Insurance market density and economic growth in Eurozone countries:the granger causality approach

Background:This study examines the relationship between insurance market density(IMD)and economic growth.Methods:We employed Granger causality technique in 19 Eurozone countries for the period 1980-2014.We use three different indicators of IMD,namely life insurance density,non-life insurance density,and total insurance density.We particularly emphasize on whether Granger causality runs between IMD and economic growth both ways,one way,or not at all.Results:Our empirical result recognizes the presence of both unidirectional and bidirectional causality between insurance market density and economic growth.However,these results are mostly non-uniform across Eurozone countries.Conclusions:This study holds important policy implications-economic policies should recognize the differences in the insurance market density and economic growth in order to maintain sustainable economic growth in the Eurozone.

Rural Financial Development and Rural Economic Efficiency Improvement Based on Granger Causality Test

Based on the co-integration test theory,Financial Interrelation Ratio(FIR),level of financial efficiency(LFE),level of financial development(LFD) and other indices evaluating the rural financial development are selected by Granger Causality Test.The rural loan balance(RLB),rural deposit balance(RDB),total rural output(TRO),fixed assets investment(FAI),Financial Interrelation Ratio(FIR),economic efficiency(EE),level of financial efficiency(LFE),and level of financial development(LFD) in the years 1979-2007 are collected.Graphical method intuitively reflects the development trend and historical track of relevant indices;and Granger Causality Test verifies the relationship between rural financial development level and rural economic efficiency in the years 1979-2007.Result shows that rural financial development level has significant impact on rural economic growth,but rural economic growth has no significant impact on rural financial development;and rural financial development also has insignificant promotion impact on rural economic efficiency.Thus,conclusions are obtained.Although rural financial development has made certain contribution to the development of rural economy,this kind of contribution is only reflected in total quantity,but not efficiency.Therefore,government should further strengthen the promotion function of financial development for economic efficiency,and gradually establish a virtuous circle system for rural finance and economic development.

Research on Interest Rate Transmission Mechanism of China’s Bond Market:Empirical Analysis Based on Granger Causality Complex Network

The bond market is an important market for investment and financing in China’s economic sectors,and also an important part of the monetary policy framework.The internal transmission of bond market is an important part of market interest rate transmission,which iscritical to the effectiveness of monetary policy.However,few scholars have studied the characteristics of interest rate transmission in China.An in-depth study of the interest rate transmission mechanism and its dynamic evolution between different bond markets is conducive to clarify the pulse of transmission within Chinese bond market and to further unblock the transmission mechanism of monetary policy.From the perspective of system theory and based on the analysis method of Granger causality complex network,this paper finds that the interest rate transmission among various varieties in China’s bond market is relatively significant.Treasury bonds and CDB bonds are the two core bond varieties of interest rate transmission in the bond market.Simultaneously,this study concludes that the medium and long-term interest rate played a dominant role in the transmission of market interest rate during the easing phase of monetary policy,while the short-term interest rate played a dominant role in the transmission of market interest rate during the tightening phase of monetary policy.This paper also gives enlightenment and suggestions.

LEARNING GRANGER CAUSALITY GRAPHS FOR MULTIVARIATE NONLINEAR TIME SERIES

An information theory method is proposed to test the. Granger causality and contemporaneous conditional independence in Granger causality graph models. In the graphs, the vertex set denotes the component series of the multivariate time series, and the directed edges denote causal dependence, while the undirected edges reflect the instantaneous dependence. The presence of the edges is measured by a statistics based on conditional mutual information and tested by a permutation procedure. Furthermore, for the existed relations, a statistics based on the difference between general conditional mutual information and linear conditional mutual information is proposed to test the nonlinearity. The significance of the nonlinear test statistics is determined by a bootstrap method based on surrogate data. We investigate the finite sample behavior of the procedure through simulation time series with different dependence structures, including linear and nonlinear relations.

Directionality of paper reviewing and publishing of a scientist: A Granger causality inference

It has been evidenced that peer review activities are positively correlated to scientists’bibliometric performance(e.g.,Ortega,2017,2019).However,how the number of paper’reviewing’interacts with a scientist’s’publishing’has not been addressed in previous studies.This paper attempts to employ the Granger causality inference to explore the directionality between a scientist’s publication performance and his/her review activities.Our dataset comprises scientists’reviewed articles derived from Publons in the Web of Knowledge database,and their publications retrieved from Pub Med.We find that scientists who reviewed less or published less tend to have Granger causality between reviewing and publishing activities.In addition,compared with early-career researchers,reviewing advances publishing for senior scientists.

