Vector Autoregression Research Articles

Impact of bank-affiliation on liquidity seeking of foreign mutual funds during adverse shocks: Evidence from China

Using daily shareholding data and event-based measures of composite shocks, we investigate the liquidity-seeking behavior of foreign mutual funds in China that are affiliated with commercial banks, comparing it with the behavior of unaffiliated foreign mutual funds. Fixed effect and panel structural VAR models both reveal differential responses between affiliated and unaffiliated foreign mutual funds. While both types of mutual funds seek liquidity by withdrawing from illiquid, volatile and growth-oriented stocks facing negative shocks, unaffiliated mutual funds exhibit a higher degree of liquidity seeking, consistent with the results of bank-affiliated funds providing liquidity stability. This finding is robust to the selection of shock periods and measurements. The evidence shed new lights on the liquidity management of open-end foreign mutual funds in emerging markets.

Energy factors affecting environmental pollution for sustainable development goals: The case of India

This study investigates the nexus between electricity consumption, fossil fuel dependency, renewable energy adoption, population growth, trade activities, economic growth, and environmental pollution in India. The primary objective is to understand how these factors interrelate and influence each other, focusing on their implications for sustainable development. The study used data from the World Bank from 2000 to 2023; the methodology adopted includes vector autoregression modeling, Granger causality tests, cointegration analysis, impulse response functions, and variance decomposition. These econometric techniques were selected due to their ability to capture dynamic relationships, determine causality, and identify long-term equilibrium among the variables. The findings reveal that economic growth significantly increases electricity consumption and fossil fuel usage, leading to higher carbon dioxide emissions. On the other hand, renewable energy adoption reduces environmental pollution. The study also highlights the complex interplay between population growth, urbanization, and trade activities in shaping India's energy demand and environmental outcomes. The implications of these findings are critical for India's sustainable development. The results suggest that while economic growth is essential, it must be balanced with sustainable energy practices to mitigate environmental pollution. The findings emphasize the need for policy interventions that promote renewable energy, enhance energy efficiency, and enforce environmental regulations. Recommendations include accelerating renewable energy adoption, implementing stringent energy efficiency standards, and developing integrated policies that simultaneously address the economic, energy, and environmental dimensions. These actions will help India achieve a sustainable balance between economic growth and environmental protection, ensuring a healthier future for its population.

How do Carbon Emissions Spillovers Reshape Metal Market Dynamics? Time– Frequency Insights on Precious and Non-precious Metals

Global disruptions, such as health crises and geopolitical tensions, significantly impact both climate and commodity dynamics. This study, well-grounded on environmental finance theory, input–output modelling, socio-transition philosophy and behavioural finance perspectives, explores the evolving interactions between carbon emissions (CE) and metal markets during COVID-19 pandemic and Russia–Ukraine war (RUW). The study uses time-varying parameter vector autoregressive model (TVP-VAR) technique to evaluate time and frequency varying connectedness between CE and metal markets from 3 January 2020 to 28 June 2024. During the COVID-19 pandemic, initial connectedness among selected markets peaked at 85%, averaging 46%, highlighting a significant CE–metal nexus that necessitates strategic responses. In the RUW period, connectedness averaged 47.82%. CE influence metal markets primarily in the short term. Wavelet coherence analysis reveals that palladium and platinum are highly sensitive to CE over the long term, while gold and silver may serve as effective diversifiers and hedges against carbon-related risks in metal investments. The study is relevant for investors in the metal sector with environmental considerations. JEL Classifications: G110, Q430, L720

Influencing Factors and Formation Mechanism of Carbon Emission Rights Prices in Shanghai, China

With the intensification of global climate change issues and increasingly stringent carbon emission controls, carbon emissions trading has become a crucial market-based mechanism for reducing carbon emissions. A deeper understanding of the factors influencing carbon emission rights prices and their formation mechanisms can enhance market transparency and effectiveness and promote the healthy development of the carbon trading market. This paper identifies the main factors affecting carbon emission rights prices and explores their formation mechanisms from both the internal and external perspectives of enterprises. Using a vector autoregression (VAR) model, it quantifies the impacts of energy prices, the climate, and technology levels on carbon trading prices in Shanghai, China. The results show that, in the short term, enterprise technology levels and energy prices significantly impact carbon emission rights prices, while the climate becomes more critical in the medium to long term. These findings propose specific policy recommendations for improving the carbon emissions trading market, with a focus on these three factors.

