coefficients. In addition, due to the quantile check loss function, it is robust against outliers and heavy-tailed distributions of the response variable, and can provide a more comprehensive picture of modeling via exploring the conditional quantiles of the response variable. Although extensive studies have been conducted to examine variable selection for the high-dimensional quantile varying coefficient models, the Bayesian analysis has been rarely developed. The Bayesian regularized quantile varying coefficient model has been proposed to incorporate robustness against data heterogeneity while accommodating the non-linear interactions between the effect modifier and predictors. Selecting important varying coefficients can be achieved through Bayesian variable selection. Incorporating the multivariate spike-and-slab priors further improves performance by inducing exact sparsity. The Gibbs sampler has been derived to conduct efficient posterior inference of the sparse Bayesian quantile VC model through Markov chain Monte Carlo (MCMC). The merit of the proposed model in selection and estimation accuracy over the alternatives has been systematically investigated in simulation under specific quantile levels and multiple heavy-tailed model errors. In the case study, the proposed model leads to identification of biologically sensible markers in a non-linear gene-environment interaction study using the NHS data.

Censored Interquantile Regression Model with Time-Dependent Covariates

Conventionally, censored quantile regression stipulates a specific, pointwise conditional quantile of the survival time given covariates. Despite its model flexibility and straightforward interpretation, the pointwise formulation oftentimes yields rather unstable estimates across neighboring quantile levels with large variances. In view of this phenomenon, we propose a new class of quantile-based regression models with time-dependent covariates for censored data. The models proposed aim to capture the relationship between the failure time and the covariate processes of a target population that falls within a specific quantile bracket. The pooling of information within a homogeneous neighborhood facilitates more efficient estimates hence, more consistent conclusion on statistical significances of the variables concerned. This new formulation can also be regarded as a generalization of the accelerated failure time model for survival data in the sense that it relaxes the assumption of global homogeneity for the error at all quantile levels. By introducing a class of weighted rank-based estimation procedure, our framework allows a quantile-based inference on the covariate effect with a less restrictive set of assumptions. Numerical studies demonstrate that the proposed estimator outperforms existing alternatives under various settings in terms of smaller empirical biases and standard deviations. A perturbation-based resampling method is also developed to reconcile the asymptotic distribution of the parameter estimates. Finally, consistency and weak convergence of the proposed estimator are established via empirical process theory. Supplementary materials for this article are available online.

Does globalization escalate the carbon emissions? Empirical evidence from selected next-11 countries

Adverse consequences are observed in developing countries due to the impact of the globalization process. Therefore, our study aims to empirically verify whether globalization escalates carbon dioxide emissions in selected N-11 (next-11) countries between 1990 and 2019. The study also analyzes how per capita GDP, per capita GDP2, population growth, and renewable energy consumption affect carbon emissions. For this reason, the researchers used several econometric methods, including the slope homogeneity test, the cross-sectional dependency test, the panel unit root test, the panel cointegration test, the method of moment’s panel quantile regression analysis, and the Wald test. The estimated results of panel quantile regression show how carbon emissions change across a range of quantiles (0.1 to 0.9). The findings show that per capita GDP significantly impacts the overabundance of carbon emissions in N-11 countries. Over time, the study found that the positive coefficient value of per capita GDP decreased from the first to the last (7.41 to 5.87), leading to the validation of the EKC hypothesis. The adverse correlation between per capita GDP2 and environmental contamination confirms that the Environmental Kuznets Curve hypothesis is valid for selected N-11 countries. Globalization deteriorates the environment by directly affecting CO2 emissions. It increases monotonically from the lower quantile to the upper quantile (0.972 to 1.002). At the quantile level of 0.1 to 0.9, population growth and renewable energy consumption increase impede carbon dioxide emissions in these selected countries. Coefficient values in the quantile 0.1 to 0.9 (-0.35 to -0.53) suggest that governments can reduce carbon emissions more due to renewable energy consumption over time. But the negative coefficient values of the population (-0.97, -0.93, -0.90, -0.88, -0.86, -0.85, -0.83, -0.81, and -0.77) decrease from the lower quantile to the upper quantile. The Wald test supports the asymmetric effects of different quantiles. As a robustness check of estimators, the study used FMOLS, DOLS, and CCR, which show the variables’ long-run elasticity. The research developed targeted policy recommendations for sustainably mitigating carbon emissions based on the above results.

