Granger Causality Relationship Research Articles

Investigating the Causal Relationship between Renewable Energy Consumption and Life Expectancy in Turkey: A Toda-Yamamoto Causality Test

This study examines the causal relationship between renewable energy consumption and life expectancy in Turkiye using the Toda-Yamamoto causality test. By analyzing data from 1990 to 2019, the study explores the relationship between these variables. The results of the Toda-Yamamoto causality test indicate that there is no Granger causality relationship from renewable energy consumption to life expectancy, indicating that renewable energy consumption does not have a significant impact on life expectancy in Turkiye. However, the study found a Granger causality relationship from life expectancy to renewable energy consumption, suggesting that improving life expectancy could lead to an increase in renewable energy consumption in Turkiye. This study is significant as it provides insights into the relationship between renewable energy consumption and life expectancy in Turkiye. The results highlight the importance of considering factors other than renewable energy consumption when examining public health outcomes. The study's findings can inform policymakers in developing energy policies that prioritize public health outcomes and promote sustainable energy practices.

Kripto Paralar Arasında Getiri ve Risk Açısından Nedensellik İlişkisi

Çalışmanın temel amacı; son on yıla damga vuran kripto paralar arasındaki getiri ve risk açısından nedensellik ilişkisini tespit etmektir. Bu amaçla piyasa değeri en yüksek 10 kripto paradan en fazla verisi bulunan Bitcoin, Ethereum, Litecoin, Stellar, Ripple arasında 10 model oluşturulmuş ve her model, Granger nedensellik ve Hafner-Herwatz varyansta nedensellik testleri test edilmiştir. Çalışmada 23 Şubat 2017 ile 18 Haziran 2021 tarihleri arasındaki günlük verilerden oluşan 1577 gözlem kullanılmıştır. Nedensellik analizi sonuçlarına göre seçili kripto paralar arasında ortalamada Ethereum - Litecoin hariç diğer değişkenler arasında Granger nedensellik ilişkisi, varyansta ise Bitcoin - Ethereum ve Bitcoin - Litecoin hariç diğer değişkenler arasında varyansta nedensellik ilişkisi tespit edilmiştir.

How does tourism affect the urban heat island effect? A case study of the tourism heat footprint in Macao.

This paper uses the tourism heat footprint (THF) and a structural vector autoregressive model to investigate how tourism has affected the urban heat island effect in Macao, a typical urban tourism destination. The dynamic relationships between the THF, heat island intensity (HII), and quarterly average temperature (QAT) are investigated. The impulse response function and variance decomposition analysis are used to assess if a long-term causal relationship exists between the three indicators. The results show the following. (1) The hotel industry in Macao is the source of energy consumption and heat release. (2) A Granger causality relationship exists between the THF and QAT but not between the THF and HII. (3) The interaction effect between the growth rate of the THF and QAT is manifested as shocks with the same frequency and regular periodic fluctuations. (4) The heat island effect of this tourism destination is reflected in an increase in local temperature due to the energy consumption and heat release by tourists. Based on the results, policy implications for a sustainable tourism city are provided.

Climate Change: Linear and Nonlinear Causality Analysis

The goal of this study is to detect linear and nonlinear causal pathways toward climate change as measured by changes in global mean surface temperature and global mean sea level over time using a data-based approach in contrast to the traditional physics-based models. Monthly data on potential climate change causal factors, including greenhouse gas concentrations, sunspot numbers, humidity, ice sheets mass, and sea ice coverage, from January 2003 to December 2021, have been utilized in the analysis. We first applied the vector autoregressive model (VAR) and Granger causality test to gauge the linear Granger causal relationships among climate factors. We then adopted the vector error correction model (VECM) as well as the autoregressive distributed lag model (ARDL) to quantify the linear long-run equilibrium and the linear short-term dynamics. Cointegration analysis has also been adopted to examine the dual directional Granger causalities. Furthermore, in this work, we have presented a novel pipeline based on the artificial neural network (ANN) and the VAR and ARDL models to detect nonlinear causal relationships embedded in the data. The results in this study indicate that the global sea level rise is affected by changes in ice sheet mass (both linearly and nonlinearly), global mean temperature (nonlinearly), and the extent of sea ice coverage (nonlinearly and weakly); whereas the global mean temperature is affected by the global surface mean specific humidity (both linearly and nonlinearly), greenhouse gas concentration as measured by the global warming potential (both linearly and nonlinearly) and the sunspot number (only nonlinearly and weakly). Furthermore, the nonlinear neural network models tend to fit the data closer than the linear models as expected due to the increased parameter dimension of the neural network models. Given that the information criteria are not generally applicable to the comparison of neural network models and statistical time series models, our next step is to examine the robustness and compare the forecast accuracy of these two models using the soon-available 2022 monthly data.