Food-fuel nexus beyond mean-variance: New evidence from a quantile approach

This paper investigates the dynamic relationship between crude oil, ethanol, and corn markets across various quantiles of return distributions, as well as at higher statistical moments. Using a quantile vector autoregression model and data from 2007 to 2022, we find that the cross-market linkages are quantile dependent, with the strongest connections observed in the tails of the distribution. A shock to the oil market significantly impacts ethanol and corn returns under extreme bearish and bullish conditions. Positive shocks to the corn market reduce ethanol returns when the ethanol market is highly bullish, but this effect becomes positive in the left tail of the distribution. We also identify significant co-movement in higher statistical moments between these markets. Extreme excess kurtosis in the food-fuel nexus is more likely to occur with high financial market uncertainty, a bullish stock market, contracting industrial production, and a strong US dollar. In addition to these variables, credit spreads, futures market liquidity, futures term structure, and hedging pressure also influence kurtosis in individual markets within the nexus.

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.

Oil Price Uncertainty and Consumer Sentiment in Advanced Economies

How does oil price uncertainty affect consumer sentiment in advanced economies? Is the response of consumer sentiment to exogenous positive and negative oil price shocks symmetric or asymmetric? Motivated to answer these questions, this paper provides a comprehensive examination of the effects of real oil price shocks on consumer sentiment in the G7 economies in the context of two classes of empirical models. With the application of a bivariate structural GARCH-in-Mean VAR model we find that (in general) oil price uncertainty has a negative and statistically significant effect on consumer sentiment in the G7 countries. Moreover, using a test of symmetry, we find that the relationship between oil prices and consumer sentiment is in general asymmetric. JEL Classification: C32, D12, Q43, O57

How to enhance the resilience of domestic tourism?

Enhancing domestic tourism resilience (DTR) is crucial to achieving high-quality development of the tourism industry. However, owing to the vulnerability of the tourism industry during the COVID-19 pandemic, the enhancement pathways and associated mechanisms of DTR need to be further investigated. Therefore, this study measures the DTR of China's 31 provinces by creatively combining the simple harmonic motion and regime-switching vector autoregressive models and explores its characteristics to lay the foundation for an empirical analysis. Furthermore, the study empirically analyzes the enhancement pathways and associated mechanisms of DTR using a spatial econometric model. First, this study found that tourism agglomeration, specialization, and diversification determine the characteristics of dynamic response and spatial distributions of DTR. Second, from the perspective of spatial effects, DTR in one region positively impacts that in adjacent regions, and tourism agglomeration in a specific region positively impacts DTR in adjacent regions. Third, in terms of mechanisms, tourism agglomeration enhances DTR by optimizing industrial structure and improving production efficiency, whereas tourism specialization and diversification enhance DTR by improving production efficiency and optimizing industrial structure, respectively. These findings provide information for the enhancement of DTR and achievement of high-quality development of the tourism industry.

Impact of fiscal policy shocks on health outcomes in Sub-Saharan Africa: A panel structural VAR approach

This study investigates the impact of fiscal policy shocks on health outcomes in Sub-Saharan Africa (SSA). It is motivated by health systems’ increasing reliance on public funding despite growing macroeconomic uncertainties. This study adopts a regional analysis, sampling five countries with the highest per capita public health expenditure from the Central, Eastern, Southern, and Western African regions. The study employs the panel structural vector autoregression technique and expresses health outcomes as a function of fiscal policy and private health expenditure shocks. The results suggest that fiscal policy shock significantly and positively affects health outcomes in Central and Western Africa. However, health outcomes are resilient to fiscal policy shock in Eastern and Southern regions and to private health expenditure shock in all regions. The study indicates that achieving sustainable improvement in health outcomes in SSA would require policies designed to expand health financing options beyond the traditional public and private funding arrangements.

