Panel Vector Autoregressive Model Research Articles

Dynamics between energy intensity and carbon emissions: What does the clustering effect of labor and capital play?

Energy depletion and environmental degradation have emerged as pressing concerns in the era of China's high-quality green economic development. We present an integrated framework that incorporate energy consumption intensity, industrial clustering, and carbon emissions into the STIRPAT framework. Our study examines 30 Chinese provinces from 2006 to 2017. Unlike previous analyses, our study measures industrial clustering from two dimensions: labor and capital clustering. Specifically, the benchmark results confirm that a 1% increase in energy consumption intensity will, on average, raise regional carbon emission intensity by 0.03389%. Moderating analysis shows that labor and capital clustering significantly lower carbon emission intensity by reducing energy consumption. These findings have been consolidated after performing a panel vector autoregressive model. Interestingly, the promoting effect of energy consumption intensity on carbon emission intensity tends to be tightened when labor clustering surpasses a threshold. In contrast, the corresponding effects substantially weaken after capital clustering exceeds a threshold. In all, these findings provide new evidence to understand the energy-emission nexus from an industrial clustering perspective.

Tourism Transport-Related CO2 Emissions and Economic Growth: A Deeper Perspective from Decomposing Driving Effects

This study explores the intricate dynamics of CO2 emissions stemming from transport within the tourism sector. It aims to unravel the multidimensional aspects of how transport-related tourism contributes to CO2 emissions and to elucidate the complex relationship between regional economic growth and CO2 emissions from transport-related tourism. Our study employed Logarithmic Mean Divisia Index (LMDI) and Panel Vector Autoregression (PVAR) models to analyze data from 30 Chinese provincial regions between 2010 and 2018. The tourism transport-related CO2 emissions were decomposed into four separate driving effects using the LMDI approach. Then, PVAR models were constructed to reveal dynamic interactions between each driving effect and per capita gross regional product (GRP). Our results demonstrate a decrease in both energy structure effect (tourism transport-related CO2 emissions from energy structure) and energy intensity effect (tourism transport-related CO2 emissions from energy intensity) during the pre-COVID decade. Notably, the positive impulse response of energy structure effect to per capita GRP is observed. However, we found no evidence of a cointegrated relationship between energy intensity effect and regional economic growth, although other factors demonstrated connections. These findings echo the necessity to integrate sustainable practices into the tourism transportation business, especially in the area of energy structure, in order to mitigate adverse environmental effects from tourism. This paper disseminates the main drivers of CO2 emissions in the tourism transport sector and their interrelationship with regional economic growth. It not only guides tourism policymakers in targeting efforts to reduce carbon footprints, but also sets a new benchmark for future studies on CO2 emissions.

Towards the environment of legitimacy: Do the institutional quality and policy uncertainty matter for the performance of stock markets of South Asian countries?

PurposeThis paper assesses the environment of legitimacy by determining the role of institutional quality and policy uncertainty on the performance of five major South Asian stock markets (India, Pakistan, Bangladesh, Sri Lanka, and Nepal) using 21 years data from 2000 to 2020. The focus of this study is to approach the issue of the environment of legitimacy that leads to sustained market returns.Design/methodology/approachPanel cointegration tests of Kao and Pedroni are applied, and the Dynamic Panel Vector Autoregressive (PVAR) model is used to determine the estimates.FindingsADF P-Values of both Kao and Pedroni tests show that the panels are cointegrated; the statistical significance of the results of the Kao and Pedroni panel cointegration test confirms cointegration among the variables. After determining the most appropriate lag, the analysis is done using PVAR. The results indicate that institutional quality, policy uncertainty, and GDP positively affect stock market return. Meanwhile, government actions and inflation negatively affect stock market returns. On the other hand, stock market return positively affects institutional quality, government action, policy uncertainty, and GDP. While stock market return negatively affects inflation.Research limitations/implicationsThe sample is taken only from a limited number of South Asian countries, and the period is also limited to 21 years.Practical implicationsBased on our research findings, we have identified several policy implications recommended to enhance and sustain the performance of stock markets.Originality/valueThis paper uses a unique analytical tool, which gives a better insight into the problem. The value of this work lies in its findings, which also have practical implications and theoretical significance.