Disaggregating the impact of natural resource rents on environmental sustainability in the MENA region: A quantile regression analysis

The main contribution of this paper is to analyze the disaggregated impact of natural resource rents on environmental sustainability in the MENA region. While previous literature has explored the overall effect of rents, this study focuses on the specific impacts of rents from forests, minerals, and oil. To do so, a novel quantile method based on moments is employed to analyze long-run heterogeneous impacts at various quantile levels of environmental conditions. The results from the quantile regression reveal that forest rent improves environmental quality, while rents from oil and minerals deteriorate environmental conditions. The study also found that economic growth and fossil fuel use led to an upsurge in carbon dioxide emissions, whereas renewable energy sources showed the potential in mitigating environmental issues. The study provides valuable insights for policymakers to achieve sustainable development goals in the MENA region.

Internationalisation, employment generation and wage premia: evidence from Indian manufacturing firms

AbstractThe study investigates the impact of internationalisation on employment and wages in the Indian manufacturing sector by using an extensive plant‐level dataset. Using system generalised method of moments (GMM) and quantile regression estimators, we find strong evidence of the dampening effect of exports and imports on wages, whereas a positive impact of foreign investment on wages. However, the magnitude of the impact of internationalisation on wages reduces marginally at higher quantile levels. The impact of imports and exports on employment generation is slightly ambiguous though overall indicates a beneficial impact.

The heterogeneous effect of financial technology on green total factor productivity in China

Due to the unbalanced and inadequate development of fintech among various provincial regions, this paper proposes a novel sparse quantile model to explore the heterogeneous impacts of fintech on green total factor productivity (GTFP) in the provinces of China based on the annual data from 2011 to 2020. Quantile estimators of the proposed method are used as empirical “location” measures for the heterogeneous influence of fintech on GTFP. Two nonparallel twin functions at each quantile level capture the unbalanced information between fintech and GTFP. The empirical results show that fintech effectively promotes the quality of economic growth in every province, while the effects of fintech on GTFP are heterogeneous and unbalanced. Specifically, the effects in high-green-development regions are more powerful than those in low-green-development regions. Moreover, the influencing strength of fintech on GTFP among provincial regions has become weaker in recent years. The impact mechanism test indicates that fintech mainly promotes GTFP through the upgrading of industrial structure. Based on these results, this paper discusses some policy recommendations, such as optimizing the allocation of financial technology resources, improving the quality of innovation, promoting the upgrading of industrial structure, and strengthening foreign exchange and cooperation for the sustainable development.

Powerlifting Balance Of SBD Disciplines (Squat, Bench Press And Deadlift) Ratio To Total Score

This research studied the powerlifting balance of squat, bench press and deadlift (SBD) disciplines ratio to the total score. The data set was from Powerlifting Championships recognized by the International Powerlifting Federation, compiled by the Open Powerlifting project. The records were 65,867 men’s and 35,679 women’s samples from classic powerlifting and 19,295 men’s and 7,426 women’s samples from equipped powerlifting, all with ages from 24 to 39 years from 2012 to November 2022. For each record, the SBD ratios were calculated by the mean of the formula (SBD discipline / Total score) by one hundred. Dispersion plots of Good Lift Points (GLP) score versus the SBD ratios and Bar plots of mean and variance of SBD ratios were done, highlighted by quantiles of GLP score. The Mean and Standard Deviation from Elite Powerlifters (90-100th quantiles) of each weight class were used to calculate the SBD ratio ranges. An algorithm with twenty-six permutations, where the addition and subtraction from 0.5 to 3 standard deviation to the mean marked the ranges' Upper and Lower borders. The best permutation by each weight class was selected when the athletes' group inside the optimal ratio ranges (ORR) showed the highest Impact Factor (GLP mean by frequency) and the shortest ORR. To evaluate the effectiveness of the ORR for each weight class in sexes and events were analyzed lower levels of Powerlifters, inside 0-50th and 50th-90th quantiles of GLP score. For these sets, was applied One Way ANOVA to evaluate the meaning differences between IN and OUT groups from ORR, using post-hoc test of Tukey's HSD or Games-Howell. From sixty-four evaluations, forty-eight were higher in GLP mean score for the IN group, meaning differences regarding the OUT group. At the same time, fifteen were only the highest but not statistically different, and only one was rejected for both. Regardless of the quantile level, for Classic Powerlifting, four Women’s (57, 63, 69 and +84) and five Men’s (66, 74, 93, 105 and +120) classes presented ORR supported; while for Equipped Powerlifting were four Men’s (74, 83, 93 and 105) and six Women’s (57, 63, 69, 76, 84 and +84) classes. These findings support the theory of Powerlifting balance in SBD disciplines, which influences performance.

