Vector Autoregression Research Articles

Spatial and temporal diffusion of property prices and sales across British regions

Purpose The purpose of this study is to reveal the dynamics of house prices and sales in spatial and temporal dimensions across British regions. Design/methodology/approach This paper incorporates two empirical approaches to describe the behaviour of property prices across British regions. The models are applied to two different data sets. The first empirical approach is to apply the price diffusion model proposed by Holly et al. (2011) to the UK house price index data set. The second empirical approach is to apply a bivariate global vector autoregression model without a time trend to house prices and transaction volumes retrieved from the nationwide building society. Findings Identifying shocks to London house prices in the GVAR model, based on the generalized impulse response functions framework, I find some heterogeneity in responses to house price changes; for example, South East England responds stronger than the remaining provincial regions. The main pattern detected in responses and characteristic for each region is the fairly rapid fading of the shock. The spatial-temporal diffusion model demonstrates the presence of a ripple effect: a shock emanating from London is dispersed contemporaneously and spatially to other regions, affecting prices in nondominant regions with a delay. Originality/value The main contribution of this work is the betterment in understanding how house price changes move across regions and time within a UK context.

Empirical analysis of the development relationship between Shanghai shipping industry and primary industry based on VAR model

This study employs the Vector Autoregression (VAR) model to conduct an in-depth empirical analysis of the dynamic relationship between Shanghai’s shipping industry and the development of the primary sector from 2002 to 2022. The findings reveal that, as a globally significant maritime hub, the development of Shanghai’s shipping industry has a notable positive impact on the growth of the primary sector, particularly in agriculture and fisheries. Through ADF unit root tests, cointegration tests, and Granger causality tests, a stable long-term relationship was identified between shipping industry indicators, such as container throughput and total waterborne cargo transport volume, and the output value of the primary sector. Impulse response analysis and variance decomposition further reveal the critical roles and mechanisms through which the shipping industry influences the development of the primary sector. Based on these insights, the paper proposes policy recommendations aimed at strengthening the integrated development of shipping and the primary sector, promoting technological innovation in the shipping industry, optimizing the policy environment, enhancing environmental protection measures, and bolstering international cooperation. These strategies are intended to foster synergistic development between the two sectors and drive sustainable economic growth.

Empirical analysis of the relationship between economic growth and environmental pollution based on VAR model: A case study of Hubei Province China

In this paper, the industrial three wastes from 1992 to 2018 in Hubei Province were collected as environmental indicators, and the per capita GDP was used as economic indicators. The study of Kuznets curve showed that industrial wastewater gradually decreased with economic growth, showing an inverted N shape; Both industrial emissions and solid waste will increase with the growth of per capita GDP, industrial waste gas shows a linear relationship, and industrial solids show an inverted U relationship. Then through the VAR model research, it is found that the response of industrial “three wastes” to per capita GDP growth in Hubei Province is positive in the short term. The long-term response of economic development in Hubei province to industrial waste gas and industrial solid waste discharge is positive and decreasing, but the short-term response to industrial wastewater discharge is negative. The variance decomposition analysis reveals that the primary environmental determinants influencing the current economic growth trajectory of Hubei Province are industrial wastewater, industrial waste gas, and industrial solid waste. Fluctuations in economic growth within Hubei Province demonstrate a stronger correlation with variations in emissions of industrial wastewater and industrial solid waste, while exerting comparatively less influence on changes in emissions of industrial waste gas.

Cyclical dynamics and co-movement of business, credit, and investment cycles: empirical evidence from India

AbstractThe paper aimed to investigate the cyclical dynamics of the business, credit, and investment sectors in India. This was achieved by utilizing annual data from 1980 to 2021 and investigating the impact of domestic and global financial cycles on the business cycle. The cycles were derived using the Hodrick–Prescott filter, and structural vector autoregression (SVAR) and Granger causality tests were employed to establish the dynamic interactions among these cycles. The results of the study revealed a clear divergence between domestic and global financial cycles. Additionally, the SVAR analysis confirmed the presence of a long-run relationship between business, investment, and credit cycles. Notably, the findings suggest that credit cycles can provide valuable insights to manage business cycles in India. Finally, robustness checks were conducted to confirm the reliability of SVAR findings.

