Quantile Regression Techniques Research Articles

Factors Influencing Underwriting Performance of the Life and Non-Life Insurance Markets in South Africa: Exploring for Complementarities, Nonlinearities, and Thresholds

ABSTRACT Underwriting is crucial for insurers’ performance and sustainability, yet empirical evidence on factors influencing its performance has received limited attention. Therefore, this study investigates this phenomenon considering South Africa’s life and non-life insurance sectors between 2013–2019. Using the generalized method of moments and the bootstrap quantile regression techniques for analysis, the findings show that insurance size, market share, and investment income significantly and positively impact underwriting performance. However, premium growth weakens the underwriting performance of both sectors. Also, non-life insurance shows a positive relationship for reinsurance, while life insurers present an inverse relationship. Underwriting risk negatively impacts non-life performance across quantiles, while life insurer risk was negative in higher quantiles. More so, initial solvency levels weaken underwriting performance, while extreme increase improves it, implying a direct U-shaped relationship. Hence, highly solvent insurers are more likely to succeed in underwriting operations. Additionally, market share complements return on assets in promoting underwriting performance. Policy recommendations are discussed.

Environmental sustainability, terrorism, and military expenditure in Sub-Saharan Africa: Evidence from panel quantile ARDL regression and panel causality

The prevalence of terrorism and military expenditure in Sub-Saharan Africa (SSA) is experiencing rapid and significant growth over time. This study investigates the impact of terrorism and military expenditure on environmental sustainability in 35 economies in SSA from 1990 to 2021, utilizing panel quantile autoregressive distributed lag regression and causality analysis techniques to bridge the gap in existing studies on the military expenditure, environmental sustainability, and terrorism nexus. The study's novelty is evident because it assesses the short-term and long-term impacts of military expenditure and other covariates across various quantiles of CO2 emissions. The study achieves this by categorizing SSA nations based on their varying levels of CO2 emissions, specifically low, moderate, and high, an approach that is extremely rare among SSA countries. The study's findings revealed that terrorism and military expenditure have a positive impact on CO2 emissions across the quantiles, implying that these variables contribute to environmental degradation in the region. The study also reveals a bidirectional relationship between CO2 emissions and the selected predictors (terrorism, military expenditure, trade, and economic growth). Thus, the study proposes military strategies and macroeconomic policies that ensure environmental sustainability.

Mineral resource drivers in the global south: A case study of Australia

Mineral resource depletion directly restricts sustainable mineral development. This study investigates the drivers of sustainable mineral resource development via an evolutionary game model involving mineral firms, the Australian government, and financial institutions. The tripartite model provides a guide to strategies and interactions with external influences based on company liquidity. Leveraging panel data from Australian mining companies from 2015 to 2022, this study utilizes unconditional quantile regression techniques, culminating in three salient discoveries. First, despite financial limitations, small-to medium-sized enterprises (SMEs) can accumulate ample resources to enhance their green technology R&D and corporate social responsibility (CSR) expenditure through diverse financing methods, thereby creating an internal strategic impetus for CSR perpetuation. Second, by participating in CSR, mineral firms can transmit positive signals in an asymmetric market, which promotes their sustainable development. Finally, despite the Australian government's R&D tax refund policy incentivizing R&D investment, SMEs' lack of innovative capacity has led to the diversion of operational funds, which negatively impacts their sustainable development. These findings provide valuable insights for sustainable mineral resource development that extend beyond Australia to other Southern Hemisphere nations.

The Role of Individual Income in Modulating the Impact of Health Determinants: Exploring the Interplay between Socioeconomic Factors and Health Outcomes

This article analyses the self-perceived health of the population surveyed by the European Health Interview Survey (EHIS), with 316,277 observations. The main novelties of this research are: first, we use the econometric technique of quantile regression, which will allow us to distinguish the respondent’s income level. Second, the personal, social, lifestyle and macroeconomic context dimensions of the respondents are considered simultaneously as determinants of self-perceived health. In this way, we will be able to see what the determinants of health are, and whether they vary with income. Finally, it is evident that there are indeed different responses to the same stimulus depending on the level of income, especially in the elasticity of the response, seeing how the higher the income, the more or less the same stimulus influences a person. In addition, it is established that age, nationality and employment status are the most influential variables in self-perceived health.