Defense investment and economy: a study on the causality relationship

The effect of defense expenditure on economic growth is the subject of many debates. This study investigates the relationship between defense expenditure and economic growth applying Granger causality test. To do this, data of military expenditure, gross domestic product, and gross capital formation from six countries are analyzed. They are: Brazil, China, United States of America, France, United Kingdom and Russia. The findings of the study indicate Granger causality relationship for some of these countries.

Dampak Jumlah Uang Beredar, Nilai Tukar Rupiah dan Suku Bunga Bi Rate Terhadap Inflasi di Indonesia setelah Krisis Global 2008

This study aims to determine how the response and how much the contribution of the Money Supply, Rupiah Exchange Rates and BI Rate on Inflation in Indonesia After the 2008 Global Crisis. The data used in this study are time series data in 2008.1 to 2018.12. This research uses quantitative methods and analyzed using Eviews 8, using the Vector Autoregression (VAR) method. The results of this study indicate that there is no cointegration on the Amount of Money Supply, Rupiah Exchange Rates and BI Rate on Inflation in Indonesia after the 2008 Global Crisis. In the granger causality test results, if we use the null hypothesis with a significant level of ten percent, we get a granger causality relationship namely: Exchange rates against inflation and Exchange rates against JUB. But if we use the null hypothesis with a significant level of five percent, a granger causality relationship is obtained, namely inflation against the BI rate and the BI rate against the exchange rate. Analysis using the Impluse Response Function shows that JUB tends to give a negative response to inflation. Whereas the exchange rate and the BI rate tend to provide a positive response to inflation. Analysis using Variance Decomposition shows that the BI rate has the largest contribution in the formation of inflation that is far greater than the JUB and Exchange Rates. The relationship between the BI rate and inflation shows the importance of monetary policy to control the rate of inflation in Indonesia.

Forecasting the natural gas demand of Türkiye: A deep learning approach

Natural gas is an important input for the production of electricity and other forms of energy. The aim of this study is to investigate the relationship between the natural gas consumption, population and the gross domestic product of Türkiye. For this purpose, deep learning methods are utilized for the modelling of the natural gas consumption for the data covering the period of 1982-2021. First of all, the data required for the modelling are taken from the official sources and then the Granger causality relationship among these variables are studied together with the seasonal and trend decomposition. After assessing the nature of the data that contain high seasonality and nonlinearity, a deep learning network is developed in Python programming language. It is visually demonstrated that the developed deep learning model can be successfully used to describe and forecast the natural gas consumption dependent on the population and the gross domestic product. The performance of the developed deep learning model is also verified using the performance metrics such as the coefficient of determination and the mean absolute percentage error. The developed model is shown to be useful for energy planners and for economists dealing with the energy pricing.

On the dynamic relationship between transaction volume and returns: evidence from the cryptocurrency market

PurposeUsing vector autoregressive modelling (VAR) and Granger causality tests, this paper attempts to empirically investigate the dynamic relationship between return and volume of transactions of two main cryptocurrencies: Bitcoin and Ethereum.Design/methodology/approachBased on a generalized autoregressive conditional heteroskedasticity (GARCH) model with a transaction volume parameter in the conditional volatility equation.FindingsThe results provide empirical evidence of a positive contemporaneous relationship between the variation in transaction volume and the daily return of Bitcoin and Ethereum. The results also show that the conditional volatility of the returns is affected by the past volatility, which implies weak-form inefficiency for both Bitcoin and Ethereum markets. The results of the VAR model, testing Granger causality, indicate that the volume of transactions Granger-Causes Bitcoin and Ethereum returns. Furthermore, the findings show a Granger causal relation from returns to volume.Originality/valueThis result suggests that cryptocurrency returns can predict transaction volumes and vice versa.

CAN A NEW INFRASTRUCTURE PLAN REALLY PROMOTE THE DUAL CIRCULATION STRATEGY IN CHINA?

