Granger Causality Method Research Articles

Analyzing the relationship between economic growth and electricity consumption from renewable and non-renewable sources: Fresh evidence from newly industrialized countries

This study analyzes the impact of renewable electricity consumption on economic growth for the panel of 10 newly industrialized countries from 1990 to 2015. For this, panel unit root tests, panel heterogeneous co-integration method, panel Fully Modified Ordinary Least Square and the Granger causality method are employed. The primary outcomes of this study are as follows: (1) panel unit root test and panel co-integration method confirm that the economic growth, renewable electricity consumption, non-renewable electricity consumption, gross capital formation, labor force and trade openness are co-integrated, (2) panel Fully Modified Ordinary Least Square confirms that all studied variables have a positive long-run effect on economic growth, a 1% increase in renewable and non-renewable electricity consumption increased economic growth by 0.095% and 0.017% respectively, (3) the outcomes from Granger causality test also illustrates that there is bidirectional causation between renewable electricity consumption and economic growth in both the short-run and long-run that supports the feedback hypothesis. From the empirical findings, the feedback hypothesis is valid for newly industrialized countries.

Air Transportation, Economy and Causality: Remote Towns in Brazil’s Amazon Region

There is a vast amount of literature on relations between air transport activities and economic growth. The recognition that these are important relationships has aroused the interest of researchers, who have studied them with regard to remote towns. The empirical studies have concentrated more on developed areas, such as Europe, North America, and Australia. No empirical studies were found for Brazil, which holds most of the Amazon region. In this study, the Granger causality method was used to investigate for causal relationships between regular domestic air passenger transport links and the gross domestic product of small municipalities in this region. The results indicate short- and long-term, two-way causality in which gross domestic product has a stronger impact on air transport than vice versa.

Is internet penetration narrowing the rural–urban income inequality? A cross-regional study of China

Internet penetration (NET) brings new opportunities as well as challenges to countries all over the world. It can narrow the rural–urban income inequality (RUI), because it increases the connections of rural areas to the urban areas from both the production side and consumption side. It can also enlarge the RUI, because the internet may be skill-biased. Meanwhile, income level and the RUI may lead to different local internet development. However, the relationship between NET and RUI remains unclarified. This study applies the method of bootstrap panel Granger causality to explore the causal relationship between NET and RUI. The estimation results show that the causal relationship between NET and RUI varies across different provinces and regions, which is in line with the hypothesis of the inverted U-shaped technological Kuznets curve (TKC). Specifically, the NET does Granger-cause RUI in two-fifths of China’s provinces, primarily in North China and East China, while RUI does not Granger-cause NET in China since the NET itself is largely dependent on government policies. Therefore, policymakers should develop fair internet development policies targeting the improvement of rural and urban income distribution.

Neural Granger Causality.

While most classical approaches to Granger causality detection assume linear dynamics, many interactions in real-world applications, like neuroscience and genomics, are inherently nonlinear. In these cases, using linear models may lead to inconsistent estimation of Granger causal interactions. We propose a class of nonlinear methods by applying structured multilayer perceptrons (MLPs) or recurrent neural networks (RNNs) combined with sparsity-inducing penalties on the weights. By encouraging specific sets of weights to be zero-in particular, through the use of convex group-lasso penalties-we can extract the Granger causal structure. To further contrast with traditional approaches, our framework naturally enables us to efficiently capture long-range dependencies between series either via our RNNs or through an automatic lag selection in the MLP. We show that our neural Granger causality methods outperform state-of-the-art nonlinear Granger causality methods on the DREAM3 challenge data. This data consists of nonlinear gene expression and regulation time courses with only a limited number of time points. The successes we show in this challenging dataset provide a powerful example of how deep learning can be useful in cases that go beyond prediction on large datasets. We likewise illustrate our methods in detecting nonlinear interactions in a human motion capture dataset.

Assessment of Anesthesia Depth Using Effective Brain Connectivity Based on Transfer Entropy on EEG Signal.

Introduction:Ensuring an adequate Depth of Anesthesia (DOA) during surgery is essential for anesthesiologists. Since the effect of anesthetic drugs is on the central nervous system, brain signals such as Electroencephalogram (EEG) can be used for DOA estimation. Anesthesia can interfere among brain regions, so the relationship among different areas can be a key factor in the anesthetic process.Methods:In this paper, by combining the Wiener causality concept and the conditional mutual information, a nonlinear effective connectivity measure called Transfer Entropy (TE) is presented to describe the relationship between EEG signals at frontal and temporal regions from eight volunteers in three anesthetic states (awake, unconscious and recovery). This index is also compared with Granger causality and partial directional coherence methods as common effective connectivity indexes.Results:Based on a statistical analysis of the probability predictive value and Kruskal-Wallis statistical method, TE can effectively fallow the effect-site concentration of propofol and distinguish the anesthetic states well, and perform better than the other effective connectivity indexes. This index is also better than Bispectral Index (BIS) as commercial DOA monitor because of the faster response and higher correlation with the drug concentration effect-site, less irregularity in the unconscious state and better ability to distinguish three states of anesthestesia.Conclusion:TE index is a confident indicator for designing a new monitoring system of the two EEG channels for DOA estimation.