Tokenomics in the Metaverse: understanding the lead–lag effect among emerging crypto tokens

The convergence of blockchain and immersive technologies has resulted in the popularity of Metaverse platforms and their cryptocurrencies, known as Metaverse tokens. There has been little research into tokenomics in these emerging tokens. Building upon the information dissemination theory, this research examines the role of trading volume in the returns of these tokens. An empirical study was conducted using the trading volumes and returns of 197 Metaverse tokens over 12 months to derive the latent grouping structure with spectral clustering and to determine the relationships between daily returns of different token clusters through augmented vector autoregression. The results show that trading volume is a strong predictor of lead–lag patterns, which supports the speed of adjustment hypothesis. This is the first large-scale study that documented the lead–lag effect among Metaverse tokens. Unlike previous studies that focus on market capitalization, our findings suggest that trade volume contains vital information concerning cross-correlation patterns.

Prediction and Allocation of Stocks, Bonds, and REITs in the US Market

AbstractThis study employs dynamic model averaging and selection of Vector Autoregressive and Time-Varying Parameters Vector Autoregressive models to forecast out-of-sample monthly returns of US stocks, bonds, and Real Estate Investment Trusts (REITs) indexes from October 2006 to December 2021. The models were recursively estimated using 17 additional predictors chosen by a genetic algorithm applied to an initial list of 155 predictors. These forecasts were then used to dynamically choose portfolios formed by these assets and the riskless asset proxied by the 3-month US treasury bills. Although we did not find any predictability in the stock market, positive results were obtained for REITs and especially for bonds. The Bayesian-based approaches applied to just the returns of the three risky assets resulted in portfolios that remarkably outperform the portfolios based on the historical means and covariances and the equally weighted portfolio in terms of certainty equivalent return, Sharpe ratio, Sortino ratio and even Conditional Value-at-Risk at 5%. This study points out that Constant Relative Risk Averse investors should use Bayesian-based approaches to forecast and choose the investment portfolios, focusing their attention on different types of assets.

DETERMINANTS AND IMPACT OF PUBLIC EXPENDITURE IN ODISHA

The theoretical framework and rationale of the study assess the most important fiscal parameter of the state of Odisha i.e., Expenditure parameter. As we know, aggregate of the expenditure of the state consists of Revenue expenditure and Capital expenditure, among which the largest share is contributed by Revenue expenditure. The paper highlights the responsiveness of expenditure to the automatic change in NSDP Elasticity approach, position of odisha among the 18 major non-special category states in terms of infrastructural development, bank credit, factory employment, level of urbanisation etc. and also examine the causative relationship between SDP and different expenditure variables in Odisha. The study is based on secondary source of data for the State from different government publications, Budget documents, Finance Accounts etc. The paper used different methodologies for analysis, which includes simple statistical techniques such as, ratio, percentage, and also Regression Analysis to estimate growth. In this regard models like, semi log, multiple regression, correlation, VAR models are used to analyse the time series data from 1980-81 to 2014-15 on revenue and capital expenditure of state finances . Apart from that to show the response of revenue expenditure to the automatic change in NSDP, Elasticity approach is utilized. The major results of the study are that higher growth in total expenditure in period t-1 leads to higher growth of SDP in period t. The same result holds only in case of growth of Total Non-Plan Expenditure. The results of VAR Granger Casualty Test are similar to the VAR Model excepting that Total Non-Plan Expenditure is found independent of SDP and there is also independence of SDP from Total Non-Plan Expenditure. KEY WORDS: budget, capital, elasticity, finance, grants, growth, NSDP, expenditure, VAR

A Study on the Spill Over Effects on Nifty 50 Returns: Evidence from Stock Market Performances of Major Asian Counterparts of India

This study investigates the spillover effects of major Asian stock market performances on the returns of the Nifty 50, India's benchmark index. Employing advanced econometric techniques, we analyze the dynamic relationships between the Nifty 50 and key Asian counterparts, considering both short-term and long-term horizons. Our research delves into return spillovers aiming to comprehensively assess the interconnectedness and transmission of market movements across the region. This study utilizes an extensive dataset covering Nifty 50 returns from April 1, 2013, to March 31, 2023, along with meticulously documented returns from stock market indexes linked to major strategic alliances in India. By applying advanced multivariate time series analysis techniques, encompassing Vector Autoregression (VAR), Granger causality tests, and impulse response functions, we model the intricate dynamics interweaving these indexes with the Nifty 50. The research endeavors to answer fundamental questions, including assessing short- term and long-term effects resulting from major Asian counterparts of India on Nifty returns, as well as exploring noticeable patterns that indicate the direction of causality between these counterparts and Nifty returns. The outcomes of this study carry substantial implications for decision-makers. Investors can make more informed decisions, businesses planning strategic partnerships can refine their strategies, and policymakers can formulate economic and financial regulations that accurately reflect the complexities of these dynamics. By unveiling the ripple effects of these counterparts on the Nifty 50, this research offers valuable insights into the evolving dynamics of India's financial markets and their integration into the global economy.

