Panel Quantile Regression Model Research Articles

Evaluating the Heterogeneity Effect of Fertilizer Use Intensity on Agricultural Eco-Efficiency in China: Evidence from a Panel Quantile Regression Model.

Chemical fertilizer is one of the most important input factors in agricultural production, but the excessive use of fertilizer inevitably leads to the loss of agricultural eco-efficiency (AEE). Therefore, it is necessary to explore the impact of fertilizer use intensity (FUI) on AEE. However, ordinary panel regression, based on the assumption of parameter homogeneity may yield biased estimation conclusions. In this regard, a panel quantile regression model (QRM) was constructed with the provincial panel data of China from 1978–2020 to test the difference and variation of this impact under heterogeneous conditions. The model was then combined with the spatial econometric model to explore the effect of the spatial lag factor. The results are as follows: (1) The QSM has unveiled a great improvement space for AEE that remains low overall, despite displaying a rising trend; the highest AEE is in the eastern region. (2) The FUI has a significant negative effect on AEE with the rise in quantiles, this negative effect tended towards weakening overall, although it rebounded slightly; it was stronger in areas with low AEE. It is necessary to consider the heterogeneous conditions in comparison with the average treatment effect of ordinary panel econometric regressions. (3) The impact of FUI shows significant variability in different economic sub-divisions and different sub-periods. (4) After considering the spatial effect of fertilizer use, the negative influence on local AEE had a faster decay rate as the quantile rose, but could produce a positive spatial spillover effect on AEE in neighboring areas. Local governments should dynamically adjust and optimize their fertilizer reduction and efficiency improvement policies according to the level and development stage of their AEE to establish a complete regional linked agroecological cooperation mechanism.

Modelling of South African Hypertension: Application of Panel Quantile Regression.

Hypertension is one of the crucial risk factors for morbidity and mortality around the world, and South Africa has a significant unmet need for hypertension care. This study aims to establish the potential risk factors of hypertension amongst adults in South Africa attributable to high systolic and diastolic blood pressure over time by fitting panel quantile regression models. Data obtained from the South African National Income Dynamics Study (NIDS) Household Surveys carried out from 2008 to 2018 (Wave 1 to Wave 5) was employed to develop both the fixed effects and random effects panel quantile regression models. Age, BMI, gender (males), race, exercises, cigarette consumption, and employment status were significantly associated with either one of the BP measures across all the upper quantiles or at the 75th quantile only. Suggesting that these risk factors have contributed to the exacerbation of uncontrolled hypertension prevalence over time in South Africa.

Towards Achieving Environmental Sustainability: The Role of Nuclear Energy, Renewable Energy, and ICT in the Top-Five Carbon Emitting Countries

In the era of globalization, the incidence of global warming emerges from the issue of climate change, which attracts the attention of several scholars to attain sustainability with respect to ensuring sufficient energy access and diminishing environmental adversities. However, in view of these circumstances, this study examines the heterogenous impacts of nuclear energy, renewable energy, and information and communication technologies (ICTs) on pollution emissions reduction for the top-five emitter countries, covering the data from the period from 1995–2017. This study employs an advanced panel quantile regression model that takes into account both unobserved individual heterogeneity and distributional heterogeneity. The findings illustrate that the effect of all the selected explanatory variables on CO2 emissions is heterogenous along the quantiles. Our outcome supports the notion that nuclear energy consumption is insignificant in contributing to lower environmental pollution. Renewable energy consumption and ICT significantly decrease the carbon emissions of emitter economies, but the negative influence is more robust at the quantiles level (0.30–0.80) and (0.10, 0.20), both factors correct the environmental pollution in the five emitter countries. Finally, the findings of the study provide crucial policy recommendations to policymakers.

Revisiting global energy efficiency and CO2 emission nexus: fresh evidence from the panel quantile regression model.

Given the importance of energy efficiency in environmental degradation, the effects of energy efficiency and renewable and nonrenewable energy consumption on global environmental pollution were investigated. For this purpose, panel data from 107 countries from 1996 to 2014 were examined. In addition, the present study also tested the well-known environmental Kuznets curve (EKC). The long-run relations were estimated by applying a panel quantile regression (PQR) approach, which is useful for finding heterogeneous effects at lower- and upper-level quantiles of CO2 emissions. The empirical results indicated that energy efficiency had a significantly negative impact on CO2 emissions with low intensity at higher-level quantiles.Furthermore, the impact of renewable and nonrenewable energy consumption on environmental degradation was significantly negative and positive across all quantiles, respectively. The empirical results provide evidence supporting an inverted U-shaped nexus between GDP and CO2, whereby the EKC is found valid. Hence, energy efficiency improvement and renewable energy consumption policies must align with strategies to curb environmental degradation.