GGC:Gray-Granger Causality Method for Sensor Correlation Network Structure Mining on High-Speed Train

Vehicle information on high-speed trains can not only determine whether the various parts of the train are working normally,but also predict the train’s future operating status.How to obtain valuable information from massive vehicle data is a difficult point.First,we divide the vehicle data of a high-speed train into 13 subsystem datasets,according to the functions of the collection components.Then,according to the gray theory and the Granger causality test,we propose the Gray-Granger Causality(GGC)model,which can construct a vehicle information network on the basis of the correlation between the collection components.By using the complex network theory to mine vehicle information and its subsystem networks,we find that the vehicle information network and its subsystem networks have the characteristics of a scale-free network.In addition,the vehicle information network is weak against attacks,but the subsystem network is closely connected and strong against attacks.

Robust Parameter Identification Method of Adhesion Model for Heavy Haul Trains

A robust parameter identification method based on Kiencke model was proposed to solve the problem of the parameter identification accuracy being affected by the rail environment change and noise interference for heavy-duty trains. Firstly, a Kiencke stick-creep identification model was constructed, and the parameter identification task was transformed into a quadratic programming problem. Secondly, an iterative algorithm was constructed to solve the problem, into which a time-varying forgetting factor was added to track the change of the rail environment, and to solve the uncertainty problem of the wheel-rail environment. The Granger causality test was adopted to detect the interference, and then the weights of the current data were redistributed to solve the problem of noise interference in parameter identification. Finally, simulations were carried out and the results showed that the proposed method could track the change of the track environment in time, reduce the noise interference in the identification process, and effectively identify the adhesion performance parameters.

Correlation analysis and causality test between Ludong-Huanghai block and South Japan

In this paper, we make a comparative analysis and correlation test for the seismic activities in the South Japan and the Ludong-Huanghai block （a secondary tectonic unit in the North China） and approach the relationship between the energy release processes of these two areas by using co-integration analysis and Granger causality test for the time series of random variables. The results show that the seismic activities in these two areas are correlative and synchronous to a certain extent, and their release series of cumulative strain energy are contemporaneously correlative. Both energy series are first-order difference stationary processes and there is secular and steady co-integration between them. We make a positive analysis on the first-order difference energy series through Granger causality test based on vector error correction （VEC） model and find there is unilateral Granger causality and prominent co-integration between the two energy release processes.

Causality between bank’s major activities and economic growth:evidences from Pakistan

Background:Banking is an important sector of Pakistan’s economy.It is general consideration that bank’s major activities saving and lending have positive impact on economic growth.So the aim of this study is to investigate this consideration and also investigate that either growth led deposits and credits,or deposit and credits led growth means the purpose of this study is to investigate the direction of this relationship.Methods:Johansen test of Co-integration and Granger Causality is employed by using time series data of Pakistan from 1961 to 2013.Results:The results show that two major activities of banking sector that are saving and lending don’t have any long run or short run causality towards economic growth so the general consideration of positive impact of these activities proved wrong in case of Pakistan.However there is unidirectional causality running from GDP growth to credit provided by banking sector which shows that economic prosperity or economic growth will have a major impact on lending activities of banks meaning that demand following hypothesis is true for Pakistan in case of GDP and Bank’s credit or we can say that growth led Bank’s credit in Pakistan.Conclusions:Hence Government and central bank should make policies by keeping this fact in consideration that bank’s two major activities that are saving and lending does not have impact on GDP growth.There might be other factors which influence economic growth of Pakistan more than banking sector these activities,which can be bank’s profitability,human resource,technology,infrastructure and other sectors of the economy.However GDP growth affects bank’s lending activities so during high economic growth year central bank and private bank’s management should introduce easy loans for businesses and industries and during poor economic growth years personal loan’s new schemes should be introduce by banks.