Decent work, financial inclusion, and economic growth: Analysis under the SDG 8

We investigate the potential influence of decent work and financial inclusion on economic growth, aligning our analysis with the United Nations Sustainable Development Goal 8 (SDG 8). Analyzing a diverse set of 26 low-income, 20 middle-income, and 15 high-income countries, we utilize a dynamic fixed-effect panel data approach and apply the Generalized Method of Moments from 2010 to 2021. The findings highlight that favorable conditions for decent work and increased access to digital financial services contribute positively to economic growth. The synergistic effect of these SDG 8 components is notably pronounced in high-income countries, with greater potential for elevated economic growth rates. Conversely, no discernible impact is observed on the economic growth of low-income countries. The results for middle-income countries present nuanced coefficient outcomes, offering a more intricate interpretation of their economic dynamics. An extensive impulse-response analysis, conducted using a Panel Vector Autoregression model, demonstrates distinct impacts of shocks related to decent work and financial inclusion across various income categories of countries. Our findings pass various robustness checks, affirming their reliability and suggesting significant policy implications for recognizing the crucial role of decent work and financial inclusion in fostering economic growth.

An empirical analysis of causal nexus between higher education and economic growth in BRICS countries

This study examines the connection between higher education and economic growth in the BRICS countries. This study used the gross enrolment ratio (GER) to gauge the extent of higher education in the BRICS countries, while gross domestic product (GDP) was used to estimate economic growth. The study includes both time series and panel data analysis for all BRICS countries. The vector error correction model (VECM) and the vector autoregressive model (VAR) were applied to time series data to explain the causal relationship between GDP and GER. Panel data were analyzed using the panel vector auto-regression (PVAR) model. The time series analysis revealed that there is both a bi-directional and a unidirectional causal link between economic development and higher education. However, panel data analysis revealed that the causal influence of higher education was more pronounced than economic growth.

Macroeconomic dynamics and Panel VAR -Analysis in Developing Countries

This study applied the Panel Vector Autoregressive Model (PVAR) to examine the macroeconomic dynamics impact of variables. PVAR examined the results by taking the Data from seven Developing countries; Indonesia, Mexico, Chile, Denmark, China, Israel, and India. Based on this study, five variables are included to run the Panel VAR i.e., Inflation, short-term interest rate, RGDP, Official reserves, and exchange rate Volatility. Developing countries faced lots of difficulties thus examining which variable reflects a high effect to improve countries’ infrastructure. Therefore, this study performs the Panel VAR, Variance decomposition, and Impulse response thus stating that inflation and short-term interest have a high impact on developing countries, whereas, RGDP and official reserves have a low impact, resultantly the volatility rate fluctuating highly when little change in inflation and interest rate occurs. Additionally, monetary policy, foreign transactions, and economic growth are also included in this paper. Because inflation relates to the monetary policy, short-term interest rate relates to investors’ opportunity, RGDP relates the economic growth, and official reserves reflect foreign transactions or support.

Terms‐of‐trade effects of productivity shocks in developing economies

AbstractThis paper studies the terms‐of‐trade effects of economy‐specific shocks to productivity in developing economies using a panel vector autoregression model with interactive fixed effects and the “max‐share” approach. We find that the terms of trade after unanticipated changes in economy‐specific productivity mostly exhibit insignificant dynamics in the developing economies. However, we present evidence that they can significantly deteriorate in a particular set of developing countries, namely the ones with a high score on technological deepening and upgrading.

Does geopolitical uncertainty matter for the diffusion of clean energy?

This study revisits the determinants of renewable energy (RE) deployment, with an emphasis on the role of recent adverse geopolitical events. We implement a panel vector autoregressive model where different techniques and tools are available to trace the dynamic interdependence between RE and its main drivers. The impulse response analysis indicates that geopolitical uncertainty exerts a positive impact on the clean energy sector in advanced countries. However, geopolitical shocks do not appear to stimulate the transition toward alternative sources of energy in emerging and developing economies. The forecast-error variance decomposition confirms the importance of adverse geopolitical shocks in explaining the dissemination of renewables in advanced economies. Policymakers in less developed countries are urged to continue supporting RE initiatives and infrastructure, which are key to successful mitigation strategies.

Substitutes or Complements? Evidence of the Relationship Between Air and Cruise Tourism

This article estimates the relationship between cruise and air tourism and their impact on economic growth in the Caribbean. To this end, we assemble a monthly data set of cruise and air tourist arrivals and a satellite-derived economic wealth proxy for 21 Caribbean islands over the period 2000‐2013. The estimates from a panel vector autoregression model not only demonstrate that cruise and air tourism are substitutes but it is the former for which this effect appears to be more permanent. Further, our analysis shows that while air tourism has a more immediate economic impact, cruise tourism has more long-term economic benefits. Our findings highlight the need for implementing policies to increase the value of cruise tourism to local economies as well as the effective management of revenue generation.