Probabilistic carbon price prediction with quantile temporal convolutional network considering uncertain factors

Accurate carbon price projections can serve as valuable investment guides and risk warnings for carbon trading participants. However, the escalation of uncertain factors has brought numerous new hurdles to existing carbon price forecast methods. In this paper, we develop a novel probabilistic forecast model called quantile temporal convolutional network (QTCN) that can precisely describe the uncertain fluctuation of carbon prices. We also investigate the impact of external factors on carbon market prices, including energy prices, economic status, international carbon markets, environmental conditions, public concerns, and especially uncertain factors. Taking China's Hubei carbon emissions exchange as a study case, we verify that our QTCN outperforms other classical benchmark models in terms of prediction errors and actual trading returns. Our findings suggest that coal prices and EU carbon prices have the most significant effect on Hubei carbon price forecasting, while air quality index appears to be the least important. Besides, we demonstrate the great contribution of geopolitical risk and economic policy uncertainty to carbon price projections. The effect of these uncertainties is more pronounced when the carbon price is at a high quantile level. This research can offer valuable guidelines for carbon market risk management and provide new insight into carbon price formation mechanisms in the era of global conflict.

Analyzing the static and dynamic dependence among green investments, carbon markets, financial markets and commodity markets

PurposeThis paper investigates the static and dynamic directional return spillovers and dependence among green investments, carbon markets, financial markets and commodity markets from January 2013 to September 2020.Design/methodology/approachThis study employed both the quantile vector autoregression (QVAR) and time-varying parameter VAR (TVP-VAR) technique to examine the magnitude of static and dynamic directional spillovers and dependence of markets.FindingsResults show that the magnitude of connectedness is extremely higher at quantile levels (q = 0.05 and q = 0.95) compared to those in the mean of the conditional distribution. This connotes that connectedness between green bonds and other assets increases with shock size for both negative and positive shocks. This further indicates that return shocks spread at a higher magnitude during extreme market conditions relative to normal periods. Additional analyses show the behavior of return transmission between green bond and other assets is asymmetric.Practical implicationsThe findings of this study offer significant implications for portfolio investors, policymakers, regulatory authorities and investment community in terms of carefully assessing the unique characteristics offered by each markets in terms of return spillovers and dependence and diversifying the portfolios.Originality/valueThe study, first, uses a relatively new statistical technique, the QVAR advanced by Ando et al. (2018), to capture upper and lower tails’ quantile price connectedness and directional spillover. Therefore, the results possess adequate power against departure from mean-based conditional connectedness. Second, using a portfolio of green investments, carbon markets, financial markets and commodity markets, the uniqueness of this study lies in the examination of the static and dynamic dependence of the markets examined.

Smoothed quantile regression for partially functional linear models in high dimensions.

Practitioners of current data analysis are regularly confronted with the situation where the heavy-tailed skewed response is related to both multiple functional predictors and high-dimensional scalar covariates. We propose a new class of partially functional penalized convolution-type smoothed quantile regression to characterize the conditional quantile level between a scalar response and predictors of both functional and scalar types. The new approach overcomes the lack of smoothness and severe convexity of the standard quantile empirical loss, considerably improving the computing efficiency of partially functional quantile regression. We investigate a folded concave penalized estimator for simultaneous variable selection and estimation by the modified local adaptive majorize-minimization (LAMM) algorithm. The functional predictors can be dense or sparse and are approximated by the principal component basis. Under mild conditions, the consistency and oracle properties of the resulting estimators are established. Simulation studies demonstrate a competitive performance against the partially functional standard penalized quantile regression. A real application using Alzheimer's Disease Neuroimaging Initiative data is utilized to illustrate the practicality of the proposedmodel.