Return and Volatility Spillover in Equity and Commodity Market: Some Indian Evidence

This study empirically investigates the return and volatility spillover among equity markets, Agro and non-Agro commodity markets. The study has used daily return data of BSE SENSEX as equity index, MCX iCOMEDEX, including Gold, Silver, and Crude oil, as non-agro indices, and NCDEX (AGRIDEX) as agro commodity indices to analyze volatility transmission between assets and markets using Generalized Autoregressive Conditional Heteroscedasticity (GARCH) and Exponential Generalized Autoregressive Conditional Heteroscedastic (EGARCH) model. The study has used dummy variables in the variance equation to identify the structural breaks during the COVID 19 pandemic. Further, Vector Auto Regression (VAR) pair-wise Granger causality test has been used to understand returns’ causality. The study has found a bidirectional and unidirectional causal relationship between variables. The study indicates that NCDEX could influence all the variables except MCX Gold. In structural breaks with deterministic dummy variables during COVID-19, we have found mixed evidence of asymmetric impact on conditional volatility. The study also indicates a positive and significant asymmetric volatility effect in the case of MCX Gold and MCX Silver during the COVID-19 period. The study has specified that SENSEX dominates the inflow of information to all commodity markets. Also, the findings signpost that information flows from NCDEX to MCX Gold, MCX Silver, and MCX Crude Oil. JEL Codes: C32, E30, E44, F41, G15, Q43

A new approach for hydrograph data interpolation and outlier removal for vector autoregressive modelling: a case study from the Odra/Oder River

AbstractThis study presents a new approach for predicting water levels of the Odra/Oder river using vector autoregressive models (VAR). We use water level time series from 27 gauging stations, on which we interpolate no-data gaps using the LinAR method and detect outliers with two separate methods: the extreme values (EV) approach and the isolation forest (IFO) algorithm. Before removing potential outliers, we propose a hydrological evaluation based on multivariate data analysis. Finally, we consider three separate data scenarios, i.e. LinAR (no outlier rejection), EV, and IFO. VAR models for six prediction gauges were built in a moving window manner on the most recent 720 hourly water levels prior to each prediction. The analysis covered the time range from January 2016 to May 2022 and resulted in $$\varvec{\approx }$$ ≈ 1,000,000 water level forecasts (3 scenarios x 6 gauges x 55,000 hourly time steps) with lead time of 72 h. The analysis of root mean squared error (RMSE) indicates that the VAR model performs well, especially for 24-hour predictions, with RMSE values ranging from 8 to 28 cm. The model was also found to have skills in predicting a rising limb of a hydrograph. Our numerical experiments showed the susceptibility of the VAR predictions to artefacts. The IFO method was found to detect outliers skilfully, which allowed to produce the most accurate VAR-based predictions.

Re‐Unveiling the energy efficiency impact: Paving the way for sustainable growth in ASEAN countries

AbstractThe Association of Southeast Asian Nations (ASEAN) heavily relies on fossil fuels to drive economic growth, making it one of the world's largest greenhouse gas emitters. Promoting energy efficiency has been proposed as a solution to mitigate this issue. However, the potential negative impact of energy efficiency on economic growth remains unclear. Therefore, this study aims to re‐evaluate the dynamic impact of overall energy efficiency on economic growth in ASEAN countries, considering climate change factors. Unlike prior studies, this research measures overall efficiency based on both persistent and transient components of energy efficiency. This study employs two key techniques: stochastic frontier analysis (SFA) to estimate the overall energy efficiency and panel vector autoregression (PVAR) to analyze the dynamic causal effects of determinants' shocks. The findings suggest that enhancing ASEAN's overall energy efficiency could result in an 85.9% reduction in total energy consumption, indicating high levels of inefficiency in the region. While extensive energy efficiency measures might induce a temporary economic contraction in ASEAN countries, the rate of decline seems to abate over the long term. This implies that the impact of the energy efficiency shock will be most prominent during initial growth stages, progressively waning over time. Additionally, this study confirms the presence of the Environmental Kuznets Curve (EKC) for climate change and economic growth.