This study aims to provide an answer to the question: “Can a new infrastructure plan promote the Dual Circulation Strategy in China?” Using provincial-level panel data for 31 provinces from 2005 to 2020, this study estimates a five-variant panel vector autoregressive model for the national level, eastern region, central region and western region, respectively, and conducts panel Granger-causality tests and impulse response analysis. The primary finding of this study is that there is a bi-directional Granger-causal relationship between the growth rate of new infrastructure investment and each of two variables representing internal circulation and each of two variables representing external circulation at the national level. According to the impulse response analysis, all variables representing either internal or external circulation have a positive impulse response to an increase in the growth rate of new infrastructure investment, except for trade openness. This study thus confirms that the new infrastructure investment can promote the Dual Circulation Strategy that has been a new guide for China as it moves toward a high-quality economic development stage.

Causal relationships between cryptocurrencies: the effects of sampling interval and sample size

Abstract For this paper, the relationship between seventeen popular cryptocurrencies was analyzed by multivariate Granger causality tests and simple linear regression, using data spanning the period 1 September 2020 to 8 December 2021. The novelty of this work is that it studies the effects of sampling interval and sample size in cryptocurrency markets, which can yield significantly different results. Minute-by-minute, hourly and daily data were collected to examine the Granger causality relationship between cryptocurrencies. It was found that all the currencies demonstrated a significant causality relationship when high frequency (such as minute-by-minute) data was used, in contrast to hourly and daily data. The bigger the sample size, the higher the probability of rejecting the null hypothesis. Hence, the null hypothesis for the Granger causality test can be rejected for minute-by-minute time series data because of too large a sample size. Granger causality test results for hourly and daily data indicated that Bitcoin, Ethereum Classic, and Neo were leading indicators among the cryptocurrencies included in the research. In addition, according to simple linear regression analysis, the short term marginal effect of Bitcoin plays an important role by creating significant impacts on other cryptocurrencies.

Synergistic Interconstruction of the Green Development Concept in Chinese Rural Ecological Agriculture

The development of industrialization has overwhelmed an ecological environment damaged by the modern agricultural method. The development of a circular economy, which focuses on green development while balancing the development of agricultural production capacity, has led to the emergence of ecoagriculture. This study examines the impact of the green development concept on farmers’ income, using the practice of the green development concept of ecoagriculture in Guangdong province, China, as an example. The study uses the entropy method to verify whether the green development concept advocated by top-down policies genuinely benefits farmers’ living standards. The study results are as follows. First, the green development concept’s application in Guangdong province’s agricultural production and the two-way granger causality relationship between farmer income and food production interact. Second, chemical fertilizers and pesticides have played an important role in promoting the development of agricultural production. Third, in the long run, the greater the use of chemical fertilizers and pesticides will reduce farmers’ incomes. In summary, from the perspective of sustainable development, green development will promote increased income for China’s farmers and improve rural development. It is essential to implement the concept of green development in the process of rural modernization.

Factors Influencing Foreign Direct Investment Inflow in Somalia

Purposes: The last four decades, the value of foreign direct investment (FDI) inflow in Somalia has fluctuated between $339,000,000 in 2016 and $43,390,000 in 1970. Thus, this research investigated the factors influencing foreign direct investment (FDI) inflows in Somalia. Over the period from 1980-2017 and data are obtained from the World Bank. Design/ Methodology/ approach: this study used The Vector Auto regression (VAR) model. The econometric methodology to be utilized includes the unit root test for used Augmented Dickey-Fuller (ADF), co-integration test, Johansen integration test and diagnostic test includes serial correlation, normality, heteroskedasticity and AR root test. Furthermore, they are also utilized for VAR Granger causality tests. Findings: The findings of this paper indicated unit root test showed that all variables except external debt are not stationary at the level but become stationary after first differencing at the 10% level of significant. The co-integration test indicates the relationships between variables are integrated. The Granger-causality test shows only one-way Granger-causality relationships from FDI to GDP, import and export variables or so-called unidirectional Granger causality. Moreover, impulse response function indicates results all variables are positive related in the short run and long run except for imports which is negative related with FDI. Although, GDP, imports and inflation are significant to FDI, but export and external debt are insignificant to FDI. Therefore, this research concludes that FDI influences economic growth in Somalia. Research limitations/ implications: Although this research has expended and evolved prior studied various respects, a comprehensive and systematic time-series study on FDI and its determinants in Somalia would involve more capitals than had been made available for this study. There are still a number of specific constrains to be noted on the investigating FDI and its determinants in this paper, some factors such as political and macroeconomic instability, human capital, infrastructure and corruptions are not considered owing to data availability. Moreover, it’s recommended that future studies could improvement widely and update research in FDI.