Artificial intelligence and religious freedom: divergent paths converging on economic expansion

In the current era, the symbiotic relationship between artificial intelligence, religious freedom, and economic growth remains a topic of paramount importance. This study embarks on an in-depth exploration of these dynamics across 26 countries from 2000 to 2021. Utilizing the panel vector auto-regression model, the findings underscore a significant positive correlation between artificial intelligence, religious freedom, and economic growth. The forecast-error variance decomposition highlights the growing importance of both variables. It projects their increasing relevance in future economic landscapes. Furthermore, the study reaffirms the indomitable role of traditional growth drivers, specifically labor and capital inputs. The Toda-Yamamoto Granger causality analysis bolsters the validity and reliability of these findings. In summary, this research strengthens established economic paradigms and reveals new interactions, offering important implications for academics, policymakers, and stakeholders.

ESG reputational risk and market valuation: Evidence from the European banking industry

This study examines the potential bidirectional linkage between reputational risk exposure associated with Environmental, Social and Governance (ESG) factors and market valuation in the banking sector. We build a monthly panel dataset for 19 European listed banks from 2012 to 2020. We employ a Bayesian Panel Vector Autoregressive model to examine the dynamics between the two variables of interest. The findings show an inverse bidirectional causality between ESG reputational risk exposure and banks’ market valuation and suggests that the impact of ESG reputational risk shocks on market valuation is more significant for high-exposed banks. Our results are consistent with the stakeholder and slack resources theories and highlight the importance of ESG factors in influencing the banks’ market valuation. Moreover, the study demonstrates how prior financial performances impact the ESG reputational exposure. These insights provide guidance on how banks can manage their ESG risks to enhance brand identity and market value.

Bank digital transformation and liquidity mismatch: Evidence from China

To investigate the bidirectional relation between bank digital transformation and liquidity mismatch, this paper employ the empirical methodology of panel Granger causality tests to examine the relationship between digital transformation indicators and liquidity mismatch index, employing panel vector autoregression models, as well as a panel structural vector autoregression model, employing the data of 181 Chinese commercial banks in the period before COVID-19. We find a bidirectional causality between digital transformation and the liquidity mismatch index. An increase in the degree of digital transformation in commercial banks significantly reduces the bank liquidity mismatch index; furthermore, a decrease in the degree of the bank liquidity mismatch index results in an increase in bank digital transformation. The digital transformation enhances a bank's susceptibility to liquidity stress, although a bank suffering from higher liquidity mismatch prefers to promote digital transformation. Furthermore, we find that cognitive digital transformation is the primary driving factor of the bidirectional causality relationship and the relationship between digital transformation and the liquidity mismatch index is heterogeneous for banks with various ownership types.

Spatial coupling coordination and interactive response between green finance and green total factor productivity: geographical analysis based on Chinese provinces, 2010-2020.

Based on the panel data of 30 provinces in China from 2010 to 2020, this study uses the coupling coordination degree model (CCDM), spatial autocorrelation analysis, and the panel vector autoregression (PVAR) model to quantitatively examine the dynamic coordination and interactive response of green finance (GF) and green total factor productivity (GTFP). The results show that the coupling coordination degree (CCD) between GF and GTFP during the study period shows an overall increasing trend and significant regional differentiation and remains at the medium level, indicating large room for improvement. The CCDs of provinces fluctuate between the low and high types. The distribution gradually shifts from a "high in the south and low in the north" pattern to a "high in the east and low in the west" configuration. The spatial difference of coupling development continues to expand and presents a significant spatial positive correlation. In addition, GF and GTFP in China exhibit significant self-reinforcing characteristics, but the reinforcing effect gradually decreases. There is only a unidirectional interaction relationship between GF and GTFP, which is GF actively fosters the advancement of GTFP, while the influence of GTFP on GF appears less pronounced. This research provides comprehensive insights into the dynamic interaction between GF and GTFP.