NUCLEAR NORM REGULARIZED QUANTILE REGRESSION WITH INTERACTIVE FIXED EFFECTS

This paper studies large N and large T conditional quantile panel data models with interactive fixed effects. We propose a nuclear norm penalized estimator of the coefficients on the covariates and the low-rank matrix formed by the interactive fixed effects. The estimator solves a convex minimization problem, not requiring pre-estimation of the (number of) interactive fixed effects. It also allows the number of covariates to grow slowly with N and T. We derive an error bound on the estimator that holds uniformly in the quantile level. The order of the bound implies uniform consistency of the estimator and is nearly optimal for the low-rank component. Given the error bound, we also propose a consistent estimator of the number of interactive fixed effects at any quantile level. We demonstrate the performance of the estimator via Monte Carlo simulations.

The role of the COVID-19 pandemic in time-frequency connectedness between oil market shocks and green bond markets: Evidence from the wavelet-based quantile approaches

This study contributes to the existing literature on the relationship between oil market shocks and the green bond market by investigating the impact of the COVID-19 pandemic on their dynamic correlation. We first decompose the oil market shocks into components using a time-frequency framework. Then, we combine wavelet decomposition and quantile coherence and causality methods to discuss changes during the COVID-19 era. We observe positive effects of both supply-driven and demand-driven oil shocks on the green bond market at most quantile levels. However, supply-driven oil price changes play a major role. The results also indicate that long-term changes have a greater impact than short-term changes on the connection between oil and green bond markets. Nevertheless, the COVID-19 pandemic changed the nature of the causal relationship, as we observed no relationship under extreme market conditions during the pandemic era. We argue that the economic and social impacts of the COVID-19 pandemic have left investors focusing on the short-term substitution between oil and green bond markets.

Effect of WeChat-based intervention on food safety knowledge, attitudes and practices among university students in Chongqing, China: a quasi-experimental study

BackgroundFood safety is of global importance and has been of concern in university settings in recent years. However, effective methods to conduct food safety education are limited. This study aims to evaluate the effects of an intervention on food safety knowledge, attitudes and practices (KAP) by social media, WeChat, among university students.MethodsA quasi-experimental study was conducted in Chongqing, China. Two departments were recruited randomly from a normal university and a medical university. One department from each university was randomly selected as the intervention group and the other as the control group. All freshmen students in each selected department were chosen to participate in this study. One thousand and twenty-three students were included at baseline, and 444 students completed the study. This intervention was conducted through food safety-related popular science articles with an average of three articles per week released by WeChat official accounts called "Yingyangren" for two months to the intervention group. No intervention was conducted in the control group. An independent t-test was used to test statistical differences in the food safety KAP scores between the two groups. A paired t-test was used to test statistical differences in the food safety KAP scores between before and after the intervention. And quantile regression analysis was conducted to explore the difference between the two groups across the quantile levels of KAP change.ResultsAfter the intervention, compared with control group, participants in the intervention group did not score significant higher on knowledge (p = 0.98), attitude (p = 0.13), and practice (p = 0.21). And the scores of food safety knowledge and practices slightly improved after the intervention both in the intervention group (p = 0.01 and p = 0.01, respectively) and in the control group (p = 0.0003 and p = 0.0001, respectively). Additionally, the quantile regression analysis showed that the intervention had no effect on improving the food safety KAP scores.ConclusionsThe intervention using the WeChat official account had limited effects on improving the food safety KAP among the university students. This study was an exploration of food safety intervention using the WeChat official account; valuable experience can be provided for social media intervention in future study.Trial registrationChiCTR-OCH-14004861.

