Granger Causality Test Research Articles

Towards sustainable agricultural development and food security in East Asia: The role of broad money and banking credits

The existing literature has extensively explored the role of financial development in boosting agricultural output. However, there is a lack of empirical research on the effects of broad money and banking credits on food production in East Asian countries. This study addresses this gap by examining the relationship between financial variables and agriculture in selected East Asian economies from 1995 to 2019, focusing on food security. Our findings indicate that increases in broad money and financial sector credit significantly enhance long-term food security by 0.216% and 0.846%, respectively. Additionally, we observed positive correlations between food security and agricultural inputs: a 1% increase in agricultural land, fertilizer use, and renewable energy adoption improves food security by 0.219%, 0.049%, and 0.146%, respectively. Robustness checks and Granger causality tests further validate these results. This study underscores the critical role of financial resources and agricultural inputs in food security and calls for a reassessment of credit systems to mitigate any adverse effects. These findings provide important insights for policymakers aiming to strengthen food security in East Asia.

ANALISIS INTEGRASI PASAR SPASIAL HARGA TELUR AYAM RAS MENGGUNAKAN METODE VECTOR ERROR CORRECTION MODEL

Purebred chicken eggs are one of the staple commodities whose price development continues to be monitored by the government. To maintain price stability of purebred chicken eggs, an understanding of spatial market integration is needed. Spatial market integration can show the level of closeness between markets in a region. The spatial market integration in this study was modeled using the Vector Error Correction Model (VECM) method. The VECM method was chosen with the aim of seeing the relationship between the price movement of purebred chicken eggs both in the short and long term, seeing the interaction of relationships that occur in each region and forecasting the price of purebred chicken eggs. The research objects used include the price of purebred chicken eggs in Semarang City, Salatiga City, Demak Regency, and Pati Regency in the period January 2021 to December 2022. The data used is weekly time series data that has met the stationary test at the first differential level. Based on the Johansen cointegration test on the four regions, 3 cointegrations were obtained, indicating that in each short-term period all variables adjust to each other to achieve long-term equilibrium. Granger's causality test in this study showed that not all variables have a two-way relationship. The resulting MAPE value is less than 10%, so the resulting model is very feasible to use.

MSA-GCN: Multistage Spatio-Temporal Aggregation Graph Convolutional Networks for Traffic Flow Prediction

Timely and accurate traffic flow prediction is crucial for stabilizing road conditions, reducing environmental pollution, and mitigating economic losses. While current graph convolution methods have achieved certain results, they do not fully leverage the true advantages of graph convolution. There is still room for improvement in simultaneously addressing multi-graph convolution, optimizing graphs, and simulating road conditions. Based on this, this paper proposes MSA-GCN: Multistage Spatio-Temporal Aggregation Graph Convolutional Networks for Traffic Flow Prediction. This method overcomes the aforementioned issues by dividing the process into different stages and achieves promising prediction results. In the first stage, we construct a latent similarity adjacency matrix and address the randomness interference features in similarity features through two optimizations using the proposed ConvGRU Attention Layer (CGAL module) and the Causal Similarity Capture Module (CSC module), which includes Granger causality tests. In the second stage, we mine the potential correlation between roads using the Correlation Completion Module (CC module) to create a global correlation adjacency matrix as a complement for potential correlations. In the third stage, we utilize the proposed Auto-LRU autoencoder to pre-train various weather features, encoding them into the model’s prediction process to enhance its ability to simulate the real world and improve interpretability. Finally, in the fourth stage, we fuse these features and use a Bidirectional Gated Recurrent Unit (BiGRU) to model time dependencies, outputting the prediction results through a linear layer. Our model demonstrates a performance improvement of 29.33%, 27.03%, and 23.07% on three real-world datasets (PEMSD8, LOSLOOP, and SZAREA) compared to advanced baseline methods, and various ablation experiments validate the effectiveness of each stage and module.

How green strategies and policies influence production‐based emissions, industrial development and sustainable development goals?