Exploring how economic growth, renewable energy, internet usage, and mineral rents influence CO2 emissions: A panel quantile regression analysis for 27 OECD countries

This study aims to investigate the impacts of renewable energy consumption, internet usage, mineral rent, and economic growth on CO2 emissions across 27 OECD nations between 2001 and 2020. We employ a panel quantile regression technique to discover the heterogeneous effects of these variables for the various quantile levels of environmental destruction. The panel quantile regression approach found the Environmental Kuznets curve dominated countries in which CO2 emissions were low. Additionally, the negative relationship between renewable energy consumption and CO2 emissions yielded in the paper suggests the potential benefits of incentivizing and investing in renewable sources. Internet usage demonstrates a negative impact on CO2 emissions, showcasing the role of digital technologies in promoting sustainability. Accordingly, internet use improves environmental quality due to more efficient, sustainable, and eco-friendly practices, which increase productivity. However, a positive association between mineral rents and environmental deterioration emphasizes the necessity of strategic policies to balance economic benefits with environmental preservation. Our study provides policymakers with valuable insights into tailoring effective strategies for sustainable development, renewable energy adoption, digitalization, and natural resource utilization while addressing environmental degradation.

Searching for homogeneous regions in regional flood frequency analysis for Southeast Australia

Study regionSoutheast Australia Study focusThis study examines identification of homogeneous regions for regional flood frequency analysis (RFFA) using 201 gauged catchments and applying principal component analysis (PCA) and cluster analysis. Quantile regression technique (QRT) is applied to develop prediction equations. Leave-one-out (LOO) validation is used to evaluate the accuracy of the developed prediction equations. New hydrologic insights for the regionFor the regions formed based on PCA and cluster analysis, the Hosking and Wallis’ heterogeneity (H1) statistics are found to vary in the range of 11.55–24.52, and 5.11–26.27, respectively. The assumed regions are highly heterogeneous as H1 values are much higher than 1.00. The median of absolute relative error values associated with the developed prediction equations for the assumed regions are found to vary in the range of 17–62 %, which are comparable to similar RFFA studies. PCA and cluster analysis do not deliver sufficiently homogeneous regions in southeast Australia. The degree of heterogeneity of a region has little effect on the relative accuracy of flood quantile estimates. The findings of this study will assist to update RFFA technique in Australian Rainfall and Runoff (national guideline).

Adaptation and innovation: How does climate vulnerability shapes corporate green innovation in BRICS

In the face of escalating environmental challenges, gaining insights into their influence on corporate strategies has become crucial because it paves the way for proactive and adaptive measures. In view of this, the primary objective of study is to explore the relationship between climate vulnerability (CVN) and corporate green innovation (GIN). The study leverages a comprehensive dataset spanning 13 years (2010−2022) across enterprises within BRICS nations. The regression was conducted by employing system GMM model and robustness of analysis was checked by considering the quantile regression technique. The findings reveal a significant negative relationship between CVN and GIN, signifying that heightened vulnerability to climate hazards impedes corporations' capacity for green innovation. The adverse impact of CVN on corporate green innovation (GIN) persisted consistently across various facets. The study emphasizes the importance of strategic measures to counteract the adverse effects of climate vulnerability on green innovation. Corporate managers need to prioritize climate resilience strategies, integrating sustainable innovation into their business models to navigate environmental challenges and ensure long-term viability. This study makes a valuable contribution to the existing literature by investigating the influence of climate vulnerability on corporate green innovation, presenting a unique research focus that fills an apparent gap in the current body of knowledge.

