Panel Quantile Regression Model Research Articles

Harnessing the Power of Globalization: A Study of the Association between Globalization, Transportation Energy, and Insurance and Financial Services in Europe and Central Asia

The increasing globalization and information communication associated with commerce are confronted with the risk experienced over the years in the various production sectors of the economies. The research studied the influence of ICTs capital goods exports, industrial value addition, transportation energy, and globalization on insurance & financial services in a panel of 29 selected European and Central Asian nations. The research spanned from 2012 to 2021 and used a Panel quantile regression model. It was shown that insurance and financial services increased dramatically between the 0.40th and 0.90th quantiles due to technological progress. In addition, for quantiles between 0.10th and 0.90th, industrial value added is positively correlated with increased insurance and financial services. Quantile 0.10t show a negative and significant association between transportation energy and insurance risks. In contrast, quantiles between 0.40th and 0.90th show a positive and significant relationship, indicating a U-shaped relationship between these two variables. The causality findings validated the technology-led industrialization and transportation energy, while a bidirectional association was identified between globalization and transportation energy across nations. The variance decomposition results suggested that industrial value added, globalization, and technological advancement would exert greater variance shocks on the insurance and financial services sector over time. The study concludes that the progression of technology contributes to the maintenance of insurance performance in the age of globalization. This performance might be enhanced by developing more effective transportation energy services and industrial production.

Identifying impacts of industrial co-agglomeration on carbon emissions: Evidence from China.

Based on panel data of 285 cities in China at the prefecture level and above from 2005 to 2020, this paper aims to study the nexus between industrial co-agglomeration and carbon emissions from dual perspectives including space and time. It adopts multiple approaches including a dynamic general method of moment, panel quantile regression model, panel threshold model, and dynamic spatial Durbin model. The non-spatial empirical results support the establishment of the threshold effect and the imbalance effect. The spatial empirical results indicate that industrial co-agglomeration poses a dramatic stimulating effect on urban carbon emissions, and its spatial spillover effect and spatial heterogeneity are conditionally established. Furthermore, heterogeneous effects are supported, such as the positive spillover effects of industrial co-agglomeration are more significant in western cities, resource-oriented cities, and non-low-carbon pilot cities. The heterogeneous influence of cost factors on industrial agglomeration and carbon emissions has also been partially confirmed. In terms of the channels and mechanism of action, the negative externalities of industrial co-agglomeration occupy a dominant position in the current status of economic development. The dynamic equilibrium between government intervention and marketization is a solid foundation for the optimization of carbon emission reduction paths.

Analysis of Spatial-Temporal Differentiation and Influencing Factors of Ecosystem Services in Resource-Based Cities in Semiarid Regions

The spatial-temporal differentiation characteristics and driving mechanisms of ecosystem services are of great significance for optimizing the pattern of land spatial protection and realizing regional sustainable development. Existing studies seldom consider the segmental influence mechanism of various influencing factors on different levels of ecosystem service value (ESV). Therefore, this paper analyzes the temporal and spatial differentiation evolution characteristics of ESV in semiarid regions through an improved ESV evaluation model. The spatial panel quantile regression (SPQR) model was introduced to explore the relationship between various types of influencing factors and ESV in different intervals. The results showed the following: (1) The changes in ESV in Baotou City from 2000 to 2018 tended to be stable, but the spatial differentiation of ESV intensified. The aggregation feature of the low-ESV region is significant and gradually expanding. (2) Precipitation was the dominant factor increasing the ESV in each interval, and temperature had a significant negative impact on the low-ESV area. (3) Higher land use integrity accelerates the decline of ESV in the surrounding areas of built-up areas. The high-ESV area was more sensitive to the intensity of human activity. The direction of human activities should be effectively controlled, and the structure of comprehensive land use should be optimized to enhance the service function of regional ecosystems. This research provides new thinking for the ecological restoration zoning of regional territorial spatial planning and the sustainable development of resource-based cities.