Testing bitcoin’s safe-haven property and the correlation between Bitcoin, gold, oil, stock markets, and Google trends

Since its public introduction in 2009, Bitcoin has grown to be the most well-known cryptocurrency worldwide. There is still debate as to whether Bitcoin may be used as a hedge against other assets. The purpose of this study is to investigate the correlation between Bitcoin and conventional commodity markets such as gold, crude oil, stock markets, and investor interest (quantified via Google Trends). In addition, the paper also tests Bitcoin’s safe haven role compared to other commodity markets. The Vector Autoregression model using daily database collected during the period 2013–2021 is employed to investigate the relationship between Bitcoin and traditional commodity markets. The impulse response function is used to analyze Bitcoin price movements against economic shocks from gold, oil prices, and the Dow Jones Industrial Average. In addition, the value-at-risk (VaR) model is used to test Bitcoin’s safe-haven property compared to other conventional commodity markets. The research results show that Bitcoin has negative impacts on gold, crude oil prices, and the stock market. Besides, Bitcoin responds negatively to a sharp decline in investor interest. Furthermore, the results of the VaR model show that Bitcoin is the second most volatile and risky asset, only after the crude oil market, and much riskier than gold. This result proves that Bitcoin cannot yet be considered a safe-haven instrument. These findings have several implications for investors and policymakers to minimize the risks associated with this cryptocurrency. AcknowledgmentThe authors would like to send their sincere thanks to the Reviewers and Editorial Board of the Journal. Their valuable comments and helpful support helped improve the paper’s quality. No funding was granted for this study.

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”.

Framing contestation and public influence on policymakers: evidence from US artificial intelligence policy discourse

Abstract As artificial intelligence (AI) policy has begun to take shape in recent years, policy actors have worked to influence policymakers by strategically promoting issue frames that define the problems and solutions policymakers should attend to. Three such issue frames are especially prominent, surrounding AI’s economic, geopolitical, and ethical dimensions. Relatedly, while technology policy is traditionally expert-dominated, new governance paradigms are encouraging increased public participation along with heightened attention to social and ethical dimensions of technology. This study aims to provide insight into whether members of the public and the issue frames they employ shape—or fail to shape—policymaker agendas, particularly for highly contested and technical policy domains. To assess this question, the study draws on a dataset of approximately five million Twitter messages from members of the public related to AI, as well as corresponding AI messages from the 115th and 116th US Congresses. After using text analysis techniques to identify the prevalence of issue frames, the study applies autoregressive integrated moving average and vector autoregression modeling to determine whether issue frames used by the public appear to influence the subsequent messaging used by federal US policymakers. Results indicate that the public does lead policymaker attention to AI generally. However, the public does not have a special role in shaping attention to ethical implications of AI, as public influence occurs only when the public discusses AI’s economic dimensions. Overall, the results suggest that calls for public engagement in AI policy may be underrealized and potentially circumscribed by strategic considerations.

How does monetary policy affect income and wealth inequality? Evidence from quantitative easing in the euro area

SummaryThis paper evaluates the impact of quantitative easing on income and wealth of individual euro area households. We first estimate the aggregate effects of a quantitative easing (QE) shock, identified by means of external instruments, in a multi‐country vector autoregression (VAR) model with unemployment, wages, gross operating surplus, interest rates, house prices, and stock prices. We then distribute the aggregate effects across households using a reduced‐form simulation on micro‐data, which captures the portfolio composition, the income composition, and the earnings heterogeneity channels of transmission. The earnings heterogeneity channel is important: QE compresses the income distribution because many households with lower incomes become employed. In contrast, monetary policy has only negligible effects on the Gini coefficient for wealth: While high‐wealth households benefit from higher stock prices, middle‐wealth households benefit from higher house prices.

Does State Dependence Matter in Relation to Oil Price Shocks on Global Economic Conditions?

Abstract A common thread in the literature shows that an oil price shock can have a major impact on global economic conditions. We examine the global dimensions of changes to the global oil price and world economic uncertainty using three model types: ordinary least square (OLS); general additive model (GAM); and non-linear vector autoregression (VAR) model with local projections (LP). Our study highlights a positive and statistically significant effect of oil prices on economic uncertainty during non-expansionary periods, yet the impact is negative on economic uncertainty during periods of economic growth. Using a VAR-LP we analyze the global dimensions of a world oil price shock on global economic conditions and investigate whether there is consistency in how an oil price shock influences economic growth, consumer prices and economic uncertainty based on the state of economic conditions. The empirical evidence shows that during an expansionary (a non-expansionary) period, the impact of an oil price shock lowers (elevates) economic uncertainty. The empirical evidence from the three model types taken together indicate a presence of state dependence on the influence of an oil price shock.