Testing the heterogeneous effect of air transport intensity on CO2 emissions in G20 countries: An advanced empirical analysis.

This study investigates the heterogeneous impact of air transport intensity, air passenger transport, and air freight transport onair transport carbon emissionsin G20 countries for the period of 1990-2016. The paper employs a robust and advanced fixed-effect panel quantile regression model that considers unobserved discrete and distributional heterogeneity. Our empirical results show that the impact of the independent variables on air transport carbon emissions is quite heterogeneous across various quantiles.More specifically, the effect of air transport intensity, air passenger transport, and air freight transport on carbon emissions is positive and becomes more assertive with the increasing trend at upper quantiles and is quite heterogeneous across all quantiles. Economic growth, urbanization, and tourism are significant contributing factors in enhancing air transport CO2 emissions, while crude oil price significantly reduces CO2 emissions. The Dumitrescu and Hurlincausality test estimates indicate that a bidirectional relationship extends from air transport intensity, air passenger transport, and air freight transport to air transport CO2 emissions. The findings underline the need for cleaner, renewable, and environmentally sustainable energy sources for air transport operations.

The role of bank financing in economic growth and environmental outcomes of sub-Saharan Africa: evidence from novel quantile regression and panel vector autoregressive models.

In sub-Saharan Africa, economic expansion and its environmental implications have become major problems. The banking system has been described as a mechanism for decoupling economic expansion from environmental implications. However, the function of bank financing in the growth-environmental consequences in SSA remains undeveloped. This study investigated the role of bank financing in economic growth and environmental outcomes in SSA over the period 1990-2018. We implemented the novel panel quantile regression and panel vector autoregressive models in a generalized method of moments' framework to investigate the influence of bank financing on economic growth and carbon emissions, and the moderating effect of bank financing in growth-environmental consequences among the four regional economies in SSA. The empirical results revealed that bank financing (1) increases economic growth and carbon emissions across quantiles; (2) positively influences economic growth and carbon emissions of East and Central African regions but negatively influences economic growth and carbon emissions of the West African region; (3) mitigates growth-emissions outcomes of low-emission countries but worsens growth-emissions outcomes of median and high emission countries; and (4) worsens growth-emissions outcomes of East and Central African regions but mitigates growth-emissions outcomes of Southern and West African sub-regions. The variance decomposition and impulse response results discovered that the role of bank financing in growth-environmental challenges varies in terms of magnitude and elasticities across the sub-regions over the sampled period. The study also revealed mixed findings regarding the existence of the EKC hypothesis for the sub-regional economies in SSA.

The heterogeneous dynamic effect of financial development and environmental regulation on Chinese urban green technology management efficiency.

China is facing the large-scale urbanization and great pressure of energy consumption and environmental protection in the past few decades. In this process, urban green technology management efficiency (GMEC) is of great importance for the future work of greener transformation. Using China's 286 city-level panel data from 2006 to 2018, this paper proposes a global Malmquist index, PVAR, and panel quantile regression model to explore the heterogeneous dynamic effect of financial development (FIN) and environmental regulation (ER) on GMEC. The results indicate that during 2006-2018, (1) the growth rate of GMEC fluctuated violently, with a change of more than 28%. In addition, there is "one tight and one loose" phenomenon of GMEC. This indicates that there is much room for the improvement of GMEC; (2) in northeast cities, FIN is not conductive to GMEC, but in other regional cities, FIN has dynamic beneficial effect on GMEC. In addition, FIN represents a changing trend of dropping at first to rising afterwards in all regional cities; (3) furthermore, from time angle, ER has the heterogenous dynamic effect on GMEC among regional cities. Specifically, in northeast, eastern, central, and western cities, GMEC has an "M," "N," "U," and "U"-shaped relationship with the level of ER, respectively. From space angle, there is distinct heterogeneous effect of ER on GMEC at different quantiles within regional cities. Therefore, the local government are supposed to establish and implement policies based on regional cities' characteristics.

Do economic policy uncertainty and geopolitical risk surge CO2 emissions? New insights from panel quantile regression approach.