High-dimensional latent panel quantile regression with an application to asset pricing

We propose a generalization of the linear panel quantile regression model to accommodate both sparse and dense parts: sparse means that while the number of covariates available is large, potentially only a much smaller number of them have a nonzero impact on each conditional quantile of the response variable; while the dense part is represent by a low-rank matrix that can be approximated by latent factors and their loadings. Such a structure poses problems for traditional sparse estimators, such as the ℓ1-penalized quantile regression, and for traditional latent factor estimators such as PCA. We propose a new estimation procedure, based on the ADMM algorithm, that consists of combining the quantile loss function with ℓ1 and nuclear norm regularization. We show, under general conditions, that our estimator can consistently estimate both the nonzero coefficients of the covariates and the latent low-rank matrix. This is done in a challenging setting that allows for temporal dependence, heavy-tail distributions and the presence of latent factors. Our proposed model has a “Characteristics + Latent Factors” Quantile Asset Pricing Model interpretation: we apply our model and estimator with a large-dimensional panel of financial data and find that (i) characteristics have sparser predictive power once latent factors were controlled and (ii) the factors and coefficients at upper and lower quantiles are different from the median.

Multi-scales urban-rural integrated development and land-use transition: The story of China

Urban–rural integrated (URI) development strategy is designed to solve the accumulated urban–rural gap of China in the rapid urbanization stage. As the significant link in the interaction of human society and nature, the way of land use has an important impact on URI development. Most existing research evaluates the development level of URI in China from the spatiotemporal evolution path. However, due to the caliber of statistical data and differences in development stage, existing studies mostly focus on a single scale. And few studies have conducted the evaluation of URI level from multi-scales. We unified the evaluation index system of URI and land-use transition at different scales, and introduced panel quantile regression model to calculate the response of URI to land-use transition at different scales. The results of our study show that the impact of land-use transition on URI development exhibits regional and multi-scale characteristics. At the macro scale, rational growth of urban construction land will more effectively promote URI development in underdeveloped areas. At the meso and micro scales, cities (counties) with different URI development levels coexist, and for highly urbanized cities (counties), there is little benefit from promoting the expansion of construction land, and it is urgent to optimize land-use efficiency and land spatial morphology. Lagging development areas need orderly urban–rural land development and construction, optimize land-use functions, and accelerate URI development. Our results indicate that in urban–rural land management and spatial governance, it is necessary to coordinate the overall benefits and local benefits at different scales and develop a land-use transition model that conforms to the regional reality to effectively promote comprehensive URI development.

The impact of intelligent manufacturing on industrial green total factor productivity and its multiple mechanisms

As an integration of artificial intelligence and advanced manufacturing technology, intelligent manufacturing has realized the innovation of manufacturing mode and created conditions for the green development of industry. After constructing a theoretical framework between intelligent manufacturing and industrial green total factor productivity, this paper uses panel data of 30 provinces in China from 2006 to 2020, and expresses the level of intelligent manufacturing with industrial robot density, to discuss the economic effects and mechanisms of intelligent manufacturing. The results show that intelligent manufacturing has a positive effect on industrial green total factor productivity, and the panel quantile regression model indicates that there is an increasing marginal effect. With the quantile points going from low to high, the coefficient and statistical significance become larger. Human capital is the mechanism for intelligent manufacturing to improve industrial green total factor productivity. Green technology innovation and producer service industry agglomeration have strengthened the positive effect. There is also heterogeneity in the effect, and the stronger the effect in regions launched local pilot schemes for carbon emissions trading and industrial green transformation development policy. In order to give full play to the technological dividend and empower sustainable industrial development, the paper argues that we need to accelerate the integration of artificial intelligence and manufacturing technology, thus improving the level of industrial intelligence and empowering green development.