Economic Development, Globalization, Political Risk and CO2 Emission: The Case of Vietnam

This study investigates the dynamic effects of economic development, international cooperation, electricity consumption, and political risk on the escalation of CO₂ emission in Vietnam. We adopted autoregressive distributed lag model and Granger causality method to examine the interaction between CO₂ and various economic and political factors, including foreign direct investment, trade openness, economic growth, manufacture, electricity consumption, and political risk in Vietnam since the economic revolution in 1986. The findings reflect opposite influence between these factors and the level of CO₂ in the intermediate and long-term durations. Accordingly, foreign direct investment and CO₂ emission have a bidirectional relationship, in which foreign direct investment accelerates short-term CO₂ emission, but reduces it in the long run through an interactive mechanism. Moreover, economic development increases the volume of CO₂ emission in both short and long run. There was also evidence that political risk has a negative effect on the environment. Overall, the findings confirm lasting negative environmental effects of economic growth, trade liberalization, and increased electricity consumption. These factors, with Granger causality, mutually affect the escalation of CO₂ in Vietnam. In order to control the level of CO₂, more efforts are required to improve administrative transparency, attract high-quality foreign investment, and decouple the environment from economic development.

699770 Women and Economic Development in Turkey: An Empirical Analysis

After the industrial revolution, the role of women in the economy has increased all over the world. .The phenomenon of globalization has further increased the role of women in the economy. Turkey is no exception to this development. Especially in the last two decades, the labour force participation rate of women in Turkey shows a significant increase. The education level and rate of women has increased in the same way. This study examines the role of women on economic growth of Turkey and covers the years 1991-2018. In this period, the relationships between women's labour force participation rates and economic growth in Turkey were examined empirically using VAR and Granger Causality methods. The findings show a positive relationship was observed between female labour force participation and economic growth.

Nonlinear dynamic correlation between geopolitical risk and oil prices: A study based on high-frequency data

This study investigates the nonlinear dynamic correlations between geopolitical risk (GPR) and oil prices using nonlinear Granger causality and DCC-MVGARCH methods based on high-frequency data. The relationship between GPR and oil prices is found to have a complex nonlinear relationship rather than a simple linear one. Further, a bidirectional nonlinear Granger causality is found to consistently exist between GPR and oil volatility across different components of realized volatility. In terms of returns, GPR has relatively weak unidirectional nonlinear Granger causation with oil returns. The dynamic correlation analysis shows that GPR mainly affects oil volatility rather than returns. Moreover, GPR mainly affects oil volatility through the jump component of the oil market after the financial crisis, and there is a strong positive correlation between GPR and volatility jumps. Our findings innovatively suggest that GPR can potentially be utilized to improve models of volatility jumps and provide reference for investors and price analysts in oil markets who want to design sensible risk-management strategies.

Explicit Granger causality in kernel Hilbert spaces.

Granger causality (GC) is undoubtedly the most widely used method to infer cause-effect relations from observational time series. Several nonlinear alternatives to GC have been proposed based on kernel methods. We generalize kernel Granger causality by considering the variables' cross-relations explicitly in Hilbert spaces. The framework is shown to generalize the linear and kernel GC methods and comes with tighter bounds of performance based on Rademacher complexity. We successfully evaluate its performance in standard dynamical systems, as well as to identify the arrow of time in coupled Rössler systems, and it is exploited to disclose the El Niño-Southern Oscillation phenomenon footprints on soil moisture globally.

Reconstruction of the Coupling Architecture in the Ensembles of Radio-Engineering Oscillators by Their Signals Using the Methods of Granger Causality and Partial Directed Coherence

Reconstruction of the Coupling Architecture in the Ensembles of Radio-Engineering Oscillators by Their Signals Using the Methods of Granger Causality and Partial Directed Coherence

THE EFFECT OF INSURANCE PREMIUM ON ECONOMIC GROWTH IN EUROPEAN UNION COUNTRIES: PANEL DATA ANALYSIS

Funds collected as insurance premiums are very important for the development and deepening of the national economies. These premiums paid to insurance companies provide funds for capital markets by creating savings sources. The aim of this study is to investigate how and in what direction the insurance premium volume affects the economic growth of these 25 countries. For this purpose, Johansen Cointegration, Kao cointegration, panel FMOLS, Dumitrescu and Hurlin (2012) Panel Granger Causality methods were used. Cointegration tests show that economic growth and insurance premium volume variables have long-term cointegration relationships. According to Panel FMOLS test results, a 1% increase in insurance premium volumes leads to an increase of 0.113% in economic growth. According to Dumitrescu and Hurlin (2012) panel Granger causality analysis, there is one-way causality from insurance volumes to economic growth.

