Panel Vector Autoregressive Model Research Articles

Assessing the Causal Impact of Culture on Socio-economic Variables in European Countries

Despite the growing attention to the impact of culture on socio-economic indicators, there is a lack of causality evidence. Scholars have noted that cultural values or norms, as well as, a vibrant cultural environment can have a positive impact on GDP growth, labour productivity, the attraction of human capital and the improvement of human well-being. However, the issue of endogeneity is not thoroughly debated. Not only can culture impact economic development, but socio-economic environment also affects the cultural indicators. Therefore, it is essential to identify the causal effects of culture. This article aims to assess whether culture has a causal impact on socio-economic variables. The Granger procedure is utilised to evaluate causal relationships, describing the cultural environment with a comprehensive set of 45 cultural indicators across 30 European countries. This enables the comparison of the relationship between culture and socio-economic variables using the same method across different countries. Additionally, cultural indices are constructed. The research is extended by employing a panel vector autoregression (VAR) model. The key finding of this article demonstrates a causal impact of culture on socio-economic indicators, confirmed by both the Granger procedure and panel VAR estimations. JEL Code: Z1

How climate volatility influences human rights

We explore how climate volatility influences human rights practices. We argue that climate volatility intensifies competition for material resources, increases citizens’ grievances against the state, and leads to more frequent repression as governments seek to respond to environmentally induced protests and local conflicts. We situate this argument in the existing literature’s emphasis on three mechanisms linking climate shocks and repression: resource shocks, mobilization shocks, and migration shocks. We assert that the climate volatility-human rights relationship is strongest in the context of civil conflict as rebel groups and governments place a higher priority on survival and material well-being rather than governance and public goods provision. Using a pooled time series dataset of 194 countries from 1946 to 2017, we estimate a panel vector autoregression (PVAR) model. Our analyses show that for countries experiencing armed conflicts, increased precipitation volatility significantly reduces human rights protections for multiple years. Indicators for the three mechanisms also show indirect relationships to repression through the precipitation variable, consistent with our theory. We illustrate our findings with case vignettes from Benin and Mali. Our results demonstrate that climate volatility alters states’ strategies for addressing internal security threats.

Interactive effects of intangible cultural heritage and tourism development: a study based on the data panel PVAR model and coupled coordination model

Exploring the coordination and analysis of intangible cultural heritage (ICH) and tourism development is a key issue in advancing cultural heritage and conservation in Chinese society. This paper employs panel data from 30 Chinese provinces from 2013 to 2022, utilizing the coupled coordination degree model and the panel vector autoregression (PVAR) model. In terms of provinces and regions, the data investigates the development relationship between intangible cultural heritage and tourism. The results of the study are: 1. The degree of coupling coordination between the two has shown an increasing trend over the ten-year sample data period. In 2021 and 2022, COVID-19 will have a stronger negative impact on the coupling degree. 2. The eastern coastal provinces of China, as well as the municipal areas, have a higher level of coupling coordination. Inland provinces, such as those in central and western China, exhibit relatively lower levels of coordination. The PVAR model data reveals a positive correlation between the interactions generated by intangible cultural heritage. The development of intangible cultural heritage has a particular impact on tourism. Intangible cultural heritage, in turn, depends on its own development. We should strengthen the development of intangible cultural heritage in the future. The study's findings provide theoretical references for the coordinated development of intangible cultural heritage and tourism in China.

Effects of Natural Disasters on West Africa Sahel’s Economy: The Case of Burkina Faso, Mali and Niger

The Sahel region is facing the adverse effects of the natural disasters due to the drought and floods which are the most frequent events. These effects can be spread and affected the all the Sahel States economy. Therefore, this study attempts to analyze the effects of these natural disasters on the macroeconomic variables such as production, consumption, investment and inflation in the three (3) the northern central Sahel States. To do so, the panel vector autoregressive model (PVAR) is run on the World Bank and the International Emergency Disasters Database (EM-DAT) data from 1990 to 2021. The results revealed that the occurrence of drought shock generates significantly a negative impact on the GDP per capita at 5% level across the northern central Sahel countries (Burkina Faso, Mali, and Niger). Also, the lagged of the macroeconomic variables such as production, investment and inflation have respectively a significant and positive impact on consumption at 5% level, production at 5% level and consumption at 10% level. Furthermore, the results show an asymmetry of natural disaster shocks at the scale of the northern central Sahel States but these shocks are symmetrical in pairs. As a result, the appropriate policies must make to absorb these shocks and avoid any hysteresis effect.

