Quantile Cointegration Research Articles

Exploring the role of ICT adoption technologies and renewable energy consumption in achieving a sustainable environment in the United States: an SDGs-based policy framework

In recent decades, rapid development has exacerbated climate challenges, posing serious threats to ecological sustainability. To address these issues, renewable energy, ICT technologies, financial development, and globalization have emerged as essential tools for mitigating ecological impacts and fostering green economic growth. These measures align closely with the goals of COP 28, the 2030 Sustainable Development Goals (SDGs), and the commitment to achieve carbon neutrality by 2050. However, the United States faces considerable challenges in reconciling socio-economic development with environmental sustainability. This study, therefore, investigates the key drivers of CO2 emissions (CO2) in the United States using data from 1990Q1 to 2021Q4. The study employs wavelet quantile-on-quantile regression along with quantile cointegration to analyze these dynamics across different quantiles and timeframes. The results from the study showed that across all quantiles and periods, ICT adoption technologies and fiscal decentralization increase CO2, while globalization, renewable energy consumption and financial development lessen CO2.

The Relevance of Green Energy Consumption in Mitigation and Adaptation to Climate Change among the ASEAN Economies using Quantile Cointegration

The present research evaluates the role of green energy in addressing environmental deterioration in the perspective of ASEAN economies. The recent work delves into the potential of green energy to turn the tide, using a panel dataset from 1990 to 2020. By analyzing the data using a non-parametric Granger causality in quantiles technique, the study uncovered a powerful connection between green energy and climate change in these nations. Except for Indonesia, where findings revealed no causal link, all countries showed a clear correlation between green energy consumption and decreased carbon emissions volatility. These findings are a call to action for government and private entities to invest heavily in green energy to prevent ecological collapse in ASEAN nations, which are considered among the most at-risk regions on the planet. It’s time to take bold steps towards sustainability by increasing investment in renewable energy.

What is the role of environmental stress on public health? Asymmetric evidence on carbon emissions, ecological footprint, and load capacity factor

The aim of this paper is to analyze the effects of carbon emission, ecological footprint, which takes into account the demand side of the environment, and load capacity factor, which takes into account both the supply and demand sides of the environment, on health expenditures with conventional and quantile methods. According to the conventional co-integration approach, there is no relationship between the environment and health expenditures. The other side, the findings obtained from the quantile co-integration method, which can give robust results in the presence of tailed distributions and possible endogeneity problems and consider the asymmetric structure in the data set, show the existence of a long-term relationship between the variables. According to the coefficient estimates, while carbon emission and ecological footprint increase health expenditures, the load capacity factor decreases.

Revisiting the interest rate-investment nexus in India: fresh perspective from non-parametric analysis

PurposeIt is generally believed that business spending on capital expenditure tends to decrease as interest rates rise, and vice versa, this is not always the case. The previous literature produces inconclusive results vis-à-vis the interest rate and investment nexus. This study analyzes the responsiveness of investment to changes in high and low levels of interest rates in India through a quantile-based, non-parametric method utilizing annual data from 1980 to 2022.Design/methodology/approachThis study uses Quantile-on-quantile (QQ) technique proposed by Sim and Zhou (2015) to examine the impact of interest rate quantiles on quantiles of investment. In addition, long-term association and the direction of causality are estimated through the Cho et al. (2015) test of quantile cointegration and the Jeong et al. (2012) Granger causality in quantile (GCQ) test, respectively.FindingsThe empirical evidence validates that the linkage between investments and interest rate is not consistently negative and varies from quantile to quantile. The study finds a negative impact at median quantiles and a positive impact at extreme higher quantiles. Conversely, the impact at lower quantiles is negligible, which is also observed from quantile cointegration, indicating the presence of a statistically significant association above the median quantiles. Additionally, the study finds one interesting finding that there exists unidirectional causality from investment to interest rates in India rather than other way around.Research limitations/implicationsThe study provides significant implications for policymakers as it suggests that during extreme economic conditions, the effectiveness of traditional monetary policy tools to boost capital formation is restricted. Policymakers may consider alternative measures to stimulate investment during these time periods. The study additionally posits that the neoclassical theory of investment may not be readily applicable in emerging economies in its unaltered state, mostly due to the lack of well-developed financial markets.Originality/valueThere is a limited literature available on non-linear linkage between interest rates and investment. The present study adds to the existing knowledge by investigating how investment responds differently to fluctuations in interest rates, while incorporating the complete distribution of both the variables.