In recent times, economic policy uncertainty (EPU) and geopolitical risk (GPR) are increasing significantly where the economy and environment are affected by these factors. Therefore, the goal of this paper is to investigate whether EPU and GPR impede CO2 emissions in BRICST countries. We employ second-generation panel data methods, AMG and CCEMG estimator, and panel quantile regression model. The conclusions document that most of the variables are integrated at I (1), and there exists co-integration among considered variables of the study. Moreover, we note that EPU and GPR have a heterogeneous effect on CO2 emissions across different quantiles. EPU adversely affects CO2 emissions at lower and middle quantiles, while it surges the CO2 emissions at higher quantiles. On the contrary, geopolitical risk surges CO2 emissions at lower quartiles, and it plunges CO2 emissions at middle and higher quantiles. Furthermore, GDP per capita, renewable energy, non-renewable energy, and urbanization also have a heterogeneous impact on CO2 emissions in the conditional distribution of CO2 emissions. Based on the results, we discuss the policy direction.

Trade Openness and Green Total Factor Productivity in China: The Role of ICT-Based Digital Trade

The vigorous development of modern information and communication technology (ICT) has driven the digital trade featured by the ICT technique and industry as the carrier. This study empirically tests the impact of ICT-based digital trade openness on green total factor productivity (GTFP) by selecting ICT as the representative digital trade data of 30 provinces in China over the timespan 2002–2018. We employ the slack-based model and global Malmquist–Luenberger (SBM-GML) estimation method to calculate the provincial GTFP and explore the heterogeneous impact of digital trade openness on GTFP through the scale effect, technology effect, and structure effect. In terms of empirical results, the panel fixed model and panel quantile estimation model both suggest the same findings. With the continuous expansion of the scale of digital trade, its scale effect has a significant inhibitory effect on GTFP, whereas the structure effect combined with human capital and the technology effect correlated with technological research and development (R&D) have a significant promoting effect on GTFP. The panel quantile regression model reveals that the interaction intensity increases gradually from a low quantile to high quantile. Further robustness tests also verify the consistency and stability of the results. Finally, the study puts forward corresponding practical suggestions for the construction of a high-quality open pattern of digital trade and the coordinated development of GTFP. The specific policy implications include the following: (1) Emphasize on the penetration and connection effect of the new generation of ICT, and strengthen the construction of enterprise informatization. (2) Expand digital trade openness and broaden the field of industrial cooperation. (3) Optimize the industrial structure of digital trade, and accelerate the development of core industries of digital trade. (4) Gradually promote the transformation of digital trade from relying on quantity and scale to product quality.

ON THE IMPACT OF REAL ESTATE PRICES ON THE DEVELOPMENT OF REGIONAL ECONOMY IN CHINA–AN ESTIMATION BASED ON PANEL QUANTILE REGRESSION MODEL

The paper analyzes the mechanism of real estate prices on economic development with panel quantile regression model. It is found that real estate prices can significantly promote economic development. Generally speaking, the contribution of real estate prices to economic development in regions with higher level of economic development is higher than that in regions with lower level. With the continuous improvement of the quantile, the impact of real estate prices has generally increased gradually, and the impact of urbanization level basically shows the law of diminishing marginal effect.

Analysis of Difference in Household Debt across Regions of Thailand

This study analyzed the determinants of household debt in Thailand at both the regional and the national levels using the panel data of 76 provinces over the years 2009–2017. The Panel Quantile Regression Model was employed to enable the analysis of the formation of household debt ranging from low to high levels. The findings indicate that household indebtedness in different regions has been shaped by a variety of factors, and that households in the same region with different levels of debt burden would experience different impacts or outcomes. We also tested the convergence of household debt, which produced the thought-provoking finding that household debt convergence failed to occur at both the national and the regional levels, while household debt divergence was found instead at the statistical significance level in some regions. The growing debt divergence phenomenon might be an outcome indicator of the unequal access to credit sources among different households in Thai society.

The Heterogeneous Effects of COVID-19 Outbreak on Stock Market Returns and Volatility: Evidence from Panel Quantile Regression Model

The purpose of this study is to probe the impact of the novel coronavirus (COVID-19) outbreak on stock market returns and volatility in developed markets. We employ a panel quantile regression model to capture unobserved individual heterogeneity and distributional heterogeneity. The study's findings reveal that there is a heterogeneous impact of COVID-19 on stock market returns and volatility. More specifically, there is a negative impact of COVID-19 on stock returns in the bearish stock market; however, there is an insignificant impact of COVID-19 on stock returns in the bullish stock market. Furthermore, COVID-19 has a positive impact on stock market volatility across all quantiles.JEL Classification: G24, G30, O16How to Cite:Khalid, N., Zafar, R. F., Syed, Q. R., Bhowmik, R., & Jamil, M. (2021). The Heterogeneous Effects of COVID-19 Outbreak on Stock Market Returns and Volatility: Evidence from Panel Quantile Regression Model. Etikonomi, 20(2), xx – xx. https://doi.org/10.15408/etk.v20i2.20587.