Costs of Adjustment, Portfolio Separation, and the Dynamic Behavior of Bank Loans and Deposits – Revised

AbstractWe revise the solutions of the dynamic optimization model of Elyasiani, Kopecky, and Van Hoose (1995), and show that the optimal policy function for the case of adjustment costs and no portfolio separation is a vector autoregressive model of order 1, and not of order 2. In addition, the empirical approach of Elyasiani, Kopecky, and Van Hoose (1995) is incorrect. The optimal policy functions for the no portfolio separation case have to be estimated with a panel vector autoregression model and not with a simultaneous equation approach. We also describe how the portfolio separation hypothesis can be tested empirically based on the correct policy functions.

Research on the mechanism and path of the coupling of digital technology and environmental regulation to promote urban green efficiency

Digital technology, renowned for its embeddedness and integration, revolutionizes governance ideas, institutions, and models. Based on the panel data of 285 cities in China from 2007 to 2021, a panel vector autoregressive model (PVaR) is built to reveal the interaction mechanism between digital technology and environmental regulation. The fixed-effects panel model explored the green impact of the coupling of digital technology and environmental regulation, considering the mediating role of industrial upgrading, factor allocation, and public participation. The results show that digital technology and environmental regulation promote each other. The coupling of digital technology and environmental regulation enhances urban green efficiency, especially in economically advanced and eastern regions. Industrial upgrading, factor allocation, and public participation mediate this effect. Therefore, government departments should build ecological civilization by improving environmental management decision-making, promoting the deep integration of digital technology and environmental regulation, and exploring a new path of urban green development.

Identifying the Key Drivers in Energy Technology Fields: The Role of Spillovers and Public Policies

This study investigates the salient roles of knowledge spillover and environmental policies on clean technology innovation. Employing a panel vector autoregressive model (PVAR) and connectedness network analysis with a comprehensive longitudinal dataset comprising 100 million patent documents across 26 countries, the study identifies clean technology fields that are most efficient in driving innovation and subsequently quantifies the spillover effects for each field. The impact of public environmental policies and regulations on clean technological innovations is also examined in depth. The results reveal that clean innovation is a complex and nuanced system, with significant knowledge spillovers occurring within and across energy and non-energy-related clean technology fields. The results also show that environmental policies significantly influence clean innovation, with technology support and adoption support policies having the most substantial impact. Furthermore, the results reveal that the impact of market-based policies on clean innovation is weaker than that of non-market-based policies, which is an important consideration for policymakers. The findings hold significance for policymakers in addressing sustainability goals and their implications.

Dynamic Interactive Effects of Technological Innovation, Transportation Industry Development, and CO2 Emissions

This paper aims to clarify the intricate relationships between technological innovation, transportation industry development, and CO2 emissions to facilitate a positive synergy among technology, the economy, and climate, advancing the fulfillment of the ‘double carbon’ goal. Utilizing panel data from 30 provinces in China from 2005 to 2020, we employ the panel vector autoregressive model using a generalized method of moments to empirically examine the dynamic interactive effects between these participants. The findings reveal that the transportation industry significantly promoted the inhibitory impact of technological innovation on CO2 emissions. However, such reductions cannot counterbalance the rise in emissions from the transportation industry. Moreover, its effects varied significantly across regions. Specifically, transportation industry development within eastern China contributed to a shift in the local carbon emission effects from positive to negative under the positive influence of technological innovation. In the northeast, the transportation industry enhanced the inhibitory effect of technological innovation on CO2 emissions. In contrast, across the western region, industrial development in transportation intensified the role of technological innovation in promoting CO2 emissions. Furthermore, this work found that CO2 emissions notably diminished the CO2 reduction performance of technological innovation in the eastern part and enhanced this performance in the northeastern region. These findings further revealed the complex interplay between technological innovation, the transportation industry, and CO2 emissions. They offer insights for policymakers to tailor region-specific technologies to bolster the ‘dual carbon’ goal and sustainable transportation development strategies, thereby achieving CO2 reduction.

