Common Correlated Effects Research Articles

The Refinement of a Common Correlated Effect Estimator in Panel Unit Root Testing: An Extensive Simulation Study

The Common Correlated Effect (CCE) estimator is widely used in panel data models to address cross-sectional dependence, particularly in nonstationary panels. However, existing estimators have limitations, especially in small-sample settings. This study refines the CCE estimator by introducing new proxy variables and testing them through a comprehensive set of simulations. The proposed method is simple yet effective, aiming to improve the handling of cross-sectional dependence. Simulation results show that the refined estimator eliminates cross-sectional dependence more effectively than the original CCE, with improved power properties under both weak- and strong-dependence scenarios. The refined estimator performs particularly well in small sample sizes. These findings offer a more robust framework for panel unit root testing, enhancing the reliability of CCE estimators and contributing to further developments in addressing cross-sectional dependence in panel data models.

Sustainable development goals in emerging markets: Aligning the role of financial inclusion in promoting environmental quality of the Next‐11 economies

AbstractPursuing sustainable development has emerged as a paramount global objective, with the United Nations' Sustainable Development Goals (SDGs) serving as a guiding framework. Increasing environmental degradation is associated with increased greenhouse gas emissions, deforestation, industrial waste, fossil fuel depletion, and loss of biodiversity. This study has analyzed the effect of financial inclusion on ecological sustainability while controlling the effect of financial development, energy consumption, economic growth, urbanization, and trade openness in the Next‐11 economies from 1995 to 2019. The study develops the financial inclusion index through principal components analysis by inculcating three demand‐side and three supply‐side factors of financial inclusion. To analyze the developed model, various first‐generation and second‐generation techniques are applied. The results of Westerlund co‐integration reveal significant long‐run co‐integration among the series. The long‐run elasticities are estimated through dynamic common correlated effect estimation and generalized method of moments where results reveal that FI helps to secure environmental sustainability by reducing the ecological footprint, hence it works on the SDG framework. Economic growth and financial development are found to be the root causes of increasing ecological footprint. So, it is the penetration of excessive credit, not the financial inclusivity which needs to be directed toward sustainability. It is suggested that the governments of Next‐11 countries should steer the capital toward sustainable usage and should continue facilitating financial services to curtail environmental degradation. Our findings have significant repercussions for Next‐11 countries, giving decision makers fresh perceptions of financial inclusion and development implications.

Moment-Based Estimation of Linear Panel Data Models with Factor-Augmented Errors

Abstract I compare two popular methods of estimation for linear panel data models with unobserved factors: the first eliminates the factors with a parameterized quasi-long-differencing (QLD) transformation. The other, referred to as common correlated effects (CCE), uses cross-sectional averages of the data to proxy for the factor space. I show that the CCE assumptions imply unused moment conditions that can be exploited by the QLD transformation. I also derive new linear estimators that weaken identifying assumptions and have desirable theoretical properties. Unlike CCE, these estimators do not require the number of covariates to be less than the number of time periods. I provide the first proof of a fixed-T consistent mean group estimator for heterogeneous linear models with interactive fixed effects. I investigate the effects of per-student expenditure on standardized test performance using data from the state of Michigan.

A Cross-National Study on Sustainable Smart City Indicators and Their Influence on Life Expectancy—A Cluster Analysis of EU Countries

As a consequence of climate change and its negative impacts on the environment and on human health, the topic of sustainability has become an integral part of urban policy. Smart city initiatives around the world are focusing on different aspects of sustainability in order to provide better living conditions for their residents. The aim of this study is to investigate the impact of selected smart city indicators on the average life expectancy as a variable for quality of life and well-being. Based on a Common Correlated Effects (CCE) model, Instrumental Variable Estimator with Common Factors (2SIV), and clustering regression model, EU countries were divided into three distinct clusters indicating common elements but also specificities of each group. The analysis confirmed the positive impact of GDP growth, renewable energy consumption, and the proportion of the population with a tertiary level of education on life expectancy. On the other hand, CO2 emissions and transport pollution have an adverse effect. The analysis provides valuable insights into the complex relationship between smart city variables and quality of life, and it may serve as a basis for informed and responsible decision-making by relevant urban stakeholders aimed at designing more sustainable, resilient, and healthier cities.