Spillover effect between carbon spot and futures market: evidence from EU ETS.

The spillover effects ofEuropean Union Allowances (EUA) spot and futuresmarkets are important for investors in order to understand the relevance and risk management of product prices. This paper uses non-linear methods of Granger causality to test the mean spillover relationship between the two markets and then analyzes volatility spillovers between the two by the non-linear TVP-VAR spillover index. The results show that (1) the non-linear Granger causality test better reflects the mean spillover relationship between EUA spot and futures; (2) there is a bidirectional non-linear mean spillover effect between EUA spot and futures prices for the European Union Emissions Trading Scheme (EU ETS) phases II and III; and (3) volatility spillovers, appearing in EUA spot and future markets in both phases, work increasingly strong over time and are vulnerable to financial crises and extreme events.

An Assessment of House Loans Cointegration with Macro Variables in Selected Euro Zone Countries

This paper performs an empirical study on house loans, interest rates, unemployment, and house rent prices relationship in Germany, France, Spain and Italy from the year 2003 to 2018. We look for the cointegration and causality relationship between the house loans and macro variables with the help of the Vector error correction model (VECM) and Granger causality methods. We investigate whether variables with monthly data explain better the relationship and causal effects between the variables. We find a long term cointegrating relationship between the real house loans and interest rates, unemployment and house rent prices for France, Spain, and Italy, but not for Germany. On average the equilibrium in house loan development is reached from 4 to 8 years, meaning that long term equilibrium exists, but the variables reach it in a rather long time period. The ECB deposit facility rate included as an exogenous variable in four countries gained no significant power in explaining the short term changes of house loans in any of the country. We reveal a complex interaction between the bank’s credits and unemployment, interest rates, house rental prices in the paper.

Is there a bidirectional relationship between female labour force participation and economic development in Middle Eastern countries? Evidence from a bootstrap panel Granger causality test

ABSTRACT This study explores a bidirectional relationship between the female labour force participation rate and the economic development in fourteen Middle Eastern countries. The bootstrap panel Granger causality method, using seemingly unrelated regressions and Wald tests with country-specific bootstrap critical values, is employed to test causality. The empirical results indicate bidirectional causality in Turkey, a unidirectional causality from economic development to female labour force participation rate in Kuwait, Oman, Egypt, the United Arab Emirates, and Yemen, and a unidirectional causality from female labour force participation rates to economic development in Jordan. Economic development prompts female labour force participation in Egypt, Kuwait, Oman, and Turkey, and reduce in the United Arab Emirates and Yemen. Female labour force participation harms the economic development in Jordan and Turkey.

Causality-Based Feature Fusion for Brain Neuro-Developmental Analysis.

Human brain development is a complex and dynamic process caused by several factors such as genetics, sex hormones, and environmental changes. A number of recent studies on brain development have examined functional connectivity (FC) defined by the temporal correlation between time series of different brain regions. We propose to add the directional flow of information during brain maturation. To do so, we extract effective connectivity (EC) through Granger causality (GC) for two different groups of subjects, i.e., children and young adults. The motivation is that the inclusion of causal interaction may further discriminate brain connections between two age groups and help to discover new connections between brain regions. The contributions of this study are threefold. First, there has been a lack of attention to EC-based feature extraction in the context of brain development. To this end, we propose a new kernel-based GC (KGC) method to learn nonlinearity of complex brain network, where a reduced Sine hyperbolic polynomial (RSP) neural network was used as our proposed learner. Second, we used causality values as the weight for the directional connectivity between brain regions. Our findings indicated that the strength of connections was significantly higher in young adults relative to children. In addition, our new EC-based feature outperformed FC-based analysis from Philadelphia neurocohort (PNC) study with better discrimination of different age groups. Moreover, the fusion of these two sets of features (FC + EC) improved brain age prediction accuracy by more than 4%, indicating that they should be used together for brain development studies.

Creating the illicit capital flows network in Europe – Do the net errors and omissions follow an economic pattern?