Spatial Price Transmission and Dynamic Volatility Spillovers in the Global Grain Markets: A TVP-VAR-Connectedness Approach.

The global food market's escalating volatility has led to a complex network of uncertainty and risk transmission across different grain markets. This study utilizes the Time-Varying Parameter Vector Autoregression (TVP-VAR)-Connectedness approach to analyze the price transmission and volatility dynamics of key grains, including wheat, maize, rice, barley, peanut, soybean, and soybean meal, and their dynamic spillover directions, intensity, and network. By integrating the TVP-VAR-Connectedness model, this research captures the time-varying variability and interconnected nature of global grain price movements. The main findings reveal significant spillover effects, particularly in corn prices, with prices of soybean dominating other grains while prices of peanut and corn experience higher external spillover effects from other grains. The conclusions drawn underscore the imperative for policymakers to consider a holistic perspective of all types of grains when addressing global food security, with this study providing valuable insights for risk management in the grain sector at both global level and country level.

Can financial development help alleviate the resource curse? Evidence from 286 cities in China

The resource curse adversely affects the sustainability of economic growth. A sound financial system helps improve capital rational allocation, which may be an effective path for alleviating the resource curse. Thus, from both theoretical and empirical perspectives, using Chinese 286 cities panel data from 2003 to 2018, this study examines the effects and potential mechanisms of financial development on the resource curse. For this purpose, a two–sector endogenous growth model is first constructed for the theoretical analysis, while panel vector autoregression, Hansen's threshold regression, and dynamic panel models are used for further empirical investigation. The results show that the resource curse is widespread across Chinese cities. Financial development can alleviate such a curse when it passes the threshold value; however, most cities have not passed this value yet. Foreign direct investment, education, and technology have a positive mediating effect on financial development and its impact on the resource curse.

Extreme downside risk connectedness between green energy and stock markets

This study addresses the need to understand the transmission of tail risk across financial markets, especially in the context of green energy and stock markets. Utilizing the Time-Varying Parameter Vector Autoregressive (TVP-VAR) methodology combined with the Conditional Autoregressive Value-at-Risk (CAViaR) framework, the model analyzes data from September 30, 2013, to October 11, 2023. This study focuses on five major stock indices (S&P500, CSI300, Nikkei225, STOXX50, and FTSE100), West Texas Intermediate (WTI) crude oil, and three sectors within green energy markets (Green bond, Global Clean Energy, and Renewable Energy and Clean Technology), highlighting the significant role of these sectors in risk propagation. The model can capture dynamic changes and asymmetries in financial returns, thus providing a precise estimation of extreme downside risks. Key findings show that three stock indices (S&P500, STOXX50, and FTSE100) and one green energy sector (Renewable Energy and Clean Technology sectors) are the predominant sources of risk, with significant connectedness around events, such as the COVID-19 pandemic. These insights are crucial for developing effective risk-management strategies and supporting the transition to a sustainable energy sector. This study concludes that understanding these risk dynamics is essential for strategic planning and market stability.

Intrinsic dynamic shapes responses to external stimulation in the human brain.

Sensory stimulation of the brain reverberates in its recurrent neuronal networks. However, current computational models of brain activity do not separate immediate sensory responses from intrinsic recurrent dynamics. We apply a vector-autoregressive model with external input (VARX), combining the concepts of "functional connectivity" and "encoding models", to intracranial recordings in humans. We find that the recurrent connectivity during rest is largely unaltered during movie watching. The intrinsic recurrent dynamic enhances and prolongs the neural responses to scene cuts, eye movements, and sounds. Failing to account for these exogenous inputs, leads to spurious connections in the intrinsic "connectivity". The model shows that an external stimulus can reduce intrinsic noise. It also shows that sensory areas have mostly outward, whereas higher-order brain areas mostly incoming connections. We conclude that the response to an external audiovisual stimulus can largely be attributed to the intrinsic dynamic of the brain, already observed during rest.