Analyzing the Dynamics: Asymmetric Effects of Economic Growth, Technological Innovation, and Renewable Energy on Carbon Emissions in Africa

This study examines the interactions among economic growth, technological innovation, renewable energy consumption, and carbon emissions in Africa using advanced econometric methods. Panel data analysis reveals both symmetric and asymmetric impacts on carbon emissions, showing how economic and technological variables contribute to environmental outcomes. The augmented mean group (AMG) and common correlated effects mean group (CCEMG) estimators address cross-sectional dependence and heterogeneity, ensuring robust results. Findings indicate that positive economic growth increases emissions, while economic downturns significantly reduce emissions. The study underscores the need for strategic policies that leverage technological innovations and renewable energy to promote sustainable development in African economies, balancing economic growth with environmental protection.

Empowering China's energy renaissance: Electrochemical storage strategies driving economic growth and policy evolution

This study presents an innovative framework to examine the interplay between foreign direct investment and economic development from 1996 to 2021. Using the Panel NARDL approach, our study provides an empirical framework for exploring these complex relationships. We enhance the reliability of our results by integrating additional methodologies, such as augmented mean group and common correlated effects mean group. A decline in carbon emissions is commonly hypothesized to be correlated with economic expansion, foreign direct investment inflows, advancements in renewable energy, and implementation of environmental technologies. On the contrary, detrimental disturbances have the potential to result in protracted ecological deterioration, especially in situations where there are setbacks in economic expansion and foreign direct investment. It is worth noting that although short-run and long-run coefficients exhibit comparable directional trends, there are discrepancies in their magnitudes and levels of statistical significance. This sophisticated comprehension empowers countries such as China, among others, to allocate resources strategically towards sustainable energy initiatives, promoting a systematic approach that prioritizes the adoption of renewable energy, advancements in technology, attraction of foreign investment, and transformation of structures.

Do Remittances Have Asymmetric Impacts on Human Development? Panel Evidence from South Asia

Globally, the remittances have increased significantly recently, most of which have gone to developing countries. In light of recent evidence of the role of remittances in economic development, it is pertinent to investigate how human development responds to remittances. Based on human capital and investment decision theory, the present study investigates the short and long-run asymmetric impacts on human development for six South Asian countries from 1990 to 2021 using the panel asymmetric autoregressive distributive lag model (PNARDL). A robustness check has been done using the dynamic common correlated effect pooled mean group effect model (CCEPMG). The direction of causality is examined using a panel dynamic heterogenous model. The result of this study indicates that remittances have a significant short and long-run asymmetric impact supporting the human capital and investment decision theory. Results suggest positive shocks promote human development while negative shocks have the opposite effect. Causality results suggest a two-way relationship supporting remittance-led human capital development in South Asia. In line with the above findings, the study recommends encouraging higher remittances and integrating development policies with migration policies to promote education, health, and human development.

Tourism development and carbon emissions in sub-Saharan African countries: is there an inverted U-shaped relationship?

ABSTRACT In this study, we examine the existence of an inverted U-shaped relationship between tourism and carbon emissions in 29 sub-Saharan African (SSA) countries using data from 1995 to 2019. Departing from the previous studies, we have also grouped SSA countries into low- and middleincome countries, thereby estimating three panels. We have used a wide range of panel estimation techniques to examine this linkage, including the dynamic common-correlated effects (DCCE), the Driscoll-Kraay estimators, and the fully-modified OLS (FMOLS). Our results show that the relationship between tourism and carbon emissions depends largely on the estimation technique used and the level of income of the individual countries. Overall, we can conclude that there is primafacie evidence of an inverted U-shaped relationship between tourism and carbon emissions in middle-income countries and all SSA countries combined, but not in low-income countries. This means that the perceived adverse effects of tourism expansion on environmental pollution may only be experienced in the early stages of tourism development and that as the tourism sector develops, tourism is likely to promote energy conservation through sustainable tourism or green tourism, thereby leading to a reduction in carbon emissions. Policy implications are discussed.

The effect of output level, exchange rate and interest rate on inflation in BRIC-T countries