Time-varying effects of fuel prices on stock market returns during COVID-19 outbreak

This article explores the impact of fuel price movements on the stock market return of 2020 during the COVID-19 disruptions. In doing so, a monthly data of seven selected stock market indices representing developed and emerging economies globally was used for analysis. The study used a time-varying parameter VAR model to examine a time-varying causal association between oil prices and stock market returns and a novel quantile-causality approach to capture the fluctuations of these markets under COVID-19's varying market conditions. The study further utilises the entropy transfer approach to capture the Granger-causal relationship in the presence of nonlinearities of the data series. The results indicate a high information flow from fuel prices to the FTSE-100, Pacific, and European stock indicies, but not the other way round. The results show that, for the FTSE-100 and the European region, there is a two-way information flow between equities and natural gas, and vice-versa. However, a one-way information flow was established from the stock market to the Pacific and emerging economies.

Causality between Selected Energy Companies’ Price Indexes and Barel Oil Prices

Energy production and consumption have an important place in the world. Due to the increase in demand, it reveals the result of the valuation of the companies in this sector. The main purpose of this study is to analyze the relationship between brent oil prices in the world and the index prices of energy companies, which are among the world's most important and top 10 companies. The research covers the period between January 2011 and July 2022. The time series was created by considering the data in the selected time period on a monthly basis. Co-integration analysis was applied to the series and the relationship between the variables was tried to be determined. Short-term relationships were examined by applying the VAR model. At this stage, causality was carried out with Granger causality analysis. As a result of the analysis, it was concluded that brent oil prices, which were formed as a result of events in the world, had an effect on the index prices of two important energy companies, Exxon Mobile and Gazprom. In other words, there is a causal relationship between these variables. This bilateral causality relationship between Brent oil prices and Exxon Mobile is realized unilaterally with the other two energy companies. In short, there is a Granger causality relationship between these variables.

The essential role of Russian geopolitics: A fresh perception into the gold market

This paper explores the interrelation between the geopolitical situation in Russia and the international gold market and further investigates whether Russian geopolitics can stimulate the gold market. We perform the bootstrap sub-sample method to obtain the Granger causal relation between Russian geopolitical risk (RGR) and gold price (GP). The empirical conclusions evidence positive and adverse impacts from RGR to GP exist. The positive influences indicate that Russia's deteriorating geopolitical environment may boost the international gold market. However, the adverse effects from RGR to GP cannot support this view, mainly due to the economic and non-Russian geopolitical factors. The negative influence conflicts with the intertemporal capital asset pricing model (ICAPM), which assumes that GP can be positively affected by RGR. Conversely, there is positive effect of GP on RGR, revealing that gold market could be viewed as a forward-looking indicator to mirror RGR. In the intensified war between Russia and Ukraine, these conclusions benefit the public and related countries by reducing losses by optimising investment and adjusting gold reserves, as well as guaranteeing resident health, national security and peaceful development through a more comprehensive analysis of RGR.

Economic Impacts of Cardiovascular Diseases: An Econometric Evaluation in Turkey.

The study aimed to determine the impact of the burden of cardiovascular diseases on Gross Domestic Product (GDP) in Turkey. GDP was taken as the dependent variable and the burden of cardiovascular diseases was taken as the independent variable. The variables were analyzed within the frame of Panel Data Analysis. Significant relationships were found between GDP and burden of cardiovascular diseases. The unidirectional Granger causality relationship was determined from burden of cardiovascular diseases to GDP and revealed that they acted together in the long term. The analysis that made with using econometric regression models revealed that generally 1 unit increased in per capita of cardiovascular diseases decreased GDP by between 477651.8 - 624485.6 units (PPP$). The empirical results of the study revealed that the cardiovascular disease burden was one of the reasons for the decrease in GDP and their economic effects would continue in the long term in Turkey. From this perspective establishing and implementing policies to improve the burden of cardiovascular diseases in Turkey could be an important approach for the economic development.

Digital Space of Regions: Assessment of Development Factors and Influence on Socio-Economic Growth

