Panel Vector Autoregressive Model Research Articles

Intra-Africa Agricultural Trade, Governance Quality and Agricultural Total Factor Productivity: Evidence from a Panel Vector Autoregressive Model

Intra-Africa Agricultural Trade, Governance Quality and Agricultural Total Factor Productivity: Evidence from a Panel Vector Autoregressive Model

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.

Spatiotemporal patterns, effects, and the interactive driving mechanisms of urban sustainability based on the eco-efficiency framework: Evidence from Chinese prefecture-level cities

Eco-efficiency is a vital tool for evaluating and advancing regional sustainable development. This study presents an urban eco-efficiency (UEE) framework that deserves widespread application. By using the Super Slacks-Based Measure model, the research calculates the UEE of 284 prefecture-level cities in China from 2005 to 2022. The study employs the Standard Deviational Ellipse to illustrate the spatiotemporal pattern of UEE in China and examines its effects using non-parametric kernel density estimation, the coefficient of variation, and both global and local Moran's I. The integration of UEE with urban development patterns, which is not immediately clear, is achieved through the entropy-based TOPSIS model. Additionally, the Malmquist index, the Two-way Fixed Effects (TWFE) model, and the Panel Vector Autoregression (PVAR) model reveal the dynamic changes and interactive driving mechanisms of UEE in China. The results indicate that: (1) UEE in China displays a consistent “core-periphery” pattern, exhibiting a notable hierarchical impact from regional, scale, and administrative perspectives. The complexity of UEE's spatiotemporal variations has increased due to the impact of COVID-19. (2) The UEE of Chinese cities shows a significant ‘club’ effect, with efficiency thresholds for high-high and low-low aggregations set at 0.8. (3) Only 25% of Chinese cities have achieved optimal UEE levels. Notably, cities in the west contribute most to national UEE growth, while midland cities contribute the least. The decreasing trend in UEE growth underscores the urgent need for a shift in China's urban development model. (4) UEE in China benefits from a resilient self-reinforcing mechanism, with urban development and economic growth significantly aiding UEE growth. Despite a non-significant negative impact from government intervention, the inhibitory effect of an intensive industrial structure on UEE turns into a promotional effect, especially following COVID-19.

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.

Energy poverty and shadow economy: evidence from Africa

PurposeEnergy poverty is a global phenomenon, but its prevalence is enormous in most African countries, with a potential impact on quality of life. This study aims to investigate the impact of energy poverty on the shadow economy.Design/methodology/approachThe study uses panel data from 45 countries in Africa over a period of 1996–2018. Using panel cointegrating regression and panel vector auto-regression model in the generalized method of moments technique.FindingsThis study provides that energy poverty deepens the size of the shadow economy in Africa. It also documents that there is a bidirectional causality between shadow economy and energy poverty. Therefore, the two variables can predict each other.Practical implicationsThe study suggests that lack of access to clean and modern energy services contributes to the depth of the shadow economy in Africa. African authorities are advised to strengthen rural and urban electrification initiatives by providing adequate energy infrastructure so as to reduce the level of energy poverty in the region. To ensure energy sustainability delivery, the study proposes that the creation of national and local capacities would be the most effective manner to guarantee energy accessibility and affordability. Also, priorities should be given to the local capital mobilization and energy subsidies for the energy poor. Energy literacy may also contribute to the sustainability and the usage of modern energy sources in Africa.Originality/valuePrevious studies reveal that income inequality contributes to the large size of shadow economy in developing economies. However, none of these studies analyzed the role of energy poverty and its implications for underground economic operations. Inadequate access to modern energy sources is likely to deepen the prevalence of informality in developing nations. Based on this, this study provides fresh evidence on the implications of energy deprivation on the shadow economy in Africa using a heterogeneous panel econometric framework. The study contributes to the literature by advocating that the provision of affordable modern energy sources for rural and urban settlements, and the creation of good energy infrastructure for the firms in the formal economy would not only improve the quality of life but also important to discourage underground economic operations in developing economies.

중국의 토지 공급 정책이 부동산 시장에 미치는 영향

Purpose - This study aims to explore the relationship between housing and land prices, with a specific emphasis on the impact of government policies on these factors such as land supply quantity and the ratio of residential land to total land supplied. The goal is to identify the most effective government intervention strategies for controlling both housing and land prices. Design/methodology/approach - Data from 70 primary and medium-sized cities in China spanning from 2003 to 2017 are utilized in this research. The analysis employs a panel vector autoregressive (PVAR) model, with a primary focus on examining the relationships among housing prices, land prices, and government intervention policies. Findings - Housing and land prices are influenced by various factors. Through impulse response analysis and variance decomposition, it is observed that both housing and land prices are predominantly influenced by their internal dynamics, with comparatively weaker effects attributed to policy interventions. Research implications or Originality - By investigating the impact of government policies on housing and land prices, This study establishes a foundation for effective price control measures. Our study advocates for a comprehensive examination of China's land supply mechanism to enhance understanding of the pathways through which government policies influence the markets.

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.