PurposeIn this study, it is analyzed the validity of the exchange rate pass-through (ERPT) effect and the effect of interest rate and output level on the inflation rate (IR) in Brazil, Russia, India, China and Turkey (BRIC-T) between the years 1995Q1 and 2022Q4.Design/methodology/approachThe methods such as the panel unit root test developed by Westerlund (2012), the LM bootstrap panel cointegration test developed by Westerlund and Edgerton (2007), the common correlated effects (CCE) estimator developed by Pesaran (2006) and the augmented mean group (AMG) estimator developed by Eberhardt and Bond (2009) that take into account the cross-section dependency are applied for analysis.FindingsAs a result of the findings, it is determined that the ERPT effect is valid in Turkey, Brazil, Russia, India and China and the cost channel is valid only in China. Finally, it is found out that output level positively affects inflation in Turkey, Brazil, Russia, India and China.Practical implicationsAll these results indicate that the economies of Turkey, Russia, Brazil and India have a fragile structure, especially in terms of inflation. Therefore, the central bank of these countries should maintain exchange-rate stability to implement the inflation-targeting strategy successfully. In this context, central bank independence should be increased in these countries in achieving this objective. Also the results indicate that it is still early to consider whether BRIC-T countries and accordingly the Belt and Road Initiative will be an alternative against the domination of the USA and European Union (EU) on international trade system or it will substitute them.Originality/valueIn this study, it is tested that the impact of interest-rate (NIR), exchange-rate (FER) and output level (IPI) on general level of prices. Besides, it is analyzed that whether production level affects the IR. Also, the study investigates the economic issues such as ERPT effect and cost channel. The study analyzes whether China's Belt and Road Initiative is successful or not. In this study, we used the panel data methods that allow for structural breaks and cross-section dependency. For these reasons, this study differs from other studies in the literature both in terms of scope and methods used.

Role of natural resources, renewable energy sources, eco-innovation and carbon taxes in carbon neutrality: Evidence from G7 economies

Global warming has created problems for human life, and it has been increasing for a few years. All the developing and developed countries are establishing policies to attain zero carbon status. This study extends the ongoing debate on carbon emissions. It examines the effect of natural resources and RE (Biofuel and other renewable sources) on greenhouse gas (CO2 emission and PM2.5) emissions while using data over 22 years (1999–2021) from G7 countries. In addition, this study has investigated the effect of carbon taxes, financial development, and environmental policies on carbon neutrality. The cross-sectional-ARDL, the Common correlated effect means group (CCEMG), and the Augmented mean group (AMG) cutting-edge model have been employed. Quantile regression has been employed for robustness. The study results demonstrate that biofuel and other renewable energy (RE) sources, carbon taxes, environmental policy, and eco-innovation decrease greenhouse gas emissions (CO2 emissions). Meanwhile, financial development, and natural resource dependence positively impact carbon neutrality. The robustness result also verifies the findings from CS-ARDL, AMG, and CCEMG methods. The empirical findings are used to infer policy implications for G7 economies.

Bridging the digital divide: the impact of technological innovation on income inequality and human interactions

This study seeks to understand the nuanced relationship between technological innovation and income inequality with an emphasis on the broader implications of this interplay on human–technology interactions in diverse socioeconomic settings. Using cross-country panel data from 59 nations (31 developed and 28 developing) from 1995 to 2020, the study employed the common correlated effect mean group (CCEMG) estimator. The robustness of our findings was validated using the augmented mean group (AMG) estimator and the panel causality test. The results indicate that technological innovation, while heralded for its potential to bridge communication and operational gaps, inadvertently exacerbates income disparities, with a pronounced effect in developed economies. Moreover, interactions between technological innovation and variables such as economic growth, globalisation and export trade introduce additional complexities, including both buffering and acceleration effects on the primary relationship. These findings shed light on the double-edged nature of technological advancements, underscoring the need for informed policy-making that harnesses the benefits of innovation while mitigating its unintended socioeconomic consequences. The study sets the stage for domain-specific explorations such as in education, public health and business. It also invites interdisciplinary discourse on the ethical and behavioural dimensions of technology adoption, especially user experiences and societal outcomes.

Do circular economy, renewable energy, industrialization, and globalization influence environmental indicators in belt and road initiative countries?

This paper is the first comprehensive research to examine the effect of circular economy on environment employing two environmental degradation indicators (CO2 emissions, ecological footprint) and one environmental quality indicator (load capacity factor) for 57 Belt and Road Initiative (BRI) countries during 2000-2019. The effect of other variables such as renewable energy, industrialization, and globalization was also controlled. The study applied the cross-sectional autoregressive distributed lag method (CS-ARDL), the augmented mean group (AMG), and common correlated effects mean group (CCEMG) methods as a robustness checks. The empirical findings reveal that circular economy and renewable energy have pro-environmental effects by decreasing carbon emissions and ecological footprint and increasing the load capacity factor in BRI countries. However, industrialization and globalization have detrimental effects on the environment. The result of causality shows a bidirectional causality between renewable energy, circular economy, industrialization, and three environmental indicators, but the relationship of globalization with CO2 emissions and the load capacity factor is unidirectional and with the ecological footprint is bidirectional. All the results are confirmed by the robustness tests. The study suggests policy implications for the BRI government.