AbstractSustainable development is a global imperative; however, it is unattainable without a transformation of the production sector. Therefore, the study examines the impact of green strategies [Industrial innovation, Eco‐tech imports‐IMPT, and Green finances‐GFN] and policy interventions [Environmental policy‐EPY and Environmental taxes‐ETX] on production‐based emissions (PBE), industrial value addition (IVA), and sustainable development goals (SDG). The study covers the years 1990–2022 and focuses on 25 European nations. In addition, the pooled mean grouped (PMG)‐ARDL and mean group (MG) techniques were used to assess the short‐ and long‐term parameters. The country‐specific relationships are evaluated using the Bootstrap Granger Causality test. The results affirm that green strategies and policy interventions mitigated PBE and increased the IVA and SDG in Europe. However, globalization and energy use increase the PBE and IVA while impeding progress toward the SDGs. The mitigation technologies fueled with green energy (MPRE) came out as the ultimate solution, which requires green technologies and sustainable practices across all sectors. Therefore, it is suggested that governments implement eco‐friendly policies that boost GFN, reduce pollution, increase green output, and meet the Sustainable Development Goals.

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

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

Unveiling the causal nexus between Saudi’s seaborne trade and economy: evidence from an ARDL model

An economy’s primary objective is to foster high economic growth, ultimately enhancing the well-being of its citizens. Recent economic strategies have increasingly emphasized trade-oriented policies to achieve this goal. This study delves into the nexus between Saudi seaborne trade (measured in tons) and gross domestic product (GDP), aiming to determine any causal relationship between these economic variables. Using datasets from the Saudi Central Bank and the General Authority of Statistics, this research employs rigorous unit root, cointegration, and causality tests to find the intricate interplay between trade and economic growth in Saudi Arabia. The empirical analysis highlights a robust long-term relationship between Saudi GDP and seaborne trade, suggesting cointegration. Specifically, the findings reveal a significant impact of economic growth on seaborne trade, as indicated by a trade elasticity of 1.08, which implies that changes in GDP yield a substantial shift in trade. For instance, a 1% increase in GDP corresponds to an approximate 1.08% increase in trade volume. The error correction mechanism also illustrates the dynamic adjustment process: deviations of Saudi seaborne trade and economic growth from equilibrium are corrected by 61% and 43%, in the subsequent year. Furthermore, Granger causality tests unveil bidirectional causality between GDP and trade, elucidating that economic growth changes precede trade volume alterations and vice versa. Thus, fostering international trade not only stimulates income but also bolsters the domestic economy of Saudi Arabia.

The effects of rule of law, regulatory quality, and R&D on Japan's environmental sustainability

AbstractThis study investigates Japan's ecological footprint, addressing the lack of comprehensive research on the environmental impacts of globalization, trade openness, regulatory quality, and rule of law in the country. Using data from 1990 to 2021, the study explores the relationship between these variables and Japan's ecological footprint through autoregressive distributed lag (ARDL) analysis, Granger causality tests, and diagnostics to assess variable stability. The ARDL results reveal that (i) the rule of law and trade openness reduce Japan's ecological footprint, (ii) regulatory quality and globalization increase it in the long run, and (iii) research and development improve the ecological footprint in the short run. Additionally, the Granger causality test indicates a unidirectional relationship, where the ecological footprint influences all the explanatory variables. These findings highlight the importance of coordinated policies to address Japan's environmental challenges. To mitigate its ecological footprint, Japan should promote stronger legal frameworks, trade openness, and investment in research and development, while ensuring adherence to the rule of law.

Rule of Law, Regulatory Quality and investment Growth in Nigeria: An Impact Analysis

This study focus on rule of law, regulatory quality and investment growth in Nigeria employing ordinary least square method of estimation. A time series data spanning from 1997 to 2019. The economic growth was proxied with investment growth, while other variables include rule of law, regulatory quality, consumer price index, premium lending rate, trade openness, financial deepening, investment, gross investment and gross fixed capital formation. The finding of the OLS result shows that there is significant positive relationship between rule of law, regulatory quality and economic growth. Also, the result of the ECM shows that the speed of adjustment of the investment growth in the long run would be accounted for in short run by the magnitude of 27% and 40%. In the robustness check, negative significant relationship exist between rule of law, regulatory quality, investment and gross investment. Findings from granger causality test shows that there is a unidirectional causality between rule of law and economic growth, and also a bidirectional causality between regulatory quality and economic growth. Thus, improvement in the governance and institutional framework in Nigeria is suggested as policy recommendation.