What time-varying network models based on functional analysis tell us about the course of a patient's problem

Background: Relations among psychological variables are assumed to be complex and to vary over time. Personalized networks can model multivariate complex interactions. The development of time-varying networks allows to model the variation of parameters over time. Objectives: We aimed to determine the value of time-varying networks for clinical practice. Methods: We applied time-varying mixed graphical models (TV-MGM) and time-varying vector autoregressive models (TV-VAR) to intensive longitudinal data of nine participants with depressive symptoms (n = 6) or anxiety (n = 3). Results: Most of the participants showed temporal changes in network topology within the assessment period of 30 days. Time-varying networks of participants with small, medium, and large time variability in edge parameters clearly show the different temporal evolvements of dynamic interactions between variables. The case example indicates clinical utility but also limitations to the application of time-varying networks in clinical practice. Conclusion: Time-varying network models provide a data-driven and exploratory approach that could complement current diagnostic standards by reflecting interacting, often mutually reinforcing processes of mental health problems and by accounting for variation over time. They can be used to generate hypotheses for further confirmatory and clinical testing.

The uncertainty of economic policies in the United States and its impact on the stock markets of some African countries: An empirical study with VAR modeling

The economic and financial integration that has taken place over the last 20 years has led to a situation where the reaction of financial markets is no longer solely due to local factors, but also to macroeconomic and financial news of regional and international origin. In this regard, we focus on the effect of the international transmission of uncertainty shocks on global stock market performance. Given that the United States is the world’s leading economic power, some studies have highlighted the adverse effects of the international propagation of US uncertainty shocks on international stock markets (Ehrmann and Fratzscher, 2009; Klobner and Sekkel, 2014; Hammoudeh et al., 2016).
 Using VAR modeling, our study examines the specific impact of economic policy uncertainty in the USA on the stock markets of four African countries: Tunisia, Egypt, Morocco, and South Africa. Although geographically distant from the USA, these economies are closely integrated into the global economy and are likely to be affected by international economic and political developments, particularly those originating in the USA, a major economy and a key player on the global stage.

Monitoring covariance in multivariate time series: Comparing machine learning and statistical approaches

AbstractIn complex systems with multiple variables monitored at high‐frequency, variables are not only temporally autocorrelated, but they may also be nonlinearly related or exhibit nonstationarity as the inputs or operation changes. One approach to handling such variables is to detrend them prior to monitoring and then apply control charts that assume independence and stationarity to the residuals. Monitoring controlled systems is even more challenging because the control strategy seeks to maintain variables at prespecified mean levels, and to compensate, correlations among variables may change, making monitoring the covariance essential. In this paper, a vector autoregressive model (VAR) is compared with a multivariate random forest (MRF) and a neural network (NN) for detrending multivariate time series prior to monitoring the covariance of the residuals using a multivariate exponentially weighted moving average (MEWMA) control chart. Machine learning models have an advantage when the data's structure is unknown or may change. We design a novel simulation study with nonlinear, nonstationary, and autocorrelated data to compare the different detrending models and subsequent covariance monitoring. The machine learning models have superior performance for nonlinear and strongly autocorrelated data and similar performance for linear data. An illustration with data from a reverse osmosis process is given.

AN EMPIRICAL TAKE ON THE “PLUCKING” MODEL

Traditional estimates of potential output based on the symmetric business cycle (boom–bust) view are prone to large retrospective revisions. The plucking view, which is supported by empirics, offers possible respite. Serious attempts to estimate the output ceiling in the plucking view, analogous to potential output in the traditional view, are scarce. This paper has three parts. The first estimates the output ceiling in a computationally simple approach. The second analyzes the relationship of the resultant output gap with inflation and with the unemployment rate. A comparison is made against output gap estimates based on prevailing methods. The third uses a vector autoregression (VAR) to analyze responses to supply and demand shocks. The paper finds that the plucking model offers three points of improvement over prevailing methods based on the boom–bust view. First, boom–bust estimates are prone to large retrospective revisions when two-sided filters are used, while the plucking estimates are not. Second, output gap estimates from both views do not correlate well with core inflation. This absence of evidence for a Phillips curve is problematic for the boom–bust view. The plucking estimates correlated well with the unemployment rate but the boom–bust estimates did not. Third, the output ceiling from the plucking model responded only to supply (oil price) shocks and not demand (interest rate) shocks, hence fits the description of a supply measure. This paper provides a template to estimate the output ceiling of the plucking model in other countries using publicly available macroeconomic data.