Unravelling the Link Between Bilateral Investment Treaties and Environmental Sustainability in Sub-Saharan African Countries

This study investigates the practical relationship between foreign direct investments (FDI), bilateral investment treaties (BITs), income, energy consumption and CO2 emissions in sub-Saharan African (SSA) countries between 2000 and 2020. To address the challenges of heterogeneity, non-stationarity and cross-sectional dependence, we employed the dynamic common-correlated effects estimation method introduced by Chudik and Pesaran. This methodology provides reliable results for panel datasets of significant to moderate size. The results indicate a direct connection between FDI and CO2 emissions, providing evidence for the pollution haven theory. Notably, BITs also have a favourable and substantial impact on environmental degradation in SSA countries. These findings emphasise the need for effective environmental regulatory frameworks and management techniques. Achieving sustainable growth and economic development in SSA countries requires prioritising the adoption and implementation of carbon reduction strategies. SSA countries can attain balanced growth between economic progress and environmental preservation by reducing the negative effects of FDI inflows and BITs on environmental sustainability. This study expands the current knowledge base by providing insights into the complex relationship between FDI inflows, BITs, income, energy consumption and CO2 emissions in SSA countries. The findings offer valuable guidance to policymakers and stakeholders in formulating and executing effective environmental policies and regulations to promote sustainable development in the area.

Towards sustainable development in African countries: Are modern and combustible renewable energies effective?

AbstractEnvironmental degradation and climate change problems in the African continent seriously hit country economies and human health, negatively affecting the achievement of the Sustainable Development Goals (SDGs). African countries have the potential to use economic development and renewable energy as a tool for their environmental sustainability. In this context, this study examines the effects of economic growth, modern renewable energy, and combustible renewable energy on ecological footprint (EF) in five African countries. To this end, the study uses novel regularization common correlated effects and half‐panel jackknife approaches on data from 2006 to 2020. The findings of the study show that economic progress effectively reduces the EF of African countries, but renewable energy does not contribute to ecological progress. Due to their insufficient and ineffective utilization, renewable energies are not an important tool for improving ecological systems. Therefore, African countries should prioritize their economic development as a priority goal. Based on the results, African countries experiencing economic advancement should bolster for SDGs objectives through green financial incentives.

Exploring the impact of green finance and technological innovation on green economic growth: Evidence from emerging market economies

AbstractThe sustainable future of emerging market economies (EMEs) lies within achieving sustainable development goals (SDGs), specifically SDG 8, which is linked with green economic growth (GEG) and its determinants, such as green finance (GF) and technological innovation (TI), along with some control variables. However, the current literature has insufficiently addressed these determinants in the context of emerging and developing economies. In this regard, this study deployed the most effective and advanced techniques like cross‐sectional auto‐regressive distributed lags, augmented mean group, and common correlated effects mean group to handle the issues of endogeneity, cross‐sectional dependence, and heterogeneity, which is not an easy task for conventional methods used in existing studies. This study has compiled a panel data set spanning from 2000 to 2019, across 13 EMEs. The main outcomes revealed that GF, TI, and human capital (HC) help in fostering significant GEG, whereas financial development (FD) and economic globalization (ECOGLB) fail. The study underscores the need for policymakers to focus on the promotion of diffusion of GF and enhancement of TI and HC while regulating the FD and ECOGLB process to steer emerging nations toward sustainable development.

Impact of green technology and energy on green economic growth: role of FDI and globalization in G7 economies

With the increase in economic growth, the world is facing serious challenges concerning environmental sustainability. Hence, the green economic growth is imperative for sustainable and inclusive development. The objective of this study is to contribute to the existing literature about the factors that influence green economic growth. The study investigates the role of green technology, green energy, foreign direct investment, and globalization on green economic growth in G7 countries. The data of the study is collected from WDI, KOF Swiss Economic Institute, and OECD database and the data period ranges from 1995 to 2020. The existence of cointegration between the variables of the study was tested by Westerlund’s (Oxford Bull Econ Stat 69(6):709–748) cointegration test. Due to the presence of cross-sectional dependency, the study employed the cross-sectional autoregressive distributed lag (CS-ARDL) method to estimate the coefficients in the long and short run. The study also used a common correlated effect—mean group (CCEMG) estimator for robustness check. The findings of the study reveal that green energy and FDI positively contribute to green economic growth in the long and short run. The green technology also contributes positively to enhance green economic growth but only in long run. To accelerate green economic growth, G7 countries should incorporate policies promoting green energy and technology, while acquiring more foreign investments to ensure a sustainable development.

