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In traditional quantile credibility models, it is typically assumed that claims are independent across different risks. Nevertheless, there are numerous scenarios where dependencies among insured individuals can emerge, thereby breaching the independence assumption. This study focuses on examining the quantile credibility model and extending some established results within the context of an equal correlation structure among risks. Specifically, we compute the credibility premiums for both homogeneous and inhomogeneous cases utilizing the orthogonal projection method. [ABSTRACT FROM AUTHOR]

This research is carried out to the GARCH-MIDAS model which is used with the aim of compensating for the shortcoming of conventional GARCH models; i.e., relying on symmetry in data frequency. Therefore, the advantage of GARCH-MIDAS model to GARCH models and of course other time series models is the combination of data that have different frequencies. For this purpose, stock returns are modeled based on a combination of daily and weekly volatility. Besides, the Quantile model is also one of the new models that focuses on the entire distribution instead of different frequencies, thereby does regression based on the distribution of the entire data and is not based on the characteristic of the normal distribution. The problem of the current research was formed from this difference between Garch-Midas and Quantile model, and the organization of the research was formed based on it. After describing the problem and assumptions in the first chapter, a review of the theoretical and empirical literature of the research was carried out, and in the third and fourth chapters, the research model, its description and regression were estimated. The findings of the research showed that the Garch-Midas model has a better fit than the quantile model and has a better modeling and forecast capability for the fluctuation in stock returns.

As a major source of greenhouse gases, energy generation poses a threat to modern civilization; however, it also holds some of the remedies to the climate change problem. Hydroelectricity generation and renewable electricity generation produce relatively less carbon dioxide and ensure sustainability. China is the largest producer and consumer of hydropower in the world. In this context, this study revisits the nexus of hydropower generation, trade, urbanization, economic growth and carbon dioxide emissions in China using the novel QARDL approach. The use of QARDL is warranted to explain the heterogeneous effects of factors affecting environmental degradation at distinct quantile levels. Findings revealed that EKC is valid, urbanization worsens while trade and hydropower generation mitigates environmental degradation. The study calls for effective policies to enhance investment in hydropower generation. [ABSTRACT FROM AUTHOR]

In 2016, China first proposed the concept of "digital inclusive finance", which effectively solved the problem of difficult and expensive loans for small investors, activated economic growth from all sides, and has become the trend of financial industry development. This article is based on data from 72 counties in Guizhou Province from 2014 to 2021, using the PSTR model and quantile model for empirical analysis, exploring the relationship between economic growth and digital inclusive finance in Guizhou Province. The research results show that the development of digital inclusive finance can promote economic growth in Guizhou Province. After crossing a certain threshold value, it will further promote the economy; the breadth and digitization of the second-level indicators of digital inclusive finance have a nonlinear impact on the economy of Guizhou Province. The depth of digital inclusive finance has a linear relationship with the economy of Guizhou Province; The results of the quantile test show that the effect of digital inclusive finance promoting the economy is more significant in underdeveloped areas. [ABSTRACT FROM AUTHOR]

This study discusses the basic purpose of external debt: to finance poverty alleviation and bring progress in a country in a time of crisis rather than increase the burden on the economy. Several empirical studies explored the concept of optimal debt, but they often assume a particular nonlinear specification. This study examines the impact of long run and short run external debt on the living standard for selected 23 high indebted Asian countries for the time period 1980–2020. The quadratic effects of external debt are assessed using the Two-Step Panel Quantile ARDL model. This model controls the unobserved heterogeneity, serial correlation, miss-specification and non-normality to ensure robust debt effect on living standards. This study reveals diminishing returns in external debt, whereby debt will be harmful to the standard of living beyond the estimated threshold. The estimated model also proposes that small and effective debt-based policy interventions are beneficial. [ABSTRACT FROM AUTHOR]

The quantile additive functional regression (QAFR) relates the response to the integral of F (t , X (t)) over t , where F is an unknown function and X (t) is the predictor in the form of a curve (function). This model incorporates functional linear quantile regression as a special case and the appearance of the integration as a smoothing operator makes regularization necessary to obtain a meaningful estimate as in typical inverse problems. It is conventional to perform ridge regression over the reproducing kernel Hilbert space. However, the computational complexity grows very fast when the problem scale gets large. We are thus motivated to consider the random sketching method as an approximation for this class of models. Sketching has become very popular recently and was previously used in some learning problems including kernel ridge regression. We established the optimal theoretical risk bound showing that the sketched estimator can converge as fast as the standard estimator. Numerical studies are carried out to examine the performance of sketched estimation. [ABSTRACT FROM AUTHOR]