The effect of renewable energy aid and governance quality on environmental tax effort in Sub-Saharan Africa

The literature on foreign aid and tax efforts largely overlooks the specific role of environmental aid and taxation. To fill this gap, we examine the effects of renewable energy development assistance (EDA) and governance quality on climate change tax effort, air pollution tax effort, and aggregate environmental tax effort in Sub-Saharan Africa (SSA). Utilizing panel data from 15 SSA countries over 20 years (2000–2019), the sample is divided into two groups based on income levels: seven low-income countries (LIC) and eight middle-income countries (MIC). The instrumental variable generalized method of moments (IV-GMM) regression technique is employed to estimate the parameters of the baseline model specifications. The study also uses the Smoothed Instrumental Variable Quantile Regression (SIVQR) technique to test the robustness of the baseline model. Among middle-income SSA countries, the findings suggest that renewable energy aid inflows reduce climate change tax efforts but increase air pollution and aggregate environmental tax efforts. However, among low-income SSA countries, renewable energy aid negatively impacts all three categories of environmental tax efforts. Governance quality tends to enhance all three categories of environmental tax effort in both low-income and middle-income SSA countries. The implications of these results are discussed.

Revitalising small tourism destination states: necessity and strategies for structural change in tourism development

ABSTRACT This paper delves into the controversy surrounding the link between structural change and tourism development, particularly focusing on small tourism states, over the period 1995Q1–2020Q4 using a panel-based approach. To address the research objective, the Quantile-on-Quantile (QQ) regression approach is utilised to assess the impact of different quantiles of structural change on the quantiles of tourism development. To ensure robustness, the outcomes of the QQR approach are compared with those of the conventional quantile regression technique. Empirical findings from the QQR approach reveal nuanced relationships. In Belize, Cyprus, Dominican Republic, Iceland, Malta, and Seychelles, structural changes exhibit a structural-increasing effect on tourism development. Conversely, in the Bahamas, Fiji, and Trinidad & Tobago, structural changes manifest both structural-increasing and structural-decreasing effects on tourism development. In Bahrain, structural changes have a structural-decreasing impact on tourism development. Furthermore, the study establishes a feedback nexus between tourism and structural change, offering feasible policy suggestions for policymakers. Overall, the findings suggest that sound policy responses to the relationship between structural change and tourism development should be context-specific, responsive to changing dynamics, and aimed at maximising the positive impacts of structural changes while mitigating potential negative consequences.

The impact of COVID-19 on deaths from dementia and Alzheimer’s disease in Chile: an analysis of panel data for 16 regions, 2017–2022

Although several studies have documented the detrimental impacts of global COVID-19 containment measures on individuals with Alzheimer's disease and dementia, a comprehensive analysis of mortality rates for these conditions within the Chilean population is notably lacking. This study aimed to analyze the impact of COVID-19 on mortality rates among individuals with dementia and Alzheimer's disease in Chile. A retrospective longitudinal cross-sectional study was conducted, considering mortality data for specific mental health conditions during the pre-pandemic and pandemic contexts of COVID-19 in Chile. Quantile regression techniques were employed to analyze the existence of differences between the two periods, while non-observable heterogeneity models for panel data methods were used to evaluate the effect of COVID-19 mortality on crude mortality rates. Statistically significant differences were observed in the number of deaths from dementia and Alzheimer's disease between the pre-pandemic and COVID-19 pandemic periods. Specifically, crude mortality rates decreased by 10% (-0.10 [95% CI:-0.16,-0.05]) during the pandemic period. Furthermore, the number of deaths from COVID-19 during the pandemic period has a very weak incidence of deaths from mental health conditions such as dementia and Alzheimer's. Specifically, a unit percentage increase in confirmed cases from COVID-19 would result in a 7% (-0.07 [95% CI:-0.13,-0.001]) decrease in the number of deaths from dementia and Alzheimer's. These findings are supported by the application of panel regression with one-way random effects models. The study findings indicate a reduction in mortality rates attributed to dementia and Alzheimer's disease during the COVID-19 pandemic in Chile. This decline could be attributed to the potential underreporting of mental illness as the cause of death during the pandemic period. Several studies have highlighted that approximately 30% of death certificates fail to document the presence of a dementia syndrome. Moreover, the cause of death recorded for individuals with mental health conditions may be influenced by the physician's familiarity with the patient or reflect the prevailing approach to managing end-stage dementia patients. This work received no funding.