The impacts of governance on environmental pollution in some countries of Middle East and sub-Saharan Africa: the evidence from panel quantile regression and causality.

Governance is one of the basic determinants of pollution levels through property rights, the effective judicial system, etc. it is accepted as that bad governance because of inefficient regulatory structures, government bureaucracy, weak law enforcement, etc. support environmental pollution. In this context, in some countries of the Middle East and sub-Saharan Africa, it will be studied the impacts of governance on environmental pollution over the period of 1996-2018 by the panel quantile and Granger causality methods. The countries were selected by considering two different measurements, EPI (2020) index and governance index (2020). According to EPI (2020), these countries have low scores in terms of environmental quality, and in the governance index (2020), they have bad governance scores. In this study, in which panel quantile regression model is used, control variables are included in the model to prevent omitted-variable bias. The results of the analysis determined that the effect of governance on carbon emissions is positive, as well as that the effects of independent variables on CO2 emission are heterogeneous across quantities. Panel quantile regression revealed the evidence of the relation among the environmental pollution, two parameters of governance, FDI, financial development, human development index, and trade openness used as the explanatory variable and determined that government has the greatest positive effect on CO2 emission. On the other hand, by using traditional Granger causality and Dumitrescu-Hurlin causality methods, it was found the evidence of causality among governance and environmental pollution in the context of two parameters of governance. Accordingly, it was determined the evidence of unidirectional causality relation from political governance to environmental pollution and besides from economic governance to environmental pollution. And it was determined the evidence of unidirectional causality from FDI and the other explanatory variables to environmental pollution.

THE ENERGY CONSUMPTION-FINANCIAL DEVELOPMENT RELATIONSHIP FOR G-7 COUNTRIES FROM PANEL QUANTILE REGRESSION APPROACH

This study focused on examining the relationship among energy prices, financial development, foreign direct investments and economic growth on energy consumption for G-7 countries covered the 1980-2018 data period. We take into energy use as a dependent variable to project countries energy consumption and also financial development is measured by using BankDindex and BondDindex variables, for economic growth GDP per capita, net inflows for foreign direct investment and Brent crude oil price for energy prices. It has been applied the panel quantile regression model which is a new robust econometric method and two models are established. The empirical findings suggest that diverse variables have a clearly heterogeneous effect on energy use. An increase in both energy prices and foreign direct investments increases energy consumption. In Model 1, the effect of financial development on low energy consumption levels is lower, while the effect of the Banking sector is higher in countries with medium and high energy consumption. In Model 2, the Bond sector has a high impact in countries with low energy consumption but is meaningless. It has a positive and significant effect at levels with medium and high energy consumption. The impact of the banking sector on energy consumption is three times greater than the debt sector. In addition, the banking sector has a significant effect on the quantiles of low EC. We found another results for policymakers, to use financial development role in the economic growth process.

COVID-19-related government interventions and travel and leisure stock

COVID-19-related government interventions have significantly affected tourism, while the impact of government interventions on the tourism financial market remains essentially unexplored. This paper comprehensively evaluates how COVID-19-related government interventions affected the travel and leisure stock markets based on a panel quantile regression model. Three government interventions (stringency index, containment and health index and economic support index) and two important stock market features (return and volatility) are discussed. The results reveal that the three government interventions are beneficial to the travel and leisure stock market, especially when the market is under adverse conditions. Specifically, containment and health measures lead to an increase in stock returns. Stringency measures and economic support measures promote stock return and restrain stock market volatility. This study provides significant insights for protecting and recovering the travel and leisure stock market by considering when and which government interventions should be implemented.

Effects of Economic Policy Uncertainty on Manufacturing Structural Upgrading: Evidence from China

Manufacturing is one of the drivers of China’s growth, realizing structural upgrading is vital to achieve high-quality economic development during periods of economic policy uncertainty. Based on firm-level and province-level panel data from 1997 to 2018, this paper used a fixed effect panel data model and panel quantile regression model to investigate the effect of economic policy uncertainty on structural upgrading in manufacturing. The findings indicate that the effect of economic policy uncertainty on structural upgrading in manufacturing is significantly positive and great in regions at advanced stages of manufacturing structure. The discussion about the results suggests that the mechanism of economic policy uncertainty affecting structural upgrading in manufacturing operates through pushing the manufacturing industry to implement service transformation strategies along with vertical integration.

The heterogeneous effects of urbanisation and institutional quality on greenhouse gas emissions in Belt and Road Initiative countries.