Generative dynamical models for classification of rsfMRI data

ABSTRACT The growing availability of large-scale neuroimaging datasets and user-friendly machine learning tools has led to a recent surge in studies that use fMRI data to predict psychological or behavioral variables. Many such studies classify fMRI data on the basis of static features, but fewer try to leverage brain dynamics for classification. Here, we pilot a generative, dynamical approach for classifying resting-state fMRI (rsfMRI) data. By fitting separate hidden Markov models to the classes in our training data and assigning class labels to test data based on their likelihood under those models, we are able to take advantage of dynamical patterns in the data without confronting the statistical limitations of some other dynamical approaches. Moreover, we demonstrate that hidden Markov models are able to successfully perform within-subject classification on the MyConnectome dataset solely on the basis of transition probabilities among their hidden states. On the other hand, individual Human Connectome Project subjects cannot be identified on the basis of hidden state transition probabilities alone—although a vector autoregressive model does achieve high performance. These results demonstrate a dynamical classification approach for rsfMRI data that shows promising performance, particularly for within-subject classification, and has the potential to afford greater interpretability than other approaches.

The influence mechanism of internet finance on residents’ online consumption

This study investigates the impact of Internet finance (IF)11Internet finance (IF): Traditional financial institutions and Internet enterprises use Internet technology and information communication technology to realize the new financial business model of financial financing, payment, investment and information intermediary services.on residents’ online consumption using annual data from 2013 to 2022 in China. To examine the impact of IF on overall consumption, survival, development, and enjoyment, four vector autoregressive models are constructed. This study employs comprehensive methods, including parameter estimation, impulse response function analysis, and variance decomposition. Findings reveal that the development of IF has significantly stimulated residents’ online consumption, with the increase in developmental expenditure being the most significant, followed by the increase in entertainment expenditure.

Cognitive Model Based on The Dl Random Forest Method for Profi t Forecasting and Fuzzy Algorithm for Assessing Company Sustainability in Conditions of Uncertainty

The article is devoted to the problem of supporting management decision-making on choosing a strategic partner whose activities would be effective and sustainable. During the study, based on the results of the work of enterprises in the domestic confectionery industry, a dataset was generated, which was subsequently used for the deep learning model DL-model "Random Forest" in order to calculate the predicted values of the net profi t of enterprises in the industry. Assessing companies for the purpose of selecting a strategic partner, using models such as: deep learning model "Random Forest" (DL Random Forest), VaR, Z-Altman, Hurwitz matrix, Fuzzy algorithm in modern conditions has great practical significance. The relevance of the study lies in the fact that in conditions of increasing market uncertainty, approaches to ensuring the sustainable development of an organization based on AI systems are increasingly being used. The scientific novelty lies in the fact that the study used a set of models that made it possible to assess the actual sustainability of companies and, based on the calculated forecast values of net profi t, as well as business efficiency indicators ROE and ROS, to make a decision on the choice of potential business partners. During the study the following were developed: a VaR model, which made it possible to obtain an assessment of fi nancial risk, a Z-Altman model for assessing the risk of bankruptcy of an enterprise. In addition, based on the calculated parameters of the regression equation, a Hurwitz matrix was formed, which made it possible to draw a conclusion about the sustainability of each enterprise as a system. The use of the Fuzzy algorithm made it possible to obtain a decision on choosing a partner enterprise.