The analysis of the factors affecting the stringency index during COVID-19 pandemic

Coronavirus (COVID-19) pandemic, which started in China’s Wuhan providence in the late 2019s, and then affected the entire world in a short time, causing high disease and death rates, was one of the most important unexpected crises of 21st century. In order to manage the risk the pandemic posed on public health and public order, and to control spread of the disease, governments implemented restriction policies, in which precautions such as limitation and closure were taken. This study aims to examine the factors affecting the stringency index, an indicator of the political measures taken by governments against the epidemic in the selected countries (the United Kingdom, Italy, France, Germany, Türkiye, Russia, Brazil, the United States of America, India) during COVID-19 pandemic. In the analysis, non-additive fixed effect panel quantile regression model with the instrumental variable was used. The data set covers the period between March 11, 2020 and June 29, 2021. The findings indicate that although the level of effects varied, an increase in the number of daily deaths has an increasing effect on the stringency index value in all the countries within the study. Meanwhile, it is observed that as the rate of people with age 65 and over increases, the stringency measures also increase in the countries implemented moderate and high-level restrictions.

Moderation of Services' EKC through Transportation Competitiveness: PQR Model in Global Prospective.

The continuously increasing GHG emissions have created environmental pollution and several challenges to ecosystems and biodiversity. The challenges of climate change are multipronged, resulting in melting glaciers, flash floods, and severe heat waves. In this regard, the adaptive and mitigation strategies to manage the consequences of climate change are highly important. The transport sector creates a quarter of carbon emissions, and this share is continuously increasing. Accordingly, this research study uses transport competitiveness to determine carbon emissions of the transport sector for 121 countries covering the time period from 2008 to 2018. The Panel Quantile Regression (PQR) technique is engaged to analyze the study results. The findings highlight that transport competitiveness tends to increase carbon emissions of the transport sector across quantile groups 1 and 3, while it reduces carbon emissions in quantile group 2. The U-shaped services' EKC is validated in quantile groups 2 and 4. The moderation engaged, i.e., transportation competitiveness, changes the turning point of the services' EKC across quantile groups 2 and 4. However, in the high-CO2 quantile group, the moderation impact of transport competitiveness is strongest as it reduces the sensitivity by flattening the services' EKC. Furthermore, the planned expansion of the population and improved institutional quality tend to mitigate carbon emissions across different quantile groups. The policy relevance/implications that are based on the study results/findings are made part of the research paper.

Industrial digitization and synergy between pollution and carbon emissions control: new empirical evidence from China.

Industrial digital transformation is a key engine to help developing countries reduce pollution and carbon emissions. We used the composite system synergy model (CSSM) and modified entropy weight method to measure the degree of synergy between pollution and carbon emissions control (SPCEC) and the level of industrial digitization in each province and city based on the Chinese inter-provincial panel data from 2011 to 2020. We then used the two-way fixed effects and panel quantile regression models to test the heterogeneous influence of industrial digitization on the SPCEC. We found that: (1) industrial digitization had a positive contribution to the SPCEC. (2) Digitization of industry contributes more to the SPCEC level than the digitization of agriculture and services. (3) The promotion of SPCEC by industrial digitization is significant in the western region, but not in the eastern, central and northeastern regions. (4) In provinces and municipalities with lower level of SPCEC, the contribution of industrial digitization to the SPCEC is higher. This paper reveals the impact of industrial digitization on the SPCEC and can provide a policy reference for the realization of the SPCEC from the perspective of the integration of industry and digitization.

Are Islamic and conventional banks decoupled? Empirical evidence from Turkey

PurposeThis study investigates the determinants of net interest margin (NIM) and tests the decoupling hypothesis in Turkey's Islamic and conventional banks.Design/methodology/approachThis study has employed a panel quantile model (PQM) to assess the net interest margin (NIM) and test the decoupling hypothesis in the dual banking system of Turkey.FindingsThe empirical results show that the impact of equity is positive for both Islamic and conventional banks but relatively more robust for Islamic banks. Moreover, it is observed that return on assets has a positive association with NIM in both types of banking systems. Interestingly, the impact increases from lower to higher quantiles, but a higher acceleration rate is observed for Islamic banks. The study also finds that, as bank stability increases, NIM decreases for both groups of banks but more stably for Islamic banks, resulting in lower margins than conventional banks. Thus, the paper confirms the decoupling hypothesis and suggests that, to increase profit margins, Islamic banks need to increase assets and equity.Practical implicationsThe paper confirms the decoupling hypothesis and suggests that to increase profit margin, Islamic banks need to increase assets and equity.Social implicationsSince both equity and assets contribute positively to interest margins, policymakers in the industry need to increase the size of equity and assets to get maximum returns.Originality/valueThis is one of the first studies to investigate NIM's determinants and test the decoupling hypothesis in the Turkish dual banking system using a non-parametric MCMC panel quantile regression (QRM) model.