Which return regime induces overconfidence behavior?Artificial intelligence and a nonlinear approach

Overconfidence behavior,one form of positive illusion,has drawn considerable attention throughout history because it is viewed as the main reason for many crises.Investors’overconfidence,which can be observed as overtrading following positive returns,may lead to inefficiencies in stock markets.To the best of our knowledge,this is the first study to examine the presence of investor overconfidence by employing an artificial intelligence technique and a nonlinear approach to impulse responses to analyze the impact of different return regimes on the overconfidence attitude.We examine whether investors in an emerging stock market(Borsa Istanbul)exhibit overconfidence behavior using a feed-forward,neural network,nonlinear Granger causality test and nonlinear impulseresponse functions based on local projections.These are the first applications in the relevant literature due to the novelty of these models in forecasting high-dimensional,multivariate time series.The results obtained from distinguishing between the different market regimes to analyze the responses of trading volume to return shocks contradict those in the literature,which is the key contribution of the study.The empirical findings imply that overconfidence behavior exhibits asymmetries in different return regimes and is persistent during the 20-day forecasting horizon.Overconfidence is more persistent in the low-than in the high-return regime.In the negative interest-rate period,a high-return regime induces overconfidence behavior,whereas in the positive interest-rate period,a low-return regime induces overconfidence behavior.Based on the empirical findings,investors should be aware that portfolio gains may result in losses depending on aggressive and excessive trading strategies,particularly in low-return regimes.

Industry return lead‑lag relationships between the US and other major countries

In this study,we analyze the lead-lag relationships between the US industry index and those of six other major countries from January 1973 to May 2021.We identify the leading role played by the US internationally by showing that the weekly returns of US industries,especially the US basic materials and energy industries,significantly Granger cause the returns of most other countries’industries,suggesting that non-US industries react with some delay to new information.This delayed reaction is even more notice-able during periods of recession in the US when cross-country correlations are higher.This implies that the ability of the lagged returns of US industries to predict industries’returns from other countries is even more pronounced when the US experienced an economic recession.A similar asymmetric relationship is found between the volatil-ity of US industries and that of industries in other markets.The analysis of causality in the distribution of returns and volatility shows that causality runs mainly from the US to other countries,particularly in the presence of extreme negative shocks.Finally,we demonstrate that our predictions are valuable to real-world investors.Long-short strategies generate sizable and statistically significant alphas,and a constant relative risk-averse investor obtains certainty equivalent returns well above the risk-free rate.

Coupling analysis of social-economic water consumption and its effects on the arid environments in Xinjiang of China based on the water and ecological footprints

In arid areas,ecological degradation aroused by over-exploitation of fresh water,expansion of artificial oasis and shrinkage of natural oasis,has drawn attention of many scholars and officials.The water and ecological footprints can be used to quantitatively evaluate the water consumption of social-economic activities and their influence on the eco-environments.In addition,increase of the water footprint indicates the expansion of artificial oasis,and the influence on the natural oasis could be reflected by the variation of the ecological footprint.This study was conducted to answer a scientific question that what is the quantitative relationship between the expansion of the artificial oasis and the degradation of the natural oasis in the arid environments of Xinjiang,China.Thus,based on the social-economic data,water consumption data and meteorological data during 2001–2015,we calculated the water and ecological footprints to express the human-related pressure exerted on the water resources and arid environments in Xinjiang(including 14 prefectures and cities),and explore the relationship between the water and ecological footprints and its mechanism by using the coupling analysis and Granger causality test.The results show that both the water and ecological footprints of Xinjiang increased significantly during 2001–2015,and the increasing rate of the ecological footprint was much faster than that of the water footprint.The coupling degree between the water and ecological footprints was relatively high at the temporal scale and varied at the spatial scale.Among the 14 prefectures and cities examined in Xinjiang,the greater social-economic development(such as in Karamay and Urumqi)was associated with the lower coupling degree between the two footprints.Increases in the water footprint will cause the ecological footprint to increase,such that a 1-unit increase in the consumption of water resources would lead to 2–3 units of ecological degradation.The quantitative relationship between the increases of the water and ecological footprints,together with the intensities of water consumption both in the natural and artificial oases of Tarim River Basin,have approved the fact that the formation and expansion of 1 unit of the artificial oasis would bring about the degradation of 2 units of the natural oasis.These conclusions not only provide a technical basis for sustainable development in Xinjiang,but also offer a theoretical guide and scientific information that could be used in similar arid areas around the world.