Quantifying the spillover effects of U.S. economic policy uncertainty on emerging market economies using GMM-PVAR model

This paper quantifies the spillover effects of economic policy uncertainty (EPU) in the United States on emerging market economies (EMEs). Using a generalized method of moments (GMM) estimation of a panel vector autoregression (PVAR) model on a dataset­ of 39 EMEs from 2005 to 2019, we find that increased U.S. EPU significantly raises the consumer price index (CPI) and negatively impacts the real GDP of these economies. Additionally, heightened U.S. EPU leads to a depreciation of emerging market currencies and a reduction in short-term interest rates. We employ a news-based EPU index developed by Baker et al. (2016) and conduct robustness checks using forward orthogonal transformation, an alternative EPU index, and by addressing the potential endogeneity of the oil price uncertainty (OPU) index. Our findings highlight the adverse effects of U.S. economic policy uncertainty on key macroeconomic variables in emerging markets, underscoring the importance of stable economic policies and robust institutions to mitigate these impacts.

Re-examining international spillovers: An Asian perspective

This study investigates the transmission and the business cycle implications of Chinese export shocks to other Asian economies. Based on a panel vector autoregression (PVAR) model, we provide evidence that a positive export shock originating from China simultaneously stimulates aggregate demands for both China and other Asian economies. The PVAR model takes into account three main sources of potential interactions among ten Asian economies. Simulations from a two-country DSGE model featuring endogenous trade links suggest that positive trade spillover is essential to explain the macroeconomic comovement pattern. Our findings further indicate the importance of the Chinese economy to business cycle synchronization.

Model Building for Prediction of Valuation of Growth Stocks

It has been a great challenge for investors to evaluate growth stocks because these types of stocks are especially hard to forecast because of their inconsistencies and uncertainty. This paper establishes not only regression models, which include a regular Multivariable Linear Regression (MVLR) and a Fixed Effects (FE) Regression Model but also the application of a Panel Vector Autoregression (PVAR) Model on a dataset aiming to predict the Price-to-Sales Ratio (P/S) with several variables. Excel, Python, and Rstudio environments are used in the process of coding and model building. More specifically, Excel is used to conduct the MVLR model, Python is used for the FE model, and Rstudio is used for the PVAR model. By comparing different models of each type, the most optimized model was determined using a training set. After applying predictions with all types of models using another testing set, the best model is identified to be the best approach to the problem. Ultimately, all three models are compared. PVAR model and FE model are proved to be the more reliable ones out of the three models, because the R-squared for MVLR is 0.1859, lower than that of the FE model, 0.524, and the Mean Absolute Error (MAE) of for PVAR Model and FE model are respectfully 53.2139 and 55.1529. This project serves as a good starting point for future analysis and prediction of financial metrics like P/S ratio and is able to have great significance as it discovers some key points for the prediction of P/S value like its connection with time and entity despite the fact that the models themselves aren’t the best fit mainly caused by the limitations of data and variable access.

Future pathways for green hydrogen: Analyzing the nexus of renewable energy consumption and hydrogen development in Chinese cities

Hydrogen energy offers a promising pathway for achieving zero-carbon urban energy consumption. This study quantitatively analyzes the patterns of hydrogen energy development and its linked to electricity consumption in China's urban areas. It investigates the spatial relationships between hydrogen energy expansion, the potential for green hydrogen production, and overall energy use. Utilizing a coupling coordination degree (CCD) model and a panel vector autoregressive (PVAR) model, the research explores the dynamic interactions between urban hydrogen energy development and the transition to renewable energy sources. In line with Chinese government policy, the study also conducts a scenario analysis of green hydrogen production by 2030, proposing a consumption strategy that balances environmental sustainability with economic growth. The key conclusions are as follows: (1) There is a spatial mismatch between the locations where hydrogen energy is being developed and where the potential for green hydrogen production is greatest. Cities with higher electricity consumption demonstrate stronger synergies in the coordinated development of hydrogen and renewable energy. (2) The development of hydrogen energy significantly accelerates the transition to renewable energy consumption in urban areas, with this impact being more substantial than that of the utilization capacity alone. (3) Hydrogen production in the Plan Scenario is significantly higher than in the Pessimistic Scenario, underscoring the critical importance of implementing renewable hydrogen projects in provinces with high production potential. Future trends in green hydrogen consumption at the provincial level indicate that addressing the challenges of efficient inter-provincial transportation of hydrogen energy remains essential.