Testing the ecological effect of wind and solar energy consumption: A novel regularized common correlated effect approach for top oil‐importing countries

AbstractTop oil importer countries produce large amounts of carbon emission and pollute soil and water, threatening sustainable development. Given this issue, this study examines the impact of solar energy consumption, wind energy consumption, and natural resources rent on the environmental conditions of the top five oil‐importing countries (China, the United States, India, Japan, and South Korea) for the period 1997–2021. In this context, the study analyzes the validity of the environmental Kuznets curve and load capacity curve hypotheses for the top five oil‐importing countries using the novel regularized common correlated effects method. The regularized common correlated effect results show that the environmental Kuznets curve and load capacity curve hypotheses are valid and that wind energy consumption supports environmental quality by helping to reduce carbon emissions and increase the load capacity factor. However, solar energy usage and natural resources rent have no impact on ecological quality. Based on these outcomes, the top five oil importers can provide a better environment for their societies by promoting wind energy consumption while taking environmental concerns into account.

Revisiting the EKC framework concerning COP-28 carbon neutrality management: Evidence from Top-5 carbon embittering countries

In the aftermath of the 28th Conference of the Parties (CoP) climate summit in the UAE, the majority of developing countries encounter challenges in attaining their objectives of carbon neutrality for a sustainable economy. The association of economic factors such as economic growth, governance structures, forest area, renewable energy consumption, technological innovation, and urbanization with environmental elements (carbon footprint) is vital for sustainable economic development and environmental management strategies. Therefore, this research reveals this association in five selected high-emitting countries spanning from 1990 to 2022. This research utilizes the Environmental Kuznets Curve (EKC) framework to investigate the interrelationship between these variables. To do so, this study employs the cross-sectional autoregressive distributed lags (CS-ARDL) statistical technique to determine the short- and long-term impacts of the variables under investigation on carbon footprint. In contrast, the mean group (MG) and common correlated effect mean group (CCEMG) have been applied for robustness. The findings revealed that GDP, urbanization, and forest area have positive associations with carbon footprints, whereas GDP square, renewable energy consumption, technological innovation, and governance effectiveness have inverse relationships with carbon footprints. These findings provide all stakeholders with valuable policy recommendations and management advice for accelerating the transition of renewable energy to low-carbon and green growth.

A path towards environmental sustainability: exploring the effects of technological innovation and investment freedom on load capacity factor

ABSTRACT This study examines the impact of investment freedom, technological innovation, renewable energy, and economic growth on load capacity factor (LCF) within the context of Sustainable Development Goals (SDGs) 7 and 13 for a group of high investment freedom countries (Luxembourg, Germany, Austria, Australia, Canada, Denmark, Netherlands, United Kingdom, Chile, Singapore, New Zealand, United States of America, Belgium, Finland, Uruguay, Latvia, Spain, Sweden, and Switzerland). Furthermore, this research assesses the impacts of achieving carbon neutrality by 2030. The data set covers the years between 1995 and 2019. Moreover, the validity of the load capacity curve (LCC) hypothesis is analyzed in all countries. Long-run coefficients are estimated using the Regularized Common Correlated Effects (rCCE) estimator, and the robustness analysis is performed using Common Correlated Effects (CCE) estimators. The overall assessment of the panel reveals that the LCC hypothesis is invalid in the selected nations, with the exception of Belgium. Other findings indicate that investment freedom reduces the LCF for New Zealand. However, investment freedom increases LCF, improving environmental quality in Latvia. Technological innovation decreases LCF for Singapore and increases it for Germany. Renewable energy increases LCF for the UK and Spain. Finally, policy implications for improving environmental quality are discussed.

Forecasting of clean energy market volatility: The role of oil and the technology sector

This study is the first to explore whether the well-known relationship between the clean energy sector, oil prices, and technology stocks can be leveraged to enhance the accuracy of realized volatility forecasts for individual clean energy sub-sectors. Based on intraday data and various decompositions of daily realized volatility, we account for the heterogeneity across clean energy sub-sectors using the dynamic common correlated effect heterogeneous autoregressive (DCCE-HAR) model. Our findings reveal that, in the short term, price variations in technology shares are more informative for future clean energy volatility than fluctuations in oil prices. In an out-of-sample analysis, we individually forecast the volatility of each clean energy sub-index using Lasso, Ridge, and random forest approaches. We identify sub-indices that systematically benefit from technology sector price variation (e.g. Smart Grid, Operators, Energy Management), sub-indices that benefit from oil price variation (e.g. Bio Fuel, Wind and Geothermal), while also sub-indices that show limited sensitivity to price variation in the technology and oil markets.