Nuclear energy, human capital, and urbanization tackling environmental concerns in India: evidence from QARDL and quantile co-integration

Nuclear energy, human capital, and urbanization tackling environmental concerns in India: evidence from QARDL and quantile co-integration

Can ETFs affect U.S. financial stability? A quantile cointegration analysis

This study evaluates whether exchange traded funds (ETFs) threaten financial market stability by testing two hypotheses relating the growing importance of ETFs to increased market volatility and rising equity valuations. We estimate quantile cointegration models using Standard & Poor's 500 Index (S&P 500) and Chicago Board Options Exchange volatility Index (VIX) data for 1994–2020. We found that an increase in ETFs is positively and significantly related to the long-term valuation of the S&P 500 for quantile values above the median. By contrast, ETFs have only a negative and significant effect on the VIX for quantiles around the median. Ultimately, two novel results were obtained. First, the distortion in the value of the S&P 500 relative to its fundamentals is driven by investor flow into ETFs during a bull market. Second, the impact of equity ETFs on the VIX is only affected when fundamental factors are in play, decreasing it. Therefore, ETFs contribute to forming equity bubbles and support valuation market dynamics. Both regulators and policymakers should consider these conclusions.

Quantile-based assessment of energy-CO2 emission nexus in Pakistan.

Nonrenewable energy sources maintain a substantial majority of Pakistan's energy composition, exceeding 70%, posing challenges to achieving sustainability goals for a low-carbon economy. Recognizing this, the study determines the critical thresholds where renewable and nonrenewable energies affect more significantly on CO2 emission over the period from 1972Q1 to 2020Q4. The analysis begins by confirming the stationarity of the data through quantile unit root analysis, followed by an examination of long-term associations using quantile cointegration. For quantile-based impact assessments, we apply quantile regression. To uncover the direction of causality within quantiles, we use a novel approach, quantile causality analysis. Nonrenewable energy sources exhibit a long-term association at disaggregated levels, whereas the same is not true for renewable energy across the quantile distribution. Quantile regression results reveal that renewable energy sources positively impact CO2 emissions, with coal having the highest coefficient, followed by oil and gas, particularly in the upper quantiles, τ = {0.70-0.75}. However, renewable energy sources prove insignificant in decreasing CO2 emissions. Similarly, total energy consumption has a positive influence on CO2 emissions at extremely low quantiles, τ = {0.05-0.30} and high quantiles, τ = {0.65-0.90}, indicating sensitivity to extreme variations. The quantile causality analysis highlights a bidirectional causality relationship among CO2 emissions, total energy consumption, and both renewable and nonrenewable energy consumption across lower and upper quantiles. Policymakers should reallocate funds, provide subsidies, and introduce infrastructure development projects to reduce the burden on nonrenewable energy sources based on this quantile analysis.

Investigating the impact of ICT on transport-based CO2 emissions: empirical evidence from a quantile cointegration regression analysis.

In the global initiative to leverage information and communication technologies (ICT) for reducing emissions, sub-Saharan Africa (SSA), a region of unique significance, has exhibited a delay in adopting ICT. This study aims to investigate the intricate relationship between ICT and carbon dioxide (CO2) emissions from transport in SSA. Employing the panel quantile autoregressive distributed lag (PQARDL) technique, the study analyzes panel data from 24 SSA nations spanning from 2000 to 2021. The results indicate that internet usage and fixed telephone subscriptions have a mitigating effect on CO2 emissions from transport across all quantiles in both the short and long run. However, mobile phone subscriptions contribute to CO2 emissions from transport across all quantiles. Additionally, the middle-income groups demonstrate negative relationships between ICT variables and emissions from transport, while the low-income group exhibits significant positive associations. These findings imply that ICT plays a pivotal role in mitigating transport-based emissions and reveal pronounced disparities in ICT adoption across various income groups within SSA, highlighting overarching underdevelopment in ICT infrastructure. Robustness checks employing a two-step system generalized method of moment (GMM) model reinforce our findings. The study provides policy recommendations, including the promotion of ICT infrastructure development, implementation of smart transportation solutions, and fostering public-private partnerships to address these challenges, shedding light on the path toward a greener and more sustainable transport ecosystem in SSA.

The International Oil Price in the Context of the COVID-19 Pandemic Outbreak: Evidence from BRICS and US

ABSTRACT This study applies a quantile cointegration model to investigate if COVID-19 outbreaks in the BRICS (China, India, Russia, Brazil, and South Africa) and the United States have a long-run equilibrium relationship with the dynamics of oil prices. (1) The standard cointegration models are unstable, indicating the possibility of structural breaks and nonlinearity in the relationship between the COVID-19 pandemic and oil prices; (2) The results of the quantile cointegration model suggest the COVID-19 pandemic and oil prices are nearly cointegrated over whole quantiles of the oil price distribution for the United States, Russia, South Africa, and Brazil. However, the long-run equilibrium relationship between the COVID-19 pandemic and oil prices in China is more likely to occur in the lower quantiles of the oil price distribution; (3) For India, the equilibrium link exists only across the two higher quantiles (0.7 and 0.8 quantiles) of the oil price distribution. Finally, our research has significant policy implications for the governments of the world’s largest countries that are concerned about the impact of the COVID-19 pandemic outbreak on oil prices.