The heterogeneity in the development of the digital space of Russian regions is due to the action of many factors related to the overall level of socio-economic well-being. The formation and expansion of digital space, too, has an impact on the socio-economic development of regions. The hypothesis of the study is an assumption that there is a two-way relationship between the digital space formed in the regions and the socio-economic indicators of their development. The purpose of the study is to identify the specifics of the formation of digital space in the regions, to assess the factors of its development and the cause-and-effect relationship with the indicators of socio-economic development of the territories. The article presents a methodological approach, the feature of which is the systematic use of methods of spatial autocorrelation analysis, methods of regression analysis on panel data and Granger causal relationship testing. As a result of this study, three groups of regions were identified according to the level of the development of digital space and the key factors of its development were identified. In the first group of regions, the high level of digital space development is due to the active use of advanced production technologies and a significant volume of GRP. In the regions of the second group, which included the leading production centers of the country, the development of the digital space is influenced not only by the economic condition of the territories, but also by their fiscal security. The development of the digital space in the regions of the third group, which are characterized by a weak level of the development of digital infrastructure and its use, is significantly influenced not only by the volume of the GRP produced and the budgetary provision, but also by the turnover of retail trade and the active use of advanced technologies. The study of cause-effect relationships using the Granger test allowed us to establish an inverse impact of the digital space on the socio-economic development indicators of the three groups of regions, thus confirming the hypothesis put forward. The used methodological approach and modeling results can be used to find optimal mechanisms for the development of the regions' digital space in order to ensure balanced socio-economic development.

Green Finance and Sustainable Development: Exploring Dynamic Causal Links and Global Implications

This research investigates the potential causal relationship among sustainable development and green finance on a world level, using data from 2011 to 2023. In this study, the researcher utilized the bootstrap rolling-window Granger causal relationship test to investigate the contributions of different stakeholders to sustainable development by participating in green finance initiatives. The results of the analysis demonstrate that green finance has a positive impact on sustainable development across different time periods. While green finance represents a crucial financing mechanism for advancing sustainable development goals, its direct influence on SD remains unproven. Additionally, this analysis identifies distinct causal relationships in different sub periods, emphasizing the importance of context-specific causality. Moreover, in specific contexts, the direction of causality holds significant implications. Therefore, we propose that governments and international organizations play a pivotal role in guiding high-quality green investments and implementing risk mitigation strategies within the established system framework. Furthermore, enhancing the standards for categorizing green finance, developing comprehensive evaluation systems, and promoting transparent information disclosure related to GF can foster greater contributions by countries to sustainable development initiatives.

Does the structure of state budget matter for Egypt’s fiscal deficit? An empirical investigation using an ARDL bounds testing approach

ABSTRACT The current study contributes to the existing literature by investigating the relationship between structure of the State budget and fiscal deficit-to-GDP ratio in Egypt over the period 1981/1982–2020/2021. The dynamic relationship between the examined variables is tested using an Autoregressive Distributed Lag (ARDL) bounds testing approach to cointegration. The study finds an empirical evidence that supports the hypothesis that budget structure matters for fiscal deficit in Egypt. A higher share of investment expenditure in total government expenditure is correlated with a lower fiscal deficit-to-GDP ratio in the long- and short-run. The share of taxes in total government revenue is found to be negatively (positively) associated with the fiscal deficit-to-GDP ratio in the short-run (long-run). In addition, the current account balance is found to be negatively correlated with the fiscal deficit, in the long-run, which supports the ‘twin deficits hypothesis’ and the ‘current account targeting hypothesis’. Furthermore, the study finds a unidirectional Granger causality relationship that runs from fiscal deficit to government investment expenditure.

The nexus of carbon dioxide emissions, economic growth, and urbanization in Saudi Arabia

Saudi Arabia has implemented its ambitious and comprehensive national strategy, i.e., Saudi Vision 2030, to achieve major economic, social, and environmental objectives. The main aim of this paper is to study the Granger causality relationships between economic growth, environmental degradation, and urbanization in Saudi Arabia over the period from 1985 to 2019. At first, Augmented Dicky-Fuller (ADF) and Phillips-Perron (PP) tests were applied in order to check the stationarity of the panel time-series data. Since the data were of mixed order of integration I(0) and I(1), the Autoregressive Distributed Lag (ARDL) framework was employed to perform the statistical analysis. Then, the short- and long-run relationships were evaluated using the bounds test for cointegration applied on the Error Correction Models (ECMs) for GDP, CO2 emissions, and urbanization as the dependent variables. Furthermore, the direction and significance of causality were estimated in the ARDL/ECM framework. In addition, the Environmental Kuznets Curve (EKC) hypothesis was examined for the sample data. To assess the generalization capability of the findings in this study, robustness and diagnostic tests were applied. In the long-run, the empirical findings indicate that 1% increase in economic growth Granger caused 0.15% increase in CO2 emissions and 0.006% decrease in urbanization. Whereas 1% increase in urbanization Granger caused 2.5% increase in the economic growth. In the short-run, a unidirectional causal relationship existed from economic growth to both CO2 emissions and urbanization with 1% increase in GDP Granger caused 0.3% and 0.004% increases in CO2 emissions and urbanization, respectively. Finally, policy recommendations were presented in light of the Saudi Vision 2030.