Does financial inclusion and information communication technology affect environmental degradation in oil-producing countries?

Advances in financial inclusions have contributed to economic growth and poverty alleviation, addressing environmental implications and implementing measures to mitigate climate change. Financial inclusions force advanced countries to progress their policies in a manner that does not hinder developing countries' current and future development. Consequently, this research examined the asymmetric effects of information and communication technology (ICT), financial inclusion, consumption of primary energy, employment to population ratio, and human development index on CO2 emissions in oil-producing countries (UAE, Nigeria, Russia, Saudi Arabia, Norway, Kazakhstan, Kuwait, Iraq, USA, and Canada). The study utilizes annual panel data spanning from 1990 to 2021. In addition, this study investigates the validity of the Environmental Kuznets Curve (EKC) trend on the entire sample, taking into account the effects of energy consumption and population to investigate the impact of financial inclusion on environmental degradation. The study used quantile regression, FMOLS, and FE-OLS techniques. Preliminary outcomes revealed that the data did not follow a normal distribution, emphasizing the need to use quantile regression (QR). This technique can effectively detect outliers, data non-normality, and structural changes. The outcomes from the quantile regression analysis indicate that ICT consistently reduces CO2 emissions in all quantiles (ranging from the 1st to the 9th quantile). In the same way, financial inclusion, and employment to population ratio constrains CO2 emissions across each quantile. On the other side, primary energy consumption and Human development index were found to increase CO2 emissions in each quantile (1st to 9th). The findings of this research have implications for both the academic and policy domains. By unraveling the intricate interplay between financial inclusion, ICT, and environmental degradation in oil-producing nations, the study contributes to a nuanced understanding of sustainable development challenges. Ultimately, the research aims to guide the formulation of targeted policies that leverage financial inclusion and technology to foster environmentally responsible economic growth in oil-dependent economies.

Identification of the Dynamic Trade Relationship between China and the United States Using the Quantile Grey Lotka–Volterra Model

The quantile regression technique is introduced into the Lotka–Volterra ecosystem analysis framework. The quantile grey Lotka–Volterra model is established to reveal the dynamic trade relationship between China and the United States. An optimisation model is constructed to solve optimum quantile parameters. The empirical results show that the quantile grey Lotka–Volterra model shows higher fitting accuracy and reveals the trade relationships at different quantiles based on quarterly data on China–US trade from 1999 to 2019. The long-term China–US trade relationship presents a prominent predator–prey relationship because exports from China to the US inhibited China’s imports from the United States. Moreover, we divide samples into five stages according to four key events, China’s accession to the WTO, the 2008 global financial crisis, the weak global economic recovery in 2015, and the 2018 China–US trade war, recognising various characteristics at different stages.

The tail connectedness among conventional, religious, and sustainable investments: An empirical evidence from neural network quantile regression approach

AbstractFinancial markets are highly unpredictable and often associated with tail risks. This study examines the tail connectivity among three distinct markets—conventional, religious, and sustainable—and uses a new neural network quantile regression technique to quantify their risk exposure. The findings suggest that traditional and religious investments have the greatest tail risk exposure during crises, emphasising the importance of diversification using sustainable investments. The Systematic Network Risk Index identifies intense events, such as the COVID‐19 pandemic, the European debt crisis, and the global financial crisis, as having the greatest tail risk. The Systematic Fragility Index finds the Islamic stocks during the COVID‐19 crisis and the conventional stock market before the pandemic to the highly vulnerable markets. On the other hand, the Systemic Hazard Index identifies Islamic stocks as the primary source of systemic risk. The study concludes by providing implications for decision‐makers, regulatory authorities, investors, players in the financial system, and investment managers to diversify their risk by utilising green/sustainable investments.