Impact of digital finance on energy-based carbon intensity: Evidence from mediating effects perspective

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.

Curing the resource curse with the adoption of resource-rich energy in MINT countries: An application of quantile regression

Policy analysts have focused on environmental concerns along sustainable growth for the last few decades. Recently, resource-rich energy resources have gained much importance due to their alternative role in managing ecological concerns. The study intends to examine the connection between resource-rich energy consumption (RREC) and ecological footprint (EFP), considering the Environmental Kuznets Curves (EKC) framework. The data covered in the study ranged from 1960 to 2020. The empirical analysis is performed for the MINT countries (Mexico, Indonesia, Nigeria, and Turkey) by utilizing the panel Quantile regression (PQR) model. The findings indicate that an “inverted U-shaped relationship” exists between GDP and EFP that validates the EKC hypothesis for the sample economies. The analysis highlights that at a higher level of EFP the environmental quality deteriorating effect is stronger along with strong quality improving impacts. Further, RREC and EFP are negatively and significantly related, suggesting that RREC is an alternative energy source that helps control environmental pressure by reducing EFP in the MINT countries. According to the research, achieving sustainable growth in the future requires a shift from “conventional energy sources” to “green energy sources.” It also contributes to meeting the Paris Agreement's carbon-cutting commitments. Therefore, MINT economies should work together to develop RREC in their respective nations and collaborate with global economies to control global EFP. Additionally, funding RREC installation projects and appliances would enhance environmental quality and help to sustain steady growth.

Innovations, energy consumption and carbon dioxide emissions in the global world countries: An empirical investigation

Technological innovations are the important sources of economic growth of a country and it is inter associated with other factors such as energy consumption, economic growth and carbon dioxide emission. A change in these factors affect the capability of technological innovation and thus the effect of these factors on innovations need to be explored. This study investigates the effect of carbon dioxide, energy consumption and economic growth on innovations proxies by different innovations indicators. The sample data is collected from 1980 to 2019 of the world 181 countries and OLS, fixed effect, two step Generalized method of moments and panel quantile regression models were employed for data analysis. The results reveal that carbon dioxide and economic growth increase technological innovations while the inflow of FDI decrease innovations output. Energy consumption also negatively affects innovation indicators except for research and development. In the case of quantile regression, energy consumption is positive while carbon dioxide and foreign direct investment are negative across different quantiles for research and development. Energy consumption and foreign direct investment reduce technological innovations proxy by patent application residents while carbon dioxide and economic growth increase it. The findings of this study have considerable policy suggestions for the global countries.

The impact of the social adaptability of different groups on the human capital level in an uncertain environment: Panel data analysis based on 35 urban life satisfaction.

To explore the role of the social adaptability of different groups in human capital in an uncertain environment, this paper empirically tests the relationship between social adaptation and human capital by using a panel quantile regression model (QRPD) with the life satisfaction of 35 cities in China as an indicator. By selecting Nelder Mead and adaptive MCMC estimation methods, this paper finds that (1) the higher the level of social adaptation is, the more obvious the promotion effect on the level of human capital; (2) for eastern cities, social adaptation can promote the improvement of the human capital level; for central or western cities, the relationship between social adaptation and human capital is not obvious and depends on the method selected for estimation. To enhance the social adaptability of individuals, the government leadership should actively create conditions, carry out targeted social adaptation training and active adaptation skills exchange for different social groups in order to strengthen social adaptability and enhance human capital. At the same time, policymakers need to consider the differences between the eastern, central and western regions and choose the most suitable human capital improvement action framework for the region to manage environmental uncertainty and make regional human capital sustainable competitive in the era of uncertainty.

