Panel Vector Autoregressive Model Research Articles

Study on the relationship between informatization and marine eco-efficiency in China–taking 11 coastal provinces as examples

This article focuses on 11 coastal provinces in China and selects relevant data from 2008 to 2021. The improved entropy method is used to calculate the level of informationization development in each province. The Malmquist index is used to calculate the marine ecological efficiency of each province. The panel vector autoregression (PVAR) model is established to study the dynamic relationship between informationization and marine ecological efficiency. The results show that: (1) There is a long-term equilibrium relationship between informationization and marine ecological efficiency, and informationization is a Granger cause of marine ecological efficiency at the 5% significance level. (2) Overall, informationization has a promoting effect on marine ecological efficiency, but with a certain lag; the improvement of marine ecological efficiency also benefits the development of informationization. (3) There is a bidirectional relationship between informationization and marine ecological efficiency, and informationization has become the main influencing factor of marine ecological efficiency.

The impact of housing prices and land financing on economic growth: Evidence from Chinese 277 cities at the prefecture level and above.

With the rapid progress of urbanization in China, the real estate industry, characterized by a long industrial chain, has become a pillar industry for economic development. Therefore, we inspect the nexus between land finance, housing prices, and economic growth. For this purpose, we use the panel data of 277 cities at the prefecture level or above in China from 2011 to 2019, and empirically examine it by using the Panel Vector Auto Regression (PVAR) model. The results show that there is a causal relationship between housing prices and economic growth. Housing prices promote economic growth in the short term and inhibit it in the long term. Both economic growth and housing prices have a significant impact on land finance. The economic growth show a significantly positive impact, while housing prices promote land finance in the short term with a long-term trend from positive to negative. This is the first study that tries to probe the relationship between urban housing prices, land finance, and economic growth by considering 277 prefecture-level and above cities in China. To promote the stable development of the regional economy, local governments need to overcome their dependence on the housing market and land finance and promote the healthy development of the housing market.

Dynamic Analysis of the Growth of Building Area and the Immigrant Population in Shanghai based on the PVAR Model

Since the reform and opening up more than 30 years ago, Shanghai has witnessed rapid development of economic and continuous acceleration of urbanization and continuous growth of building area. However, the number of migrants has decreased over the same period, which is inconsistent with previous studies that show that population affects land demand and floor space. In this paper, panel data of each district of Shanghai from 2014 to 2018 are selected and the panel vector autoregression model (PVAR model) is constructed to analyze the dynamic relationship between the building area growth of each district of Shanghai, the immigrant population and the economic growth. The results show that : (a)There is no long‐term equilibrium relationship between the growth of Shanghai's floor area and the migration of foreign population. (b) The construction area growth in Shanghai can affect the regional GDP growth to a certain extent. (c) The migration of migrants into Shanghai has a certain effect on the economic growth of Shanghai.

Sustainable Impact of Green Building on the Eco-Economic Efficiency of the Construction Industry: Evidence from China

Addressing the global warming challenge, the carbon emissions reduction potential of green building (GB) is garnering increasing attention. However, the extent to which GB can impact the eco-economic efficiency (EE) of the construction industry remains unclear. To fill this gap, based on panel data for 30 regions of China from 2003 to 2018, in this study, a comprehensive analysis of the EE and sustainable impact of GB is conducted using a super slacks-based measure (SBM-DEA), panel vector autoregression (PVAR) and threshold models. The findings reflect that only about 10% of provinces have achieved EE effectiveness, with pure technical effectiveness being a significant driving force behind this. The average comprehensive, pure technical, and scale EE show a 62.23%, 46.42%, and 31.91% improvement potential regarding the efficiency frontier. The EE in the Eastern Region is relatively high, while the Western Region surpassed the Central Region in regards to EE in the areas of scale and pure technical efficiency. EE is significantly reduced in the current period (year) when it is subject to the positive impact of a standard deviation from the existing economic level. This impact was the strongest in period 1, then gradually disappeared until period 6, aligning with the Kuznets curve’s theoretical assumption. GB awareness negatively impacted the current period, but is expected to gradually show a positive effect after period 2. The urbanization, green building awareness, and green building coverage make a very small contribution to the EE, accounting for 8.6%, 1.6%, and 9%, respectively, with early EE and economic level identified as the primary variables affecting the current EE. The impact of GB on EE exhibits a threshold effect, with the ecological effect of GB significant only in cities with economic levels higher than 11.063. This research contributes to the existing knowledge of the eco-economic mechanism of GB and provides insights for government policies, promoting the sustainable development of the GB market.