Asymmetric connectedness among BSE SENSEX, INR–USD exchange rate, gold price and crude oil price: fresh evidence from nonlinear ARDL

PurposeThis paper investigates the asymmetric connectedness among the Indian stock market, crude oil price, gold price and the USD–INR exchange rate.Design/methodology/approachWe construct a nonlinear autoregressive distributed lag model that contains the four aforementioned variables. We further used the pairwise Granger causality test to identify the direction of causality.FindingsThe results verify an asymmetrical long-run co-movement between the Indian stock market index, exchange rate, international crude oil prices and gold prices. In the long run, the performance of the Indian stock market is mainly affected by both positive as well as negative shocks in the INR–USD exchange rate, positive shocks in gold prices and positive shocks in crude oil prices. Further, a feedback mechanism is observed between positive shocks in the INR–USD exchange rate and Indian stock market performance.Originality/valueThe research paper revisited the linkage among the variables using a novel methodology proposed by Shin et al. (2014).

A Research on Relationship between Tax Revenues and Social Demography: A Comparison of Bosnia and Turkey

Tax revenue is one of the common issues of both public administration and public law. The tax system is an important phenomenon that shows not only the income and cash flow of the country but also the functioning of the legal system and justice within the country. In this study, the population of Bosnia and Herzegovina and tax revenues in Turkey aimed to investigate the relationship between the demographic structure. For this purpose, the data obtained from the World Bank for the last twenty years, the gender, age ratio of the population, and some demographic variables for both countries, urban and rural population rates are associated with tax revenues. In the study, the Kolmogorov Smirnov Test was used for the normality test of data, Mann Whitney U was used for the non-normally distributed data, and the Independent Sample T-test was used for the normally distributed data. Correlation and regression analysis and Granger Causality Test were performed for both countries. According to the results of the study, changing demographic variables affect indirectly even if they are not directly influential in affecting tax revenues. Therefore, in order to ensure fairness in tax distribution and the allocation of a more effective tax distribution system, a tax system that takes into account demographic variables and evaluates regional and social differences in a better way is needed. Although Turkey to Bosnia and Herzegovina in accordance with a more comprehensive macroeconomic structure, Bosnia and Herzegovina has a more regular distribution of the tax system.

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

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

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

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

THE RELATIONSHIP BETWEEN FDI AND ECONOMIC GROWTH IN INDIA. A TIME SERIES APPROACH

Foreign Direct Investment (FDI) has gained importance after the second world war. In broader sense Foreign Direct Investment incorporates the establishment of up to date and advanced facilities, mergers and acquisitions, investing the profits earned from other overseas business activities, providing loans to other companies etc. In narrow sense the Foreign Direct Investment means investment in the country other than the investors own country. FDI can be better understood by studying the real reason that why the companies have shown interest in investing abroad rather than exporting and outsourcing their products to domestic firms. In due course of time many theories and approaches were developed to explain FDI. Although several researchers have tried to explain the phenomenon of FDI, we cannot say there is a generally accepted theory, every new evidence added some new elements and criticism to the previous ones. FDI in India is playing a noticeable role in the growth and development of the country. India is one of the important countries of the world which is able to receive substantial amount of FDI. The present study has made an effort to see the relationship between the FDI and Economic growth in India. GDP has proxies for economic growth. First the Johansen cointegration test result shows the long run relationship between variables and the Granger causality test result exhibits that there is unidirectional Granger causality between FDI and GDP. Next the Multiple Regression model result presents that the estimated coefficients on FDI & exports have a positive relationship with GDP. Therefore, it is statistically revealed that FDI and Exports are instrumental in influencing the level of economic growth in India. India is having certain drawbacks like Weak infrastructure, Complicated tax structure, Restrictive labor laws, Bureaucracy and Corruption. Unless and until these challenges are met with, India will never compete with China regarding FDI inflows. KEYWORDS: Foreign Direct Investment, Gross Domestic Product, Economic Growth.