Does internet infrastructure improve or reduce carbon emission performance? --A dual perspective based on local government intervention and market segmentation

The Internet itself contains the characteristics of technological progress, as a green technological means to promote the green development of the economy. The study focuses on the practical issue of how Internet infrastructure affects the efficiency of carbon emissions. It uses panel data of prefecture-level cities from 2004 to 2017, based on the carbon emission efficiency measured using the slacks-based measure-global Malmquist–Luenberger index model, employing the static panel and panel quantile regression models. The different degrees of the impact of internet infrastructure on carbon emission efficiency were quantified from the perspectives of the local government intervention status and market segmentation, and the following results were obtained: First, Internet infrastructure improves total factor carbon emission performance significantly, with significant inhibitory effects on carbon emission intensity under the conditions of market segmentation and government intervention. Second, the interactions of internet infrastructure with local government intervention and market segmentation improved the carbon emission performance. Third, internet infrastructure enhanced carbon emission performance significantly (by 25%) under the local government intervention and market segmentation conditions, and the degrees of carbon emission efficiency varied with both contributions. Thus, this study revealed how local government intervention and market fragmentation can break the bottleneck of the effect of internet infrastructure on carbon emissions and how the government can optimize the external environment continuously to ensure the positive role of internet infrastructure in carbon emission performance.

Association between energy consumption preferences and macroeconomic stability: an empirical analysis from developing Asia.

The study examined the impact of macroeconomic performance and energy consumption on carbon emissions in Asian developing countries. Although, macroeconomic performance is a critical measure for the determination of development in societies. Thus, the examination of macroeconomic performance variables is a core issue for policymakers. The panel quantile regression model assesses the macroeconomic performance at several quantiles, ranging from 0.05 to 0.95. The effect of macroeconomic performance and energy consumption on CO2 emissions has also been established. The findings indicate that CO2 emissions and macroeconomic performance are negatively associated in all quantiles of macroeconomic performance. Though, fossil fuel energy and population growth positively influence carbon emissions in all low and medium quantilesbut this relationship becomes weaker in theupper quantiles. In addition, hydroelectricity consumption and carbon emissions are negatively and significantly associated in the quantile ranges of low and higher. Based on the aforementioned results, the study suggests the formulation of such policies that can improve macroeconomic performance in the region and the promotion of hydel energy instead of fossil fuels to reduce environmental pollution in developing Asian countries. Moreover, the population control program, promotion of renewable energy, and dangers of fossil fuel consumption should be a part of public policies to control environmental issues by reducing carbon emissions at the regional level.

The asymmetric relationship between sustainable innovation and industrial transformation and upgrading: Evidence from China's provincial panel data

Previous studies have considered the one-way relationship between sustainable innovation and industrial transformation or upgrading. This research further investigates the asymmetric relationship between sustainable innovation and industrial transformation or upgrading from long-term and short-term perspectives. We analyse 30 provinces in China from 2001 to 2020 using ordinary least squares and panel quantile regression models to explore the positive effect of sustainable innovation on industrial transformation and upgrading with spatiotemporal heterogeneity. The panel autoregressive distributed lag (ARDL) model and panel vector autoregression (PVAR) further survey the causal relationship between sustainable innovation and industrial transformation and upgrading. The findings reveal that sustainable innovation promoted advancing industrial structures over the long-term and short-term promotion of industrial structure rationalisation. Simultaneously, there are low mutual coordination and extension between sustainable innovation and industrial transformation and upgrading. For regions with high coordination between sustainable innovation and industrial transformation and upgrading, the interaction between sustainable innovation and the advanced industrial structure is more pronounced. The paper's results confirm the asymmetric relationship between sustainable innovation and industrial transformation and upgrading, and the findings have crucial significance for the sustainable development of China's economy.