Impact of governance quality, population and economic growth on greenhouse gas emissions: An analysis based on a panel VAR model

Unlike the previous literature on climate change, this study employed the panel vector autoregressive model to examine the impact of population growth, governance quality, and economic growth on global warming. Moreover, we used the impulse response function tool, which was developed in the same context, to better understand the reaction of the main variables of interest, per capita greenhouse gas (GHG) emissions, aftershocks on population change, governance quality, and economic growth variables. Finally, the analysis was completed by the variance decomposition of all variables. These analyses were conducted for the 33 most polluting countries in terms of GHG from 1996 to 2019. The results show that both population growth and economic growth have a positive and significant influence on climate change and can strongly explain the gradual rise in Earth's temperature. Our empirical results show that there is a negative and significant relationship between the quality of governance and GHG emissions. The results show that there is a bidirectional causality between GHG emissions, population and economic growth. Based on the findings, this study proposes several policy recommendations.

Size isn't everything: Understanding the relationship between police workforce and crime problems

If and how policing affects crime has long been studied. On the relationship between police force size and crime, different authors come to different conclusions. This study examines the relationship between police resourcing, including workforce size, structure and stability over time using data for 42 police forces in the UK over a 13-year period.We construct two novel panel datasets. The first comprises measures of police workforce Size, Structure and Stability. The second provides measures of both crime frequency and crime severity. Issues of endogeneity make the modelling of the police-crime association complicated. Consequently, we analyse the data using a panel vector autoregression (PVAR) model which is capable of forecasting a temporal sequence of the interdependencies between police-crime relationships.Changes in total police personnel play an important role in reducing both crime frequency and severity, but the findings are more nuanced than this. Results highlight that the structure and stability of police organisations are important although these impacts are not always the same for crime volume and crime severity. We find that increases in frontline (non-sworn) support staff are associated with reductions in crime, while turnover rates of police staff are associated with increases in crime. In contrast, changes to the number of sworn police officers do not appear to be a good predictor of crime volume.The findings suggest that investment in frontline support staff and the development of strategies to retain skills and knowledge by reducing staff turnover may be efficient approaches for Police Forces to maximise the impact on crime of their workforce in resource-pressed policing settings. While previous research has found that police force size has a limited effect on crime, our findings indicate that more nuanced measurements of police resourcing are necessary to understand how police impact upon crime risk. The idea of police forces using basic officer-to-population ratios to make staffing decisions appears outdated and over-simplistic.

A Dynamic Impact Evaluation of the High-Quality Development of China’s Construction Industry Using the Panel Vector Autoregressive Model

A Dynamic Impact Evaluation of the High-Quality Development of China’s Construction Industry Using the Panel Vector Autoregressive Model

Research on the synergy of the water–energy–food composite system in the Beijing–Tianjin–Hebei region

ABSTRACT Water, energy, and food are interdependent and mutually restrictive, and changes in any one resource may have an impact on the others. In order to promote the sustainable utilization of resources, it is important to study the synergy of the water–energy–food composite system (WEFCS). Taking the Beijing–Tianjin–Hebei region as an example, this study adopts the composite system synergy degree model to quantitatively measure the synergy degree of WEFCS from 2011 to 2021, then constructs an evolutionary model to identify the interactions between the subsystems, and finally analyzes the interactions dynamically by using the panel vector autoregression model. The results show that the WEFCS synergy level in the Beijing–Tianjin–Hebei region is generally basic and shows low stability during the study period. Regional subsystems are more dependent than collaborative or competitive. Moreover, the regional water subsystem has a short-term positive impact on the energy subsystem, the energy subsystem has a short-term negative impact on the food subsystem, and the food subsystem has a delayed and complex influence on the water and energy subsystems, with the water subsystem being particularly affected. This study provides a decision-making basis for policymakers to optimize resource allocation.

House prices and credit as transmission channels from monetary policy to inequality: Evidence from OECD countries

Using a panel vector autoregressive model, this paper assesses the impact of monetary policy on the Gini coefficient of disposable income for 30 OECD countries between 1995Q1 and 2019Q4. We assume house prices and household credit as transmission channels, representing households’ financial balance. To capture the effect of the financial crisis, the analysis further distinguishes the period that started in 2008Q4. We find that a contractionary monetary policy increases the Gini coefficient in a moderate way. For the entire period, house prices are an effective transmission channel, a positive shock decreasing inequality, while credit is not statistically significant. Following the crisis credit stands out as the transmission mechanism, a positive credit shock reducing the Gini coefficient, while house prices lose relevance. These results are robust to different model specifications.