Coupling coordination and driving mechanisms of water resources carrying capacity under the dynamic interaction of the water-social-economic-ecological environment system

The water resources carrying capacity (WRCC) is a complex and comprehensive system that is jointly influenced by water resources, society, the economy and the ecological environment. Previous WRCC studies have primarily focused on estimating the overall level of regional WRCC. Few studies have explored the interactions among the various elements in the WRCC system and their influence on the WRCC evolution. Therefore, the purpose of this paper is, on the one hand, to explore the dynamic interactive relationships within the WRCC system from the perspectives of water resources, society, the economy and the ecological environment using a coupling coordination degree model and a panel vector autoregressive (PVAR) model, and on the other hand, to determine the evolutionary driving mechanism of the WRCC using the geographically and temporally weighted regression (GTWR) model to improve the regional WRCC. Taking 21 cities in Guangdong Province as an example, the results show that (1) the coupling coordination degree among the four WRCC subsystems in Guangdong Province shows an overall upward trend from 2009 to 2020, and the coordination between water resources utilization and other subsystems needs to be further strengthened. (2) The economic subsystem is the core of the WRCC system with reinforcing effects on both water resources and social subsystems but significant inhibitory effects on the ecological environment subsystem. Notably, the development of the ecological environment plays a crucial role in promoting social and economic development. (3) From 2009 to 2020, the development of the WRCC in Guangdong Province is initially driven by social and economic development, followed by economic development and ecological environmental protection, and then mainly by ecological environmental protection, which gradually becomes the primary driving force. This study provides a new entry point for studying the regional WRCC and formulating targeted measures for enhancing the regional WRCC.

ANALYSIS OF DETERMINANTS OF FOOD INFLATION IN ASEAN COUNTRIES: A PVAR MODEL APPROACH

Purpose: This research aims to analyze the impact of macroeconomic factors, namely the money supply, exchange rate, economic growth, food supply, interest rates, imports, exports, and wheat prices on food inflation. Theoretical reference: The data used in this study are secondary data obtained from the World Bank's website, covering the years 2002-2021. Method: The methods employed for data analysis include Panel Vector Autoregression (PVAR) and Panel Vector Error Correction Model (PVECM). Results and Conclusion: The results and conclusions The research indicate that economic factors such as the money supply, exchange rate, interest rates, imports, and wheat prices can influence the level of food inflation, but their effects may vary in the long run and short run. Implications of research: Implications of research availability of food supply and economic growth and can play a role in controlling food inflation. value: The novelty of this study is that economic factors can affect the inflation rate and affect the long-term and short-term and can control food inflation.

Impact of the COVID-19 Market Turmoil on Investor Behavior: A Panel VAR Study of Bank Stocks in Borsa Istanbul

Assuming that investors can be foreign or local, do high-frequency trading (HFT) or not, and submit orders through a bank-owned or non-bank-owned broker, we associated trades to various investors. Then, building a panel vector autoregressive model, we analyzed the dynamic relation of these investors with returns and among each other before and during the COVID-19 market crash. Results show that investor groups have influence on each other. Their net purchases also interact with returns. Moreover, during the turmoil caused by the pandemic, except foreign investors not involved in HFT, the response of any investor group (retail/institutional, domestic investors doing HFT and those not doing HFT, and foreign investors doing HFT) significantly altered. This shows that the interrelation among investor groups is dynamic and sensitive to market conditions.

Green technology innovation, trade deficit and carbon emission transfer in agriculture under the new “dual circulation” development pattern of China

Under the new development pattern of domestic and international “dual circulation” in China, studying the relationship among green technology innovation, agricultural trade deficit (ATD) and carbon emission transfer embodied in agricultural trade (ATCT) becomes crucial for achieving coordinated and unified high-quality economic development and efficient environmental protection. Based on this, the paper uses environmental input–output model, “two-stage” decoupling and recoupling model, panel vector auto-regressive model, spatial econometric model and “rolling” Grey-BP neural network prediction model to explore present status of ATD, ATCT and their relationship, driving role of green technology innovation, as well as whether that relationship will be improving in the future. The results reveal the following: (1) China's ATD and ATCT show an increasing trend year by year, and their inter-provincial differences are gradually significant over time. The overall relationship between ATD and ATCT does not display ideally, which makes it necessary to quickly transform that relationship into reasonable and ideal state. (2) Green technology innovation has significant positive “U-shaped” and inverted “U-shaped” local effects on ATD and ATCT respectively, with a reasonable range of ATD reduction while ATCT increase valuing from 0 to 0.290, and has significant positive “U-shaped” spatial spillover effect on both without any reasonable range. The short-term effect of green technology innovation on coordinated promotion between ATD and ATCT is superior to the long-term one. (3) Green technology innovation has a significant regional heterogeneity. Regional differences must be taken into consideration seriously, and the measures adapted to local conditions are crucial. The role of fundamental and progressive green technology innovation has heterogeneity, which means that targeted development and utilization of green technology products are important. (4) Regardless of the time horizon, the relationship between ATD and ATCT will improve, which can provide a key step for coordinated development of economy and environment in agricultural trade. Therefore, in order for China to implement and promote further opening-up, reduce agricultural trade dependence, ensure food security and important agricultural product supply, as well as achieve the unity of high-quality economic development and ecological efficient protection, it is necessary to reasonably reverse trade deficit and increase carbon emission transfer in agriculture.