Quantifying uncertainty in coastal salinity regime for biological application using quantile regression

AbstractSalinity regimes in coastal ecosystems are highly dynamic and driven by complex geomorphic and hydrological processes. Estuarine biota are generally adapted to salinity fluctuation, but are vulnerable to salinity extremes. Characterizing coastal salinity regime for ecological studies therefore requires representing extremes of salinity ranges at time scales relevant to ecology (e.g., daily, monthly, and seasonally). Here, we propose a framework for modeling coastal salinity with these overall goals: (1) quantify uncertainty in salinity associated with important terrestrial and oceanographic drivers, (2) examine time scales of salinity response to river streamflow events, and (3) predict salinity continuously over space at key time scales. Salinity is modeled as quantile surfaces related to river discharge, tidal dynamics, wind, and spatial location, applied to Suwannee Sound estuary, FL, USA, where salinity has been monitored spatially since 1981. Each quantile level is regressed independently, and together they comprise a distribution of salinity uncertainty across space, with upper and lower quantiles describing salinity extremes. Effects of physical drivers on salinity are compared through four base models with various combinations of tide and wind variables, each including spatial coordinates and a single streamflow metric (in cubic meters per second). Multiple time scales of streamflow are considered by taking means across various periods, from 1 to 12 days, and at various lagged intervals prior to salinity sample, totaling 144 streamflow metrics. We found that the Suwannee coastal salinity regime is dynamic at multiple time scales and varies nonlinearly across space from the river effluence outward. Salinity increases nonlinearly with decreasing river flow rates below 200 m3/s, most prominently in the lower quantiles of salinity (τ = 0.05–0.25). Wind appears to have a stronger influence on salinity than astronomic tides for this estuary. The regression approach developed here can be applied to any coastal system that has sufficient spatial and temporal monitoring coverage to capture multiple flood and drought events. It is implemented with a simpleRroutine, and is less computationally‐intensive than finite difference hydrodynamic modeling. The characterizations of salinity uncertainty developed in these analyses can be directly applied to future studies of fish and wildlife responses to changes in watershed management.

Supply option purchasing decisions via mismatch cost minimization

In this paper, the optimal purchasing decisions of the option procurement of newsvendor-type commodities are studied. Based on the mismatch cost minimization criterion, the present study analyzes the option purchasing decisions of a risk-averse newsvendor under the measure of Conditional Value-at-Risk (CVaR). For the purpose of controlling the procurement risk, it is found that a newsvendor’s optimal option purchasing decision under the CVaR measure increases at the quantile level under certain conditions. The result provides alternative explanations for a newsvendor’s over-purchasing behaviors in reality. To mitigate the conservativeness of the CVaR measure, a combined objective defined as the mean-CVaR on mismatch cost is introduced, and the optimal option purchasing decision of a newsvendor minimizing the combined objective is derived. The combined objective can strike a balance between the minimization of CVaR of mismatch cost as well as the minimization of expected mismatch cost. In addition, a sensitivity analysis is performed to exhibit the results and managerial insights are presented to hedge against the potential risks for newsvendors in the option purchasing decision-making.

Empirical research on household consumption carbon emissions and key impact factors in urban and rural China.

The analysis of household consumption carbon emissions (HCCEs), a significant source of CO2 emissions, is essential to achieving China's carbon peak before 2030 and carbon neutrality before 2060. Based on the calculation of urban and rural HCCEs during 2005-2019, the differences between urban and rural areas, spatial-temporal pattern and agglomeration characteristics of HCCEs were analyzed, and the panel quantile STIRPAT model was constructed to empirically test the influence of socioeconomic factors on urban and rural HCCEs at different quantile levels. The results indicate that, first, China's HCCEs are generally growing, indirect HCCEs are more than direct HCCEs, urban HCCEs are far more than rural, and the gap has a growing trend. Second, the urban and rural HCCEs have significant disequilibrium and agglomeration characteristics in space, and high-high and low-low agglomerations dominated the local region. Third, household size and the number of patent application authorizations increase the urban and rural HCCEs, while the consumption capacity and consumption structure inhibit the urban and rural HCCEs. In addition, the level of education also has an inhibitory effect on the rural HCCEs, while the aging degree of the population has a significant positive impact on the rural HCCEs when it is only at the 90th percentile. Finally, it is suggested to formulate differentiated emission reduction policies.