Impact of Economic Progress, Energy Consumption and Carbon Dioxide Emissions on Life Expectancy: Evidence from Asia and Africa

In today's society, determining living standards requires taking into account life expectancy. Thus, a key concern for policymakers is the analysis of life expectancy characteristics. The life expectancy is evaluated using the panel quantile regression model across many quantile ranges, including 0.05, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, and 0.95. From 2000 through 2018, the study examines the effects of economic progress, energy use, and carbon dioxide releases on life expectancy across Asia and Africa. The analysis shows that CO2 emanations and life expectancy are intimately linked across all life expectancy quantiles. The impact of economic progress on life expectancy is negative in all except 0.95 quantiles. Additionally, except for the higher quantile (0.95), there is a negative and significant correspondence between hydroelectricity usage and life expectancy in the low and higher quantile series. The findings have thus shown beneficial impacts on life expectancy in low, medium, and greater quantities of petroleum and other liquid consumption. The outcomes suggest boosting the corporate structure to increase productivity and development. However, implementing a clean form of energy sources, i.e., renewable energy and technological efforts, need to employ excellently, contributing to environmental sustainability and a healthy ecosystem.

Yüksek Hava Kirliliği Yaşanan Ülkelerde Doğumda Yaşam Beklentisi ve Çevresel Bozulma Bağlantısı

The aim of this study is to examine the effects of PM2.5 air pollution, public health expenditures, fertility and mortality rates on life expectancy at birth between 2000-2019 in a total of 32 countries, which are in the categories of countries with hazardous, very unhealthy and unhealthy air pollution in the World Air Quality Report (2021), using a panel quantile regression model. Correlation analysis, multicollinearity, normality tests and Hausman test methodologies are followed in line with the aim. Panel quantile regression analysis is performed for the consecutive 10th-90th quantile range, and the findings are interpreted by estimating with robust standard errors for the case where the heterogeneity assumption is confirmed. In the estimation results with robust standard errors, it is reached that all variables in the model are statistically significant at 1% significance level. According to the results, a 5% increase in PM2.5 air pollution, fertility and mortality rates decreases life expectancy at birth by ~0.02%, ~0.17% and ~0.09%, respectively, while a 1% increase in health expenditures increases life expectancy at birth by ~0.04%. In this context, it is seen that the variable that affects life expectancy at birth the most is the fertility rate, and the variable that affects the least is PM2.5 air pollution.

Dynamic evolution and influencing factors of coupling coordination between innovation environment and transformation development of resource-based cities in Gansu province, China

Studying the internal relationship between the innovation environment and the transformation development of resource-based cities from the perspective of coupling and coordination helps reveal the mechanism between them, which is of great significance to sustainable development of resource-based cities. This study reconstructs the index system for innovative environment and transformation development. We used the coupling coordination degree model, Epanechnikov function, and panel quantile regression model to analyze the coupling coordination level, dynamic evolution characteristics, and influencing factors of innovative environment and transformation development of resource-based cities in Gansu Province from 2009 to 2019. The study shows that the innovation environment index and the transformation development index of resource-based cities increased significantly during the study period. The innovation environment index of resource-based cities has been greatly improved, showing a dynamic evolution trend of rapid growth and development characteristics of “low-level, fast growth”. There are differences in the characteristics of transformation development in resource-based cities, but on the whole, it shows a trend of increasing fluctuation. The state of coupling coordination has changed from a severely unbalanced state to a moderately unbalanced state; some cities have turned to a low-level coordination state, and the overall trend is presented to optimize the process continuously. The quantile regression results show that the level of economic development and industrial structure has a significant positive effect on improving the coupling coordination degree of the two. The degree of government intervention has a negative impact. The level of capital investment has a negative effect at a high score but has no significant impact at a low score. This study tries to provide a scientific basis for formulating the policy of coordinated development between an innovation environment and transformation development of resource-based cities and also provides a reference for high-quality development of other resource-based cities.