The Emerging Stock Markets and Their Asymmetric Response to Infectious Disease Equity Market Volatility (ID-EMV) Index

The infectious disease equity market volatility (ID-EMV) index, projected by Baker et al. (Baker, SR, N Bloom, SJ Davis, KJ Kost, MC Sammon and T Viratyosin (2020). The unprecedented stock market impact of COVID-19. Review of Asset Pricing Studies, 10(4), 742–758), relates infectious disease to equity market variability during the COVID-19 disease. The ID-EVM index examines the asymmetric influence on the stock market returns of seven developing countries: Mexico, Turkey, Brazil, China, Mexico, India and Indonesia. The investigation applies various statistical estimations, for instance, unit root, quantile cointegration and quantile-on-quantile regression (QQR) approaches. The relation between the stock returns of seven emerging economies and infectious disease EMV index is revealed by the quantile cointegration approach. Additionally, the QQR procedure shows that amid bullish market situation, stock returns are positively influenced by the infectious disease index. While, amid the bearish market situations, stock returns are negatively influenced by infectious disease index, the findings of this research have important policy implications. A piece of valuable information on the nexus between the variability in the equity market and the infectious disease index is provided by this investigation during the COVID-19 pandemic. Policymakers and investors can benefit from this newly introduced ID-EMV index to understand the influence on emerging market countries of this infectious disease.

A safe path towards carbon neutrality by 2050: Assessing the impact of oil and gas efficiency using advanced quantile-based approaches

A safe path towards carbon neutrality by 2050: Assessing the impact of oil and gas efficiency using advanced quantile-based approaches

Wheat Import Demand in Mexico: Evidence of Quantile Cointegration

The decline in the production of basic grains in Mexico has intensified since the 1990s, with wheat (Triticum) being no exception. This reduction was covered by the growth of Mexican imports. The objective of this research was to analyze the import demand function for wheat from 1994, the time of the initiation of the North American Free Trade Agreement (NAFTA). An autoregressive distributed lag (ARDL) model revealed the change in the conditional mean import demand using variations in its determinants, the Global Indicator of Economic Activity (IGAE for its Spanish acronym) and the real exchange rate, as proxy variables for income and relative prices, respectively. However, the conditional mean is insufficient in a context of increasing foreign purchases of wheat and outliers. Through a quantile extension of the ARDL model (with the acronym QARDL), we then found that the change in imports, and the relevance of the determinants, differed across import levels. In the short term, the upper quantiles of wheat imports responded mainly to their history and the exchange rate. Meanwhile, in the long term, the IGAE and the exchange rate influenced the lower quantiles of imports. We conclude that there was an asymmetric response in the conditional distribution of imports. In other words, this study provides evidence of short- and long-term location asymmetry in wheat imports under NAFTA. The research contributes to the econometric study of basic grain imports. For the first time, the QARDL model is used to understand the relationship between imports and their determinants, and the circumstances under which its use is recommended are indicated. Therefore, a new econometric method is used, avoiding the linearity of the ARDL model, and thus allowing a detailed and accurate estimation of the demand for imports. Consequently, the estimates and conclusions obtained will undoubtedly help economic agents to make more efficient decisions, from national and international investors to government agencies responsible for the promotion of Mexican agriculture.

Examination of the impacts of the immediate interest rate of the United States and the VIX on the Dow Jones Islamic Market Index

AbstractThis study examines the effects of the market volatility index of the Chicago Board Options Exchange (VIX) and the immediate interest rate of the United States on the Dow Jones Islamic Market Index (DJIMI) using quantile‐based techniques and wavelet coherence (WTC) analysis with monthly data for the period January 2010to May 2021. A quantile cointegration model indicated that the relationship between the VIX and the DJIMI can be valid in the long term since the estimated coefficients are negative and statistically significant across the quantiles 0.05 and 0.50, while a quantile autoregressive model revealed that large negative and positive changes in the VIX and the immediate interest rate of the United States do not have a significant impact on the DJIMI in the short term. Allowing the role of regime changes, it was found by the quantile regression model that an increase in the VIX lowers the performance in the DJIMI, supported by the WTC. It was also underlined that the DJIMI may not benefit from the positive financial conditions. According to the quantile regression models, the immediate interest rate of the US has asymmetrical effects, and the stabilizing effect of the increase/decrease is valid during bearish/bullish market conditions in the DJIMI.