Environmental DNA (eDNA) analysis can detect aquatic organisms, including rare and endangered species, in a variety of habitats. Degradation can influence eDNA persistence, impacting eDNA-based species distribution and occurrence results. Previous studies have investigated degradation rates and associated contributing factors. It is important to integrate data from across these studies to better understand and synthesize eDNA degradation in various environments. We complied the eDNA degradation rates and related factors, especially water temperature and amplicon lengths of the measured DNA from 28 studies, and subjected the data to a meta-analysis. In agreement with previous studies, our results suggest that water temperature and amplicon length are significantly related to the eDNA degradation rate. From the 95% quantile model simulation, we predicted the maximum eDNA degradation rate in various combinations of water temperature and amplicon length. Predicting eDNA degradation could be important for evaluating species distribution and inducing innovation (e.g., sampling, extraction, and analysis) of eDNA methods, especially for rare and endangered species with small population size.

Earlier studies to assess the effects of risk factors on child nutritional status in Bangladesh have used conventional regression models that are inadequate to capture a complete scenario of effects. Therefore, this study aimed to evaluate the heterogeneous effects of factors at different points of conditional height-for-age Z-score (HAZ) distribution accounting for cluster-level variation using linear quantile mixed model (LQMM) and to compare them with a linear mixed model (LMM). In addition, an unconditional quantile model (UQM) was used to measure the effect of factors on the unconditional (marginal) HAZ distribution. A total of 6340 children aged 0–59 months extracted from the 2014 Bangladesh Demographic and Health Survey. Different factors – maternal characteristics (age, occupation, nutritional status, parity, birth interval), parental education, child age, breastfeeding status, and morbidity had significant heterogeneous effects on HAZ distribution. For example, secondary or higher educated parents had substantial differential impacts on the lower tail and upper tail of the child HAZ distribution, which was masked by LMM estimate. Moreover, significant cluster-level variations found across all quantiles of child HAZ. During intervention design, heterogeneous effects of factors and cluster variation ought to consider addressing the undernutrition problem in Bangladesh. [ABSTRACT FROM AUTHOR]

In the new era of Chinese economy stepping toward high‐quality development, with the enhancement of the public environmental protection awareness and continuous improvement of technological innovation level, it is of great significance to study how technological innovation reduces water contamination against unofficial environmental regulations, which is also the primary task of construction of ecological civilization. The purpose of this paper is to study the contribution of technological innovation to water pollution intensity at different quantile levels and to test the nonlinear relationship between technological innovation and water pollution emission intensity under informal environmental pollution. On the basis of the provincial panel data of China from 2002 to 2018, the model of quantile and panel threshold was constructed to empirically analyze the structural mutation and threshold effects of technological innovation on the intensity of water pollution in China against the environment of informal environmental regulation. The study found that the marginal effects of technological innovation on the intensity of water pollution in China had undergone structural changes. Under different water pollution intensity, technological innovation can reduce pollution between 0.167% and 0.415%; with informal environmental regulation as the threshold variable, when the intensity of informal environmental regulation is less than the threshold value of 0.2168, technological innovation has a significant inhibitory effect on China's water pollution intensity, and when the intensity of informal environmental regulation exceeds the threshold value, the mitigation effect of technological innovation on China's water pollution intensity is more significant. The inhibition effect of technological innovation on water pollution emission intensity changed from 0.437 to 0.474. Therefore, when formulating environmental pollution policies, local governments should make rational use of informal environmental regulations and technological innovation, improve the informal environmental regulation system, and promote water pollution control with science and technology. [ABSTRACT FROM AUTHOR]

The paper considers a linear model with grouped explanatory variables. If the model errors are not with zero mean and bounded variance or if model contains outliers, then the least squares framework is not appropriate. Thus, the quantile regression is an interesting alternative. In order to automatically select the relevant variable groups, we propose and study here the adaptive group LASSO quantile estimator. We establish the sparsity and asymptotic normality of the proposed estimator in two cases: fixed number and divergent number of variable groups. Numerical study by Monte Carlo simulations confirms the theoretical results and illustrates the performance of the proposed estimator. [ABSTRACT FROM AUTHOR]