The dynamic relationship among green logistics, technological innovation and green economy: Evidence from China

Under the background of “double carbon”, exploring the growth path of green logistics and enhancing the driving force of technological innovation is the urgent need of our country to comply with the green transformation of its economy and realize high-quality economic growth. Taking the panel data of 30 Chinese provinces from 2010 to 2020 as the sample, the green logistics index evaluation system is constructed based on the driver-press-state-impact-response (DPSIR) theoretical framework, and the green economic efficiency of each province in the sample period is measured by using the non-expected output Super- Slacks-based measure (SBM) model, and by constructing the panel vector autoregressive (PVAR) model including technological innovation is used to systematically elaborate the dynamic influence paths among the three. The study found that: China's green economy, technological innovation, and green logistics all have their own mechanisms for growth, which will gradually diminish over time. In the near and long term, green logistics will promote technological innovations and the evolution of a green economy, but there is a lag in the long-term benefits of green logistics on technological progress. In the short term, technological innovation does not lend support to the growth of a green economy, but over time, the impact of technological innovation on the growth of that economy will shift from negative to positive. This shows that improving technological innovation capability is an important path for green logistics to promote green economic efficiency. The findings of the study provide a basis for decision making to achieve the emission reduction target and improve the efficiency of the green economy.

Exploring the relationship between regional tourism development and land use efficiency: A case study of Guangxi Zhuang Autonomous Region, China.

The utilization efficiency of land resources is an essential embodiment of economic development, social development, and ecological development and is a critical core to measure how to maximize the efficiency of land resources under limited conditions. The land is an important content and essential carrier of the research of tourism development level. This paper selects Panel Data from 2010 to 2019 to research the Guangxi regional tourism development. The entropy weight method and stochastic frontier production function (SFA) model were used to evaluate the development level of urban-rural tourism and the utilization efficiency of land resources in Guangxi. This paper uses the Panel Vector Autoregression (PVAR) model to analyze the internal relationship between urban-rural tourism development. The results show that: (1) Guangxi has a good level of tourism development and a high land use efficiency. (2) There is a reciprocal causation relationship between the regional tourism development level and land use efficiency in Guangxi, with significant levels of 0.005 and 0.034 respectively, indicating high credibility. This indicates that there is a mutual promotion and interaction between the two, which rely on and drive each other, promoting the joint sustainability of tourism development and land use efficiency. (3) . The tourism development level is greatly influenced by itself, with impact values all above 0.99. At the same time, land use also has a significant self-impact, with impact values all above 0.87. Their internal optimization system is solid and endogenous impetus is robust, which can drive their development. Establishing an effective strategy for developing and protecting land use is beneficial to promote the long-term effectiveness of sustainable tourism development, enhancing high-quality development of the tourism economy and improving people's living standards and quality.

Aquaculture production and diversification: What causes what?

Aquaculture, an important source of food supply, is expected to be the main sector to satisfy growing seafood demand in the future. Being the most diversified food product in the world, aquaculture diversification can add resilience to global food security, satisfy consumer preferences, and promote price stability. Understanding the relationships between aquaculture production and diversification and their causations is crucial for developing effective strategies to support long-term sustainability and resilience of aquaculture development. This study investigates the direction of causal relationships between aquaculture production and diversification using a panel vector autoregression (PVAR) model with three decades of aquaculture production data and the Effective Number of Species (ENS) values by country. Diversification is measured in terms of within-group diversity (ENSα) and between-group diversity (ENSβ); this approach provides deeper insights into diversification strategies. The model results show that between-group diversification is more conducive to production expansion globally, especially in the Americas and Asia, and the positive effects are long lasting. Within-group diversification also induces more production in Europe and, to a lesser extent, in Asia. Therefore, policies and market incentives that promote diversification across different species groups in the Americas and Asia, and diversification within the same species group in Europe and Asia, are potential strategies to expand aquaculture production. Other findings include production leading within-group diversification in Asia and Europe, but not in the Americas and Africa. A possible explanation is that production expansion would accumulate experience, develop scope economies and emerging technologies, and build up capacities. These factors generate spillover effects that facilitate species diversification, considering that Asia and Europe have a longer history of aquaculture development compared to the Americas and Africa. However, no significant relationship is found indicating production leading between-group diversification.