Photovoltaic Power Prediction Based on Hybrid Deep Learning Networks and Meteorological Data.

Conventional point prediction methods encounter challenges in accurately capturing the inherent uncertainty associated with photovoltaic power due to its stochastic and volatile nature. To address this challenge, we developed a robust prediction model called QRKDDN (quantile regression and kernel density estimation deep learning network) by leveraging historical meteorological data in conjunction with photovoltaic power data. Our aim is to enhance the accuracy of deterministic predictions, interval predictions, and probabilistic predictions by incorporating quantile regression (QR) and kernel density estimation (KDE) techniques. The proposed method utilizes the Pearson correlation coefficient for selecting relevant meteorological factors, employs a Gaussian Mixture Model (GMM) for clustering similar days, and constructs a deep learning prediction model based on a convolutional neural network (CNN) combined with a bidirectional gated recurrent unit (BiGRU) and attention mechanism. The experimental results obtained using the dataset from the Australian DKASC Research Centre unequivocally demonstrate the exceptional performance of QRKDDN in deterministic, interval, and probabilistic predictions for photovoltaic (PV) power generation. The effectiveness of QRKDDN was further validated through ablation experiments and comparisons with classical machine learning models.

Evaluating Innovation and Institutions for Tech-Trade: A Global Assessment in the Quest for Sustainable Economic Prosperity

Sustainable economic prosperity lies in the production of technological products that use the latest technology and do not depend upon natural, non-renewable sources. Trade of such goods boosts the economy and diversifies its existing trade pattern. This study analyses various variables that contribute to high-tech exports. All countries of the world that have more than one per cent share of high-tech exports to manufactured products are taken from 2008 to 2021. A robust statistical method called Feasible Generalized Least Squares (FGLS-hetero) is used to deal with heteroscedasticity. To minimize the impact of outliers, quantile regression, and bootstrap quantile regression techniques are also used. The main purpose of using multiple regression techniques is to assess whether the sign and significance of the coefficient will change by change in estimation techniques. The findings reveal that innovation, access to information, strong institutions, and partnerships between universities and industries all play a significant role in boosting high-tech exports in all estimation techniques. Financial development has a significant direct impact on high-tech export in the case of simple linear regression and FGLS (hetero) but an insignificant positive impact in the case of quantile regression and bootstrap quantile regression.

Inflation at risk in advanced and emerging market economies

Using quantile regression techniques, we study the drivers of inflation risks in a large panel of advanced and emerging market economies (EMEs). We document several facts regarding the inflation forecast distribution and highlight some key differences between these two groups of countries. First, the exchange rate has a quantitatively important and non-linear impact on the inflation outlook in EMEs: a depreciation is associated with larger increases in the upper quantiles than in the lower quantiles, increasing the right skewness of the distribution. By contrast, there is no evidence of such non-linearities for advanced economies. Second, tighter financial conditions in EMEs carry both downside and upside risks to inflation, while having a muted impact on the modal or mean outcome. This is in contrast to advanced economies, where only downside risks prove sensitive. Third, the zero lower bound on policy rates translates into substantial downside risks to inflation. Finally, the adoption of inflation targeting is associated not only with lower mean inflation but also with a less right-skewed distribution. Our findings underscore the importance of including non-linearities in structural models of inflation dynamics.