Different from classical Bühlmann and Bühlmann Straub credibility models in which independence between different risks are assumed, this paper takes dependence between risks into consideration and extends the classical Bühlmann model by introducing a common stochastic shock element. What is more, instead of relying on complete information of historical data, we aim to derive the premium using quantile of the available data. By the method of linear regression, we manage to obtain the quantile credibility premium with common effects. Our result is the generalization of existing results in credibility theory. Both quantile credibility model proposed by Pitselis (2013) and credibility premium for models with dependence induced by common effects obtained by Wen et al. (2009) are special cases of our model. Numerical simulations are also presented to illustrate the impact of quantile credibility with common effect.

This research measures energy-based carbon intensity based on energy consumption of 30 Chinese provinces and investigates the impact of digital finance. First, the baseline results were examined through panel data model. Second, we reveal the "black box" system of digital finance and energy-based carbon intensity through mediating effects model. Third, by employed panel quantile regression model, we examine the heterogeneity of the effects of various factors under different carbon intensity quantile. The main quantitative results are as follows: (1) Digital finance shows significant sustainable effect, and its growth of 1% will reduce the carbon intensity by about 0.092%. (2) The intermediary effects of technological innovation and energy structure are −0.0663 and 0.007, respectively, accounting for 41.88% and 19.36% of the total effects. Both are significant transmission mechanisms. (3) When the carbon intensity in >0.9 quantile, digital finance shows a positive coefficient. On the contrary, the coefficient at <0.75 quantile is negative and its absolute value increases with the decrease of quantile. It shows that with the reduction of carbon intensity, the positive impact of digital finance gradually increases. (4) In the eastern, central and western regions, a 1% increase in digital finance will reduce the carbon intensity by 0.147%, 0.096% and 0.089% respectively. This research provides a reference for regional governments to use digital finance tools to promote sustainable development. • Based on energy consumption, the energy-based carbon intensity of 30 provinces in China is estimated. • Analyzed the impact of digital finance on energy-based carbon intensity. • Examined the intermediary effect of technological innovation and energy structure. • Test the heterogeneity of digital finance under different quantiles of carbon intensity. [ABSTRACT FROM AUTHOR]

Highlights: [•] A quantile model simulated flow-regime effects on freshwater salmon production. [•] I used an evolutionary algorithm to optimize the timing of two pulse flows for salmon. [•] A pulse flow allocated in early summer moderated water temperatures. [•] In wetter years, an earlier floodplain-inundating pulse increased salmon growth. [•] Such pulses might expedite out-migration from rivers that become inhospitably warm . [Copyright &y& Elsevier]

Different from classical Bühlmann and Bühlmann Straub credibility models in which independence between different risks are assumed, this paper takes dependence between risks into consideration and extends the classical Bühlmann model by introducing a common stochastic shock element. What is more, instead of relying on complete information of historical data, we aim to derive the premium using quantile of the available data. By the method of linear regression, we manage to obtain the quantile credibility premium with common effects. Our result is the generalization of existing results in credibility theory. Both quantile credibility model proposed by Pitselis (2013) and credibility premium for models with dependence induced by common effects obtained by Wen et al. (2009) are special cases of our model. Numerical simulations are also presented to illustrate the impact of quantile credibility with common effect. [ABSTRACT FROM AUTHOR]

In real applications of the linear model, the explanatory variables are very often naturally grouped, the most common example being the multivariate variance analysis. In the present paper, a quantile model with group structure is considered, where the number of groups can diverge with the sample size. We introduce and study the adaptive elastic-net group quantile estimator, for improving the parameter estimation accuracy. This method allows automatic selection, with a probability converging to 1, of the non-zero coefficient vectors and further, the asymptotic normality of the non-zero parameter estimators. We also give the convergence rate of the adaptive elastic-net group quantile estimator, rate which depends on the number of the groups. In order to put the estimation method into practice, an algorithm based on the subgradient method is proposed and implemented. The performed Monte Carlo simulations show that the adaptive elastic-net group quantile estimations are more accurate than other existing group estimations in the literature. Moreover, the numerical study confirms the theoretical results and the usefulness of the proposed estimation method. [ABSTRACT FROM AUTHOR]