Energy-saving effect of financial development and its dynamic heterogeneity: Empirical evidence from the dynamic panel quantile model

The energy-saving effect of financial development is directly related to the formulation and implementation of financial policies. Considering the inertial characteristics of energy consumption, this study tested the energy-saving effect of financial development and examined its heterogeneity in terms of low-carbon cleaning and policy change. The results were as follows: First, when energy consumption was at the lower quantile, as consumption increased, the promoting impact of financial development on energy consumption decreased. When energy consumption was at the upper quantile, as consumption increased, the restraining impact of financial development on energy consumption increased. Second, an increase in the quantile level showed that financial development exerted an increasingly stronger influence on promoting clean energy consumption. When non-clean energy consumption was at the upper quantile, financial development exerted an increasingly strong inhibitory effect on non-clean energy consumption. Third, before green credit policy changed, the energy-saving effect of financial development was not widespread and obvious. After green credit policy changed, the restraining impact of financial development on energy consumption increased with the level of consumption. Fourth, after green credit policy changed, compared with the increase of financial development toward promoting clean energy consumption, the inhibitory effect of financial development on non-clean energy consumption significantly improved relative to the second case.

Quantile-Adaptive Sufficient Variable Screening by Controlling False Discovery

Sufficient variable screening rapidly reduces dimensionality with high probability in ultra-high dimensional modeling. To rapidly screen out the null predictors, a quantile-adaptive sufficient variable screening framework is developed by controlling the false discovery. Without any specification of an actual model, we first introduce a compound testing procedure based on the conditionally imputing marginal rank correlation at different quantile levels of response to select active predictors in high dimensionality. The testing statistic can capture sufficient dependence through two paths: one is to control false discovery adaptively and the other is to control the false discovery rate by giving a prespecified threshold. It is computationally efficient and easy to implement. We establish the theoretical properties under mild conditions. Numerical studies including simulation studies and real data analysis contain supporting evidence that the proposal performs reasonably well in practical settings.

Modeling uncertainty in financial tail risk: A forecast combination and weighted quantile approach

AbstractA novel forecast combination and weighted quantile‐based tail risk forecasting framework is proposed, aiming to reduce the impact of modeling uncertainty. The proposed approach is based on a two‐step estimation procedure. The first step involves the combination of value‐at‐risk (VaR) forecasts at a grid of quantile levels. A range of parametric and semiparametric models is selected as the model universe in the forecast combination procedure. The quantile forecast combination weights are estimated by optimizing the quantile loss. In the second step, the expected shortfall (ES) is computed as a weighted average of combined quantiles. The quantiles weighting structure for ES forecasting is determined by minimizing a strictly consistent joint VaR and ES loss function of the Fissler–Ziegel class. The proposed framework is applied to six stock market indices and its forecasting performance is compared to each individual model in the universe, a simple average approach and a weighted quantile approach. The forecasting results support the proposed framework.

Environmental Effect Evaluation: A Quantile-Type Path-Modeling Approach

The environment is a key element that affects many aspects of our society, including the economy, education and talents. In this article, the main purpose is to provide statistical models, algorithms and quantitative evidence regarding environmental effect evaluation (EEE). To accomplish this investigation, I first establish a theoretical EEE model and then apply a quantile-type path-modeling algorithm in the developed EEE model at different quantile levels. In the real-data analysis, this article investigates hypotheses regarding this theoretical EEE model and illustrates the statistical performances of quantile-type path-modeling EEE estimators through bootstraps. The results mainly illustrate that the environment has indispensable impacts on the economy, education and science and technology talent directly and has indirect effects on scientific infrastructure and science and technology output. Compared with the existing classical path-modeling algorithm, quantile-type path-modeling EEE estimators make full use of quantile regression and then overcome the classical exploration of only average effects. Both the quantile-type EEE model and quantile-type path-modeling algorithm capture changes in the relations among constructs and between the constructs and observed variables, and this helps to analyze the entire distribution of the outcome variables in this EEE model.