The relationship between international crude oil prices and China＇s refined oil prices based on a structural VAR model

With the frequent fluctuations of international crude oil prices and China＇s increasing dependence on foreign oil in recent years, the volatility of international oil prices has significantly influenced China domestic refined oil price. This paper aims to investigate the transmission and feedback mechanism between international crude oil prices and China＇s refined oil prices for the time span from January 2011 to November 2015 by using the Granger causality test, vector autoregression model, impulse response function and variance decomposition methods. It is demonstrated that variation of international crude oil prices can cause China domestic refined oil price to change with a weak feedback effect. Moreover, international crude oil prices and China domestic refined oil prices are affected by their lag terms in positive and negative directions in different degrees. Besides, an international crude oil price shock has a signif- icant positive impact on domestic refined oil prices while the impulse response of the international crude oil price variable to the domestic refined oil price shock is negatively insignificant. Furthermore, international crude oil prices and domestic refined oil prices have strong historical inheri- tance. According to the variance decomposition analysis, the international crude oil price is significantly affected by its own disturbance influence, and a domestic refined oil price shock has a slight impact on international crude oil price changes. The domestic refined oil price variance is mainly caused by international crude oil price disturbance, while the domestic refined oil price is slightly affected by its own disturbance. Generally, domestic refined oil prices do not immediately respond to an international crude oil price change, that is, there is a time lag.

Effect of Chinese tuina massage therapy on resting state brain functional network of patients with chronic neck pain

Objective:Cervical disease,a type of chronic pain,can greatly impact quality of life.Traditional Chinese tuina,a form of therapeutic massage and manipulation,has been shown to be effective in relieving pain and other symptoms in patients with chronic neck pain.This study applied functional magnetic resonance imaging (fMRI) to explore the features of the resting state network of patients with chronic neck pain caused by cervical radiculopathy,and how tuina affects the causality between intrinsic brain networks.Methods:Using Granger causality analysis,effective connectivity of brain networks of 10 patients with chronic neck pain was compared with 10 healthy control subjects.Resting state fMRI data were using magnetic resonance scanning.Cervical spondylosis symptom scores were evaluated before and after 4 weeks of tuina therapy.Independent component analysis was applied to extract the specific networks related to sensation,execution,and cognition,including sensorimotor network (SMN),visual network (VN),auditory network (AN),anterior and posterior default mode network (aDMN,pDMN),left frontoparietal network and right frontoparietal network.Results:Compared with the control group,data from the treatment group revealed two major findings:before tuina therapy,SMN had a profound influence on aDMN and AN greatly affected pDMN;however,after 4 weeks of tuina therapy,aDMN and SMN showed reversed causality.Conclusion:Chronic neck pain caused by cervical radiculopathy may influence the DMN,which plays an important role in emotion,cognition,and memory,by stimulating the sensory afferent network.Tuina not only significantly relieves pain and discomfort,but also reverses the causality between aDMN and SMN.

Impact of the COVID‑19 outbreak on the US equity sectors:Evidence from quantile return spillovers

The aim of this study is to examine the extreme return spillovers among the US stock market sectors in the light of the COVID-19 outbreak.To this end,we extend the now-traditional Diebold-Yilmaz spillover index to the quantiles domain by building networks of generalized forecast error variance decomposition of a quantile vector autoregressive model specifically for extreme returns.Notably,we control for common movements by using the overall stock market index as a common factor for all sectors and uncover the effect of the COVID-19 outbreak on the dynamics of the network.The results show that the network structure and spillovers differ considerably with respect to the market state.During stable times,the network shows a nice sectoral clustering structure which,however,changes dramatically for both adverse and beneficial market conditions constituting a highly connected network structure.The pandemic period itself shows an interesting restructuring of the network as the dominant clusters become more tightly connected while the rest of the network remains well separated.The sectoral topology thus has not collapsed into a unified market during the pandemic.

Remittances,financial development and economic growth in sub-Saharan African countries:evidence from a PMG-ARDL approach

The study investigated the relationship among remittances,financial development and economic growth in a panel of 20 sub-Saharan African countries over the period of 2000 and 2015.The study used both Pooled Mean Group and Mean Group/ARDL estimations with panel unit root and cointegration tests.After establishing cointegration,remittances and financial development were found to have positive effects on economic growth both in the short and the long run.The interactive term showed that financial development acted as a substitute in the remittances-growth relationship.Finally,unidirectional causal relationships were found to exist from GDP to remittances and from financial development to GDP.However,no causality existed between remittances and financial development in the SSA countries.