Safety Monitoring Method for the Uplift Pressure of Concrete Dams Based on Optimized Spatiotemporal Clustering and the Bayesian Panel Vector Autoregressive Model

To establish a safety monitoring method for the uplift pressure of concrete dams, spatiotemporal information from monitoring data is needed. In the present study, the method of ordering points to identify the clustering structure is employed to spatially cluster the uplift pressure measuring points at different locations on the dam; three distance indexes and two clustering evaluation indexes are used to realize clustering optimization and select the optimal clustering results. The Bayesian panel vector autoregressive model is used to establish the uplift stress safety monitoring model for each category of monitoring point. For a nonstationary sequence, the difference method is selected to ensure that the sequence is stable, and the prediction is carried out according to the presence or absence of exogenous variables. The result is that the addition of exogenous variables increases the accuracy of the model’s forecast. Engineering examples show that the uplift pressure measurement points on the dam are divided into seven categories, and classification is based mainly on location and influencing factors. The multiple correlation coefficients of the training set and test set data of the BPVAR model are more than 0.80, and the prediction error of the validation set is lower than that of the Back Propagation neural network, XGBoost algorithm, and Support Vector Machines. The research in this paper provides some reference for seepage monitoring of concrete dams.

Dynamic interaction between permanent and temporary employment across manufacturing labor markets in the Mexican states: A structural panel VAR approach

México’s implementation of several labor market reforms aimed to stimulate the flexibilization of labor relationships have brought long-standing concerns among scholars and governmental authorities about a displacement process of permanent by temporary jobs. In this regard, this manuscript aims to respond whether the dynamic interaction between permanent and temporary employment across Mexican states describe a substitution or complementary relationship. By means of implementing a structural panel vector autoregressive model, our estimation results demonstrate that a combination of both types of interaction, substitutive and complementary ones, prevail across manufacturing labor markets at state level. Moreover, a marked heterogeneity among estimated dynamic responses suggests that incentivizing permanent employment would induce stronger substitution effects on its temporary counterpart than, for example, substitution of permanent job positions arising when incentivizing temporary employment.

Interaction Effect of Export Trade, Foreign Direct Investment and Technological Independent Innovation in China

China has made significant progress in the field of clean energy and sustainable development, with its photovoltaic industry technology leading globally. What has been the trajectory of China’s successful technological catch-up over the past two decades? Is China’s experience sustainable? To clarify the interaction logic between export trade (ET), foreign direct investment (FDI), and technological independent innovation (TII) in China, this paper uses panel data from 31 provinces and cities in the Chinese Mainland between 2000 and 2022. A panel vector autoregression (PVAR) model is constructed from a dynamic endogenous perspective to verify the interaction and regional heterogeneity among the three. The results are as follows: (1) The unified analytical framework shows a significant bidirectional “positive feedback effect” between ET and TII. However, FDI inhibits TII to a certain extent. Furthermore, the correlation between ET and FDI is weak. (2) The impact of ET on TII is most pronounced in the western region, while the central region sees the highest contribution rate of FDI to TII. The self-evolution effect of TII is most evident in the eastern region. This study provides suggestions for the government to develop an adaptation policy for local industrial technology conditions and establish a National Sustainable Systems of Innovation (NSSI) with multiple comparative advantages, and serves as a reference for establishing a “Chinese model”.

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.

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.