The rise of urbanisation in Belt and Road Initiative (BRI) countries that contribute to the disruption of the ecosystem, which would affect global sustainability, is a pressing concern. This study provides new evidence of the impact of urbanisation and institutional quality on greenhouse gas (GHG) emissions in the selected 48 BRI countries from the years 1984 to 2017. The models of this study are inferred by using panel regression model and panel quantile regression model to meet the objectives of our study as it contemplates unobserved country heterogeneity. From the panel regression model, the findings indicate that although urbanisation in BRI supports the 'life effect' hypothesis that could dampen the environment quality, this effect could be reduced through better institutional quality. Using the quantile regression method, this study concludes that one-size-fits-all strategies to reduce GHG emissions in countries with different GHG emissions levels are improbable to achieve success for all. Hence, GHG emissions control procedures should be adjusted differently across high-emission, middle-emission and low-emission countries. Based on these results, this study provides novel intuitions for policymakers to wisely plan the urbanisation blueprints to eradicate unplanned urbanisation and improve institutional quality in meeting pollution mitigation goals.

Non-linear impact of globalization on financial crimes: a case of developing economies

PurposeThe Financial Action Task Force defines money laundering as “processing of these criminal proceeds to disguise their illegal origin”. This is the major portion of financial crime that has ties across borders and like all financial crimes which are well planned and camouflaged, this crime is difficult to detect and deter. Over the years, on one side, globalization has provided development opportunities, it has also become one reason for the pervasiveness of money laundering. This has led to a disturbance in the global financial system and social unrest as proceeds from money laundering are being used in terrorism. The purpose of this study is to explore the non linear effect of globalization on financial crime in the form of money laundering.Design/methodology/approachAn investigation based on 119 developing countries from the time period of 1985 till 2015 is conducted in this study. The panel quantile regression model was used to estimate antecedents of money laundering.FindingsThe study confirmed that globalization follows an inverted U-shaped relationship with money laundering. Furthermore, indicators such as investment portfolio and socioeconomic conditions have a significant effect on money laundering.Originality/valueThe panel quantile regression model was used to estimate antecedents of money laundering.

Innovation agglomeration and urban hierarchy: evidence from Chinese cities

ABSTRACT Innovation agglomeration leads to changes in the hierarchical structure of innovation, driving regional economic development. We establish a general equilibrium framework for urban innovation, that is to say, the process of production in a city can be divided into the knowledge production sector and final goods production sector. By this we explain the positive matching relationship between innovation agglomeration and a city’s position in the urban hierarchy (herein called urban tier). Then we empirically analyse 286 prefecture level Chinese cities to evaluate the impact of urban tier on innovation agglomeration using a two-way fixed effect model. To reveal the nonlinear relationship between urban hierarchy and innovation agglomeration, panel quantile and threshold regression models were also used. The main results show that: (1) The spatial structure of innovation in Chinese cities is consistent with the urban tier, and the urban tier plays a significant role in promoting innovation agglomeration. (2) Heterogeneous spatial structures of innovation among urban tiers are reflected in the higher agglomeration of innovation resources in high-tier cities, and the impact of city scale on innovation agglomeration is nonlinear. When the city scale reaches a certain threshold value, its impact on innovation agglomeration is significantly strengthened.

The heterogeneous impact of environmental regulation on urban green scale economy: an empirical analysis based on city-level panel data in China.

With continued expansions of scale, the Chinese cities are facing unprecedented economic, environmental, and energy sustainability challenges. The aim of this paper is to examine the heterogeneous impact of environmental regulation (ER) on urban green scale economy (GSEC), by combining the global Malmquist index approach, spatial econometric model, panel quantile regression model (QRPD), and the city-level panel data from 2004 to 2018. The estimation results show that (1) from 2004 to 2018, the growth rate of GSEC fluctuates sharply, and the values of GSEC are less than 1 in many years. This indicates that GSEC is far from efficient and needs to be improved in the future; (2) the results of the spatial econometric model show that GSEC has a U-shaped relationship with the level of ER. Furthermore, the estimation results of QRPD verify the above conclusions and show that there is distinct heterogeneity of ER on GSEC at different quantiles and regional cities; (3) the results further indicate that other vital control variables have a significant and heterogeneous effect on GSEC. Therefore, the focus of future work should be adapted to local conditions. Specifically, in the cities with lower-level GSEC, the policymakers should give top priority to break through the U-shaped inflection point as soon as possible and help enterprises to achieve pollution reduction by technical and financial subsidies. While in the cities with higher-level GSEC, the government should further strengthen its ER and encourage competition among enterprises by internal technological innovation. In addition, the government should try to avoid the negative effect of the "resource curse" trap, excessive financial leverage, and "race-to-the-bottom" phenomenon.