A Structural Vector Autoregression Exploration of South Africa’s Monetary and Macroprudential Policy Interactions

Interactions between monetary and macroprudential policy are crucial in safeguarding price and financial stability. This study investigates the macroeconomic and financial impacts of monetary and macroprudential policy interactions in South Africa, a leading African economy in developing macroprudential frameworks. The existing literature largely focuses on the effectiveness of these policies independently, leaving a gap in understanding how their interaction affects their overall efficacy. Employing a Structural Vector Autoregression (SVAR) model and utilizing data from 1980 to 2023, this study uniquely incorporates the financial cycle to represent financial developments. The results reveal significant effects of both policies on key variables such as output, the financial cycle, and the price level. Specifically, policy contractions reduce output and the financial cycle but increase the price level, illustrating the ‘price puzzle’. This study further identifies an endogenous response between the two policies: monetary policy reacts by rising to reduce price levels following a macroprudential shock, while macroprudential policy rises to stimulate financial activity after a monetary shock. These findings underscore the importance of using both policies in conjunction but in opposite directions to balance their effects and achieve price and financial stability. This study suggests that an optimal combination of monetary and macroprudential policies is critical for maintaining macroeconomic equilibrium.

Poverty Inflation, FDI Consumption, and Economic Growth in Indonesia in the Vector Autoregressive Model Analysis

The difference in the results from previous studies on the impact of inflation on poverty, coupled with an increase in the money supply from direct investment, inspired this research. This study aimed to ascertain how poverty, inflation, and foreign investment affect consumption spending in Indonesia based on 1997-2021 time series data. This study used Vector Autoregression estimation, utilizing data sources from the World Bank. The data will be processed with econometric models. From the study’s findings, it may be inferred that overall consumption affects inflation, which can be seen from the Granger causality test that shows that this variable has a one-way causal relationship. The results of the same test also show that FDI has an effect on total consumption and conversely consumption has an effect on FDI. However, the poverty variable does not affect total consumption and vice versa, because according to the causality test, the variable obtains an insignificant probability value. However, the VECM results explain that poverty has an effect on total consumption, and that the effect of consumption on inflation is due to the larger t-statistic value and has a positive relationship. This implies that the rate of inflation will increase the lower the level of consumption. However, the Impulse Reason test shows that poverty has a negative trend, as does the FDI variable. Keywords: poverty, inflation, FDI, consumption, economic growth, Indonesia

The Causality between Economic Growth, Public Debt and Government Spending of Lao PDR

This paper aims to test the causality between economic growth, public debt and government spending. This study used the time series data from 1995-2021 and implied with a VAR model and Granger causality test. In addition, the unit root, cointegration, Lagrange multiplier and Jarque-Bera test statistics was used. The empirical analysis by using VAR model found that public debt and economic growth have no effect on public spending. But government spending affects public debt in the same direction with a statistical significance level of 0.1 meaning that only government spending affects public debt, while public debt does not affect government spending. While government spending and public debt affect economic growth in the same direction with a statistical significance level of 0.01. For the Granger Causality Test, there is a unidirectional relationship means that only government spending and public debt affect economic growth, economic growth does not affect government spending and public debt.

The Triple Bottom Line: Prioritizing CSR, Human Capital, and GHG Reduction for Sustainable Economic Growth in Indonesia

Our study, which covered the years 2008 to 2022, aimed to create “autoregressive vectors” to investigate the interrelationships between corporate social responsibility (CSR), greenhouse gas net (GHG), human capital (HCPT), and human resources in Indonesia. We used data from the World Bank, OECD, and Bank Republic Indonesia to conduct an analysis of the correlation between these variables. Our results showed that there are significant relationships between corporate governance self-assessment (CGS), GHG, and HCPT in Indonesia. The positive correlation between GHG and CGS suggests that as GHG emissions increase, the level of corporate governance self-assessment also increases. This finding implies that companies that prioritize reducing GHG emissions tend to have better corporate governance practices. Moreover, investing in reducing GHG emissions can have a positive impact on human capital development. However, the negative correlation between HCPT and CGS implies that an increase in human capital may result in a decrease in the level of corporate governance self-assessment. This finding highlights the importance of balancing investments in human capital with maintaining good corporate governance practices to ensure sustainable economic growth. Furthermore, promoting corporate social responsibility can enhance companies’ reputation and increase their brand value, which can positively impact their CGS. Therefore, it is essential for companies to prioritize CSR practices, invest in human capital development, and reduce GHG emissions to ensure sustainable economic growth and maintain good corporate governance practices in Indonesia. Keywords: corporate social responsibility, greenhouse gas net, human capital