Income inequality and economic growth in BRICS: insights from non-parametric techniques

The income inequality-economic growth linkage is a topical issue in economics and policy discussions. Both theoretical and empirical results on the impact of income inequality on economic growth have been controversial. One of the criticisms of the existing studies relates to using cross-sectional data and linear estimation techniques for empirical analysis. Capitalising on the limitations in the existing literature, this article employs the novel Quantile-on-Quantile Regression (QQR) approach to examine the relationship between income inequality and economic growth in BRICS. Applying the novel QQR technique helps to model how income inequality distributions affect the distributions of economic growth. The quantile cointegration tests reveal cointegration between income inequality and economic growth. The QQR results indicate that income inequality has a stronger negative effect on the lower and middle tails of economic growth in Brazil while having a stronger positive impact on economic growth in Russia, China and South Africa. For India, income inequality has a stronger negative effect on the lower tail of economic growth and a stronger positive impact on the middle and higher tails of economic growth. These results are consistent with quantile regression results. Further analysis from the Granger causality-in-quantiles shows that at various quantiles, a bidirectional causal relationship between income inequality and economic growth exists in China, while a unidirectional causality runs from income inequality to economic growth in Brazil and India. No causal relationship was found between income inequality and economic growth in Russia and South Africa. The policy implications are discussed.

Quantile causality and dependence between renewable energy consumption, WTI prices, and CO2 emissions: new evidence from the USA.

This study investigates the Granger causal interplays between renewable energy consumption, WTI crude oil prices, and CO2 emissions in the USA using monthly data between January 1986 and May 2022. The study applies quantile-based techniques, namely, the quantile unit root test developed by Galvao (2009), the quantile cointegration test proposed by Xiao (2009), and the novel Granger causality-in-quantiles test suggested by Troster (2018). The results suggest that the series display a unit root across different quantiles and that the cointegration relationships exhibit heterogeneous behavior across quantiles. Regarding the causal linkages, the findings show: (i) a unidirectional causality from renewable energy consumption to oil prices, mainly at the lowest or highest quantiles of the distribution, (ii) a bidirectional causality between oil prices and CO2 emissions mainly in the middle and extreme quantiles, but there is evidence of causality from oil prices to CO2 emissions across the quantiles than in the reverse direction, and (iii) a unidirectional causality from renewable energy consumption to CO2 emissions at the lowest to medium and the highest quantiles. These findings have important implications for policy-makers.

Globalisation and the Nigerian environment: empirical evidence from quantile cointegration

Globalisation and the Nigerian environment: empirical evidence from quantile cointegration

Globalisation and the Nigerian environment: empirical evidence from quantile cointegration

Globalisation and the Nigerian environment: empirical evidence from quantile cointegration

Extended Two-Way Fixed Effects Quantile Cointegration Regression and its Application

Extended Two-Way Fixed Effects Quantile Cointegration Regression and its Application

Does US Infectious Disease Equity Market Volatility Index Predict G7 Stock Returns? Evidence Beyond Symmetry

During the COVID-19 pandemic, Baker et al. (2020) [The unprecedented stock market reaction to COVID-19. The Review of Asset Pricing Studies, 10, 742–758.] proposed the infectious disease equity market volatility (ID-EMV) index, which tracks US equity market volatility caused by infectious diseases. We extended the literature by using this newly developed ID-EMV index to examine its asymmetric effect on the share market returns of the G7 countries, which include the United Kingdom, Italy, Japan, Germany, France, Canada, and the United States of America. Moreover, we used novel techniques like the quantile-on-quantile regression test, quantile cointegration test, and quantile unit root test. The quantile cointegration test indicates that the infectious disease EMV index is cointegrated with G7 stock returns. Moreover, the quantile-on-quantile regression technique reveals that the infectious disease index positively affects stock returns during bullish states of the stock markets. In contrast, it negatively affects stock returns during bearish states of the stock market returns. The negative effect of the bearish states implies that investors may discourage investments during the downturns of the economy, whereas they need to boost their investments during economic booms.

Role of energy consumption and trade openness towards environmental sustainability in Turkey.

The growth of financial services has been critical in Turkey's pursuit of economic growth objectives throughout the last two decades. Nevertheless, it cannot be denied that it has a negative impact on environmental quality. Based on this, in the current paper, the effect of energy use, trade openness, and financial development on the load capacity factor (LCF) is explored for Turkey between 1965 and 2018. In doing so, a series of quantile approaches such as quantile cointegration (QC), quantile-on-quantile regression (QQR), nonparametric causality-in-quantiles (NCQ), and quantile regression (QR) are used. The results generated from the QQR and also validated by the QR reveal that in the majority of the quantiles, primary energy use, trade openness, and financial development impact the LCF negatively. These results suggest that primary energy use, trade openness, and financial development damage environmental quality. Furthermore, the findings gathered from the quantile causality disclose that all primary energy use, trade openness, and financial development can forecast LCF in the majority of the quantiles. Based on the research outcomes, policies, which may aid to solve the damaging environmental effects of the primary energy use, trade openness, and financial sector development in Turkey are recommended.