Investigation of the factors driving engineering insurance consumption in China’s construction industry: a hybrid approach

PurposeGiven the increasing importance of engineering insurance, it is still unclear which specific factors can enhance the role of engineering insurance as a risk transfer tool. This study aims to propose a hybrid approach to identify and analyze the key determinants influencing the consumption of engineering insurance in mainland China.Design/methodology/approachThe empirical analysis utilizes provincial data from mainland China from 2008 to 2019. The research framework is a novel amalgamation of the generalized method of moments (GMM) model, the quantile regression (QR) technique and the random forest (RF) algorithm. This innovative hybrid approach provides a comprehensive exploration of the driving factors while also allowing for an examination across different quantiles of insurance consumption.FindingsThe study identifies several driving factors that significantly impact engineering insurance consumption. Income, financial development, inflation, price, risk aversion, market structure and the social security system have a positive and significant influence on engineering insurance consumption. However, urbanization exhibits a negative and significant effect on the consumption of engineering insurance. QR techniques reveal variations in the effects of these driving factors across different levels of engineering insurance consumption.Originality/valueThis study extends the research on insurance consumption to the domain of the engineering business, making theoretical and practical contributions. The findings enrich the knowledge of insurance consumption by identifying the driving factors specific to engineering insurance for the first time. The research framework provides a novel and useful tool for examining the determinants of insurance consumption. Furthermore, the study offers insights into the engineering insurance market and its implications for policymakers and market participants.

Synthesizing the role of biomass energy consumption and human development in achieving environmental sustainability

Promoting sustainable energy use is crucial to achieving environmental sustainability. However, there is limited documentation on how human development moderates the effect of energy consumption on environmental sustainability. Therefore, this study aims to uncover the impact of biomass energy consumption and human development on the ecological footprint in 31 African countries from 1996 to 2018. To achieve this, we employ Driscoll-Kraay, instrumental variables, and panel quantile regression techniques to address heteroscedasticity, endogeneity, and conditional heterogeneity. The result shows that biomass energy consumption increases ecological footprint, while human development reduces ecological footprint. The results also show that human development reduces the adverse effect of biomass energy consumption on environmental sustainability. Moreover, the pairwise Dumitrescu-Hurlin panel causality results prove the two-way causal association between biomass energy consumption and ecological footprint. The feedback effect between human development and the ecological footprint is also supported. To improve environmental sustainability, policymakers should promote sustainable biomass energy consumption through awareness and education campaigns.

General literacy and effective taxation system in Africa

ABSTRACT This inquiry provides empirical evidence on the effect of general literacy on tax revenue mobilization in Africa over the period 1996–2021. This evidence is important given the low level of taxation in Africa and the lack of clarity on the nexus between a country’s literacy level and taxation effort. The paper employs a simultaneous quantile regression, and uses three different indicators of tax revenue mobilization as a percentage of GDP. The motivation for quantile regression technique is primarily based on two essential reasons: (i) the argument that the effect of literacy on taxation effectiveness is dependent on the existing level of tax revenue mobilization; (ii) tax revenue mobilization policies, to be effective, need to be adapted differently across countries with low, intermediate, and high levels of existing tax mobilization effort. Results show that improvement in general literacy appears to promote tax mobilization effort, and that the effect of general literacy on taxation is stronger for direct taxes than for indirect taxes (in terms of both significance and magnitude). Furthermore, in most of the results, the impact of general literacy is slightly stronger in the upper quantiles of the general literacy distributions. The results remain robust over alternative econometric estimation techniques.

Exchange rate misalignment and financial development in Africa

AbstractWe examine the effect of misaligned exchange rates on financial development in Africa. Results from quantile regression techniques and the IV Lewbel estimator reveal that exchange rate misalignment significantly hampers financial development on that continent. This result is robust across financial institutions and financial markets. We also show that while the effects of misaligned exchange rates are negative on financial institutions and positive on financial markets in African franc‐zone countries, the effects are consistently negative across all financial sectors in the non‐franc‐zone countries there. When robustness assessment is done using quantile regression, the results show that the negative effect of misalignment on financial development is only feasible from the 75th percentile and higher in Africa in general and for the non‐franc‐zone countries in particular.