Financial innovation and economic growth in Bangladesh

Background:This study provides evidence for the financial innovation in the financial system that resulted in the economic growth of Bangladesh from 1980-2016.Methods:To capture the influence of financial innovation on economic growth,we estimated the long-run cointegration by applying Autoregressive Distributed Lag(ARDL)bound testing and Granger causality-based Error Correction Model(ECM)to capture the directional association.Results:The Test of Cointegration satisfied the existence of a long-run association between economic growth and the financial innovation proxies,which were the Domestic Credit to the Private Sector(DCB)as a percentage of the Gross Domestic Product and the Broad-to-Narrow Money(M2/M1)as a percentage of the Gross Domestic Product.Our results showed that in the long run,credit circulation to the private sector and monetary management play important roles in economic growth.We also found that the coefficients of the financial innovation proxy variables were positive and statistically significant both in the short run and long run.We also ran Granger causality tests to investigate the directional effect.This study confirmed the feedback causality between the economic growth and 2 proxies of financial innovation in the short and long run.The gross capital formation and trade openness contribute significantly to explaining the economic growth in Bangladesh.Conclusion:The government of Bangladesh should encourage financial innovation in the financial system,especially at financial institutions,so that access to financial services can easily provide for equitable development.The government should also encourage financial innovation in the capital market,which will assist in raising longterm capital for investment and expedite overall economic growth.

Research on the Relationship Between Garlic and Young GarlicShoot Based on Big Data

In view of the problems such as frequent fluctuation of garlic price, lack ofefficient forecasting means and difficulty in realizing the steady development of garlicindustry, combined with the current situation of garlic industry and the collected datainformation. Taking Big Data platform of garlic industry chain as the core, using themethods of correlation analysis, smoothness test, co-integration test, and Grangercausality test, this paper analyzes the correlation, dynamic, and causality between garlicprice and young garlic shoot price. According to the current situation of garlic industry,the garlic industry service based on Big Data is put forward. It is concluded that there is apositive correlation between garlic price and young garlic shoot price, and there is a longtermstable dynamic equilibrium relationship between young garlic shoot price and garlicprice fluctuation, and young garlic shoot price can affect garlic price. Finally, it isproposed to strengthen the infrastructure construction of garlic Big Data, increase thetechnological innovation and application of garlic Big Data technology, and promote thesafety and security ability of the whole industry to promote the development of garlicindustry.

The time‑varying causal relationship between the Bitcoin market and internet attention

The increasing attention on Bitcoin since 2013 prompts the issue of possible evidence for a causal relationship between the Bitcoin market and internet attention.Taking the Google search volume index as the measure of internet attention,time-varying Granger causality between the global Bitcoin market and internet attention is examined.Empirical results show a strong Granger causal relationship between internet attention and trading volume.Moreover,they indicate,beginning in early 2018,an even stronger impact of trading volume on internet attention,which is consistent with the rapid increase in Bitcoin users following the 2017 Bitcoin bubble.Although Bitcoin returns are found to strongly affect internet attention,internet attention only occasionally affects Bitcoin returns.Further investigation reveals that interactions between internet attention and returns can be amplified by extreme changes in prices,and internet attention is more likely to lead to returns during Bitcoin bubbles.These empirical findings shed light on cryptocurrency investor attention theory and imply trading strategy in Bitcoin markets.

The impact of bank lending on Palestine economic growth:an econometric analysis of time series data

Banking is an essential sector of Palestine’s economy.More credits provided by banks are considered to have a positive impact on economic growth so that the overall objective of this study is to examine the impact of bank lending on economic growth in Palestine.The study employs the Augmented Dickey-Fuller to test for stationarity in the time series,The Johansen co-integration,Vector Autoregressive Model and Vector Error Correction Model are employed to identify the long-run and short-run dynamics among the variables,and Granger causality test in order to determine the direction of causality.The study finds that a long run relationship exists among the variables and insignificant short run relationship.Also,the study findings show that there is unidirectional causality and runs from GDP to bank lending.The insignificant contribution of bank lending to GDP is attributed to the fact that banks are not highly interested in lending to the production sector of the economy due to the high level of risk.However,the primary empirical evidence reveals that bank lending doesn’t cause economic growth,but economic growth causes bank lending.