This paper uses the Net errors and omissions, a standard measure of illicit capital flows, to create a Europe-wide network of illicit capital flows by the rolling window Granger causality method. In the second stage, we investigate the factors that determine the creation of links among country pairs in the illicit capital flow network by Cragg’s two-stage Tobit model. Our results suggest that the process of link creation is conducive to the long-term interest rate differential, while the frequency of link occurrence is determined by the strength of trade linkages, exposure to FDI flows and differences in economic development associated with overall institutional quality and corporate tax rates.

THE IMPACTS OF CORRUPTION ON BUDGET BALANCE AND PUBLIC DEBT IN TURKEY: AN EMPIRICAL ANALYSIS

Today, maintaining stability, transparency and budget balance are main indicators for developed and emerging countries. Corruption, causing deviation from these indicators and its results are important issues that should be researched. Because, corruption changes the composition of public expenditures leading to inefficient expenditures, as well as dissolving public revenues and affecting budget balance negatively. Increase in budget deficit causes an increase in public debt stock. In this study, the effect of corruption in Turkey on budget balance and public debt is analyzed by using Johansen cointegration, VAR (Vector Autoregressive Model) and Granger Causality methods for 1995-2019 years. Empirical findings state that there is not cointegration relationship among variables and there are causalities from budget deficits and public debt burden to corruption and from budget deficit to public debt. VAR method provides evidence that budget deficit and corruption affect each other.

Assessing Granger-Causality in the Southern Humboldt Current Ecosystem Using Cross-Spectral Methods.

The southern Humboldt Current ecosystem is an important topic among researchers working on the drivers of pelagic species’ biological indicators. While sea surface temperature is believed to be a major driver in anchovies’ (Engraulis ringens) reproductive and body condition indicators, this paper shows that regional drivers such as Pacific decadal oscillation anomalies also influence these biological processes. In addition, a warm condition could trigger increased gonad development of anchovies and synchronization of body condition dynamics with local environmental conditions stemming from sea turbulence and Ekman transport. To test the statistical significance of causality between two time series and determine the direction of causality, the frequency-domain Granger-causality method is considered. Therefore, this study provides additional predictive information, derived from past data on anchovy reproductive and feeding activities. The study could be useful for researchers working on relationships of environmental conditions and pelagic species to predict biological processes’ maximum and minimum peak movements and anchovy abundance in the southern Humboldt Current ecosystem.

Single-trial EEG emotion recognition using Granger Causality/Transfer Entropy analysis

BackgroundEmotion recognition has been studied for decades, but the classification accuracy needs to be improved. New methodIn this study, a novel emotional classification approach is proposed by combining the Histogram of Oriented Gradient (HOG) method with the Granger Causality (GC) or Transfer Entropy (TE) methods. HOG extracts local valid information from the GC/TE relationship matrices and then the Support Vector Machine (SVM) is employed to classify the emotional states of stress and calm. ResultsCompared with previous studies, the classification accuracy has been greatly improved. The results of this study show that the classification based on GC or TE with HOG offers an average accuracy 88.93 % and 95.21 %, respectively. The achieved accuracy is about 12 % higher than that achieved without using HOG feature extraction. Comparison with existing method(s)Numerous efforts have been devoted to classify emotional states by extracting EEG characteristics on a single channel basis, the method developed in this study utilizes information interaction between brain channels as a feature to classify emotional states. Furthermore, this study combines HOG and relation matrices for the first time and uses image processing to extract EEG features. ConclusionOur results demonstrate the feasibility of combining TE with HOG for emotion recognition with improved classification accuracy by taking advantage of both network and gradient features. More specific features can be selected to improve classification accuracy by taking advantage of information exchanges between EEG channels directly or the extracted property of the relationship matrix based on information interactions.

Guns and Blood: A Review of Geopolitical Risk and Defence Expenditures

ABSTRACT This study evaluates the possibility of having a causal link between geopolitical risk (GPR) and defence expenditure (DE) by employing panel bootstrap Granger causality method. The results highlight the causality running from GPR to DE in China, India, and Saudi Arabia. In other words, the GPR in the form of border disputes, wars and terrorism threats can pursuade countries to increase their defence expenditure. The results we obtain for these three countries are also supported by the neoclassical model, which asserts that GPR is a significant contributor to DE. Contrary to above, there are evidences for reverse causality as well where DE is causing GPR in South Korea and Turkey. The most probable reasons for reverse causality could be the various alliances for regional security by the countries and their dependency on the import of military equipment. The results indicate an insignificant relationship between GPR and DE in Brazil, Israel, and Russia; where the DE is mainly determined by the internal political system and its contribution towards employment generation. On the basis of our results, it can be inferred that the convergence of regional interests in the form of a peaceful solution of disputes may guarantee security. Also, the formulation of the policies that are independent and isolated from the influence of external powers, can help in controlling the ever-increasing DE and GPR in these countries.