The mechanism and path of pollution reduction and carbon reduction affecting high quality economic development - taking the Yangtze River Delta urban agglomeration as an example

In the wake of the COVID19 pandemic, what is crucial for the continued promotion of ecological civilization construction and sustainable green development in China is to achieve the dynamic and balanced development between pollution reduction, carbon emission reduction, and economic growth. Therefore, the indexes of CO2 emissions, pollution reduction intensity, and high-quality economic development are quantified in this study by using the panel data related to 26 cities in the Yangtze River Delta urban agglomeration(YRDUA) from 2010 to 2020. Furthermore, kernel density analysis, GIS spatial analysis, and other methods are employed to demonstrate the spatial and temporal patterns of pollution reduction intensity, CO2 emission levels, and the characteristics of high-quality economic development in the YRDUA. In order to reveal the dynamic relationship between pollution reduction, carbon emission reduction, and high-quality economic development, a panel vector autoregressive model (PAVR) was established and the system generalized method of moments was used to estimate the model coefficients. Additionally, Granger causality tests, impulse response analysis, and variance decomposition were conducted. There is a fluctuating upward trend shown by the overall level of CO2 emissions in the YRDUA, with significant spatial nonuniformity exhibited in carbon emissions. Despite the self-promotion mechanism playing a more significant role in the high-quality economic development in the YRDUA and its three regions, the impact of pollutant and carbon emissions on high-quality economic development is quite limited. A significant promoting effect is exhibited in those high and moderately high-quality economic development regions, while nonlinear effects are observed in those high and moderately high-quality economic development regions. As for the interactive relationships, the three factors are correlated with each other to a more significant extent in those high and moderately high-quality economic development regions, which contributes to mutual influence and causal relationship. However, there is no effective interactive mechanism observed among the three factors in the whole area and low-quality economic development regions, with high-quality economic development playing a major role in environmental pollution and carbon emission reduction.

Research on the coupling coordination and spatial spillover effect of industrial development and environmental protection

ABSTRACT Analysing the coordination and spatial spillover effects between industrial development and environmental protection is crucial for regional sustainable development. This paper examines the spatial and temporal evolution characteristics of the coupling coordination level between industrial development and environmental protection, as well as the evolution of the decoupling type within this system. under the evolution of the coupling coordination mode and decoupling model. Additionally, it analyzes the spatial spillover effects using the spatial panel vector autoregression model. The main conclusions are as follows: First, the coordination level between industrial development and environmental protection is increasing over time in each province. Second, the decoupling status of industrial development and environmental protection shows distinct stages, progressing towards strong decoupling over time. Third, regional industrial development has a positive spillover effect on both industrial development and environmental protection in neighbouring regions, whereas regional environmental protection has a negative spillover effect on industrial development but a positive spillover effect on environmental protection in neighbouring regions. This paper suggests that provinces should focus on the synchronisation industrial development and environmental protection and implement differentiated strategies based on their specific conditions. Additionally, joint management and governance should be pursued between neighbouring provinces.

Research on the influence of the digital economy on the development of China’s small household appliance industry and enhancement countermeasures

Purpose The implementation of the digital economy has had a considerable influence on the manufacturing industry, and this paper aims to address the important issues of how to capture the opportunities presented by digital innovation and promote the transformation and upgrading of the manufacturing industry, as well as the improvement of quality and efficiency. Design/methodology/approach Using panel data from 30 Chinese provinces and cities between 2010 and 2021, this study establishes the panel vector autoregression (PVAR) model and uses impulse response function analysis to evaluate the influence of the digital economy on the high-quality transformation and upgrading of China's small home appliance industry across five dimensions under the digital economy. Findings The development of digital infrastructure has not demonstrated a noteworthy capacity for advancing the transformation and upgrading of the small home appliance industry. Furthermore, digital industrialization has exerted a minimal restraining influence on this process. Nevertheless, digital governance has consistently exhibited a substantial impact on facilitating the transformation and upgrading of the small home appliance industry. While both industrial digitization and digital innovation hold significant potential for promoting the transformation and upgrading of the small home appliance industry, their sustainability remains limited. Practical implications The organization should logically join independent innovation and open innovation, construct an industrial ecosystem for the profound convergence of the digital economy and compact household appliances, use digital-wise science and technology to empower the establishment of brand effects, strengthen the portrayal of the digital standard framework for the intelligent compact household appliance industry, advance the development of a public stage for computerized administrations in the compact household appliance industry and develop a strategy ecosystem for computerized assets in the compact household appliance industry. Originality/value This study offers systematic evidence of the relationship between the digital economy and the development of the small home appliance industry. The results of this research contribute to the literature on the impact of the digital economy on the manufacturing sector and provide a logical explanation for the transformation and upgrading of the small home appliance industry within the context of the digital economy.