Rethinking diversification: Assessing the impact of energy concentration on climate change in rentier and non-rentier countries

Energy diversification, a critical strategy for environmental protection, has been surprisingly understudied in academic discourse. This paper seeks to address this gap by investigating the relationship between energy diversification and climate change in 76 countries from 1990 to 2020. We employ panel data techniques, and the system generalized method of moments (SYS-GMM) to analyze the impact of energy diversification, as measured by the Herfindahl-Hirschman Index, on CO2 emissions. Additionally, control variables such as economic growth, trade openness, foreign direct investment, information and technology adoption, natural resource rents, and urbanization are included in the model. The analysis is re-estimated for various country groupings based on income level and resource dependence (rentier vs. non-rentier). The findings reveal a significant positive association between energy concentration and CO2 emissions across all country groups, with the most pronounced impact is observed in middle-income, resource-rich economies (i.e., middle-income rentiers). The study also highlights differing effects of diversification on CO2 emissions between high-income and middle-income nations, which reflects the prevalence of non-rentier economies among high-income countries and rentier economies in middle-income countries. The causal relationship between CO2 emissions and energy diversification in different groups of countries is tested using the panel Granger causality test. The results reveal strong bidirectional causality between CO2 emissions and energy diversification in all country groups analyzed, except for middle-income rentier countries. By elucidating the multifaceted relationship between energy diversification and climate change, this study aims to provide valuable policy recommendations to advance sustainable development and environmental management.

Financial Development's Role in the 2030 Sustainable Development Agenda: Asian Countries

The purpose of achieving sustainable development goals (SDGs) requires the mobilisation of finance and its alignment with sustainability principles. This study intends to examine the influence of financial development on the attainment of Sustainable Development Goals (SDGs) in an economy. Approach - The authors conduct an analysis of a sample comprising 35 Asian countries, focusing on their SDG (Sustainable Development Goals). Patterns and indicative indicators of the Sustainable Development Goals (SDGs). A probit model is used to analyse the effect of Financial development impacting the trajectory of the Sustainable Development Goals. Then, a pairwise Granger causality test is used for Examining the relationship between the Sustainable Development Goals and financial development The results suggest that there is a favourable relationship between financial development and the advancement towards the Sustainable Development Goals. The test for causation There is a reciprocal relationship between financial development and industry, infrastructure, and innovation. Enhancing financial growth and promoting sustainable cities and communities, as well as fostering financial development and taking action on climate change, There is a one-way relationship where gender equality influences financial progress. Research limitations/ramifications - The findings also have implications for the government of a nation as the privately-owned enterprises.

Comparison of Trends in Sustainable Energy Development in the Czech Republic and Poland

The contemporary process of economic development necessitates a heightened focus on matters of sustainability, with a particular emphasis on sustainable energy policy. This is of paramount importance for the protection of the natural environment and the achievement of long-term economic growth. In the context of countries such as the Czech Republic and Poland, which have historically relied on high-carbon energy sources, the transition to a more sustainable energy system represents a significant challenge. The objective of this paper is to undertake a comparative analysis of the trends in energy sustainability in the Czech Republic and Poland from 2017 to 2021, with a particular focus on key performance indicators. The analysis, based on data from the OECD database, revealed notable discrepancies in the rate of change between the two countries, with Poland exhibiting a more pronounced surge in the proportion of renewable energy sources (RES). A Student’s t-test confirmed the existence of statistically significant differences in key indicators between the Czech Republic and Poland, thereby underscoring the diverse challenges that both countries encounter in their pursuit of sustainable energy development. The Granger causality test was employed to ascertain whether variables exhibit temporal relationships that may suggest potential correlations. However, it is important to note that this test does not prove direct causality, but rather indicates that the variables are related at a specific point in time. Interpretation of the results must be undertaken with caution, as the test does not account for the full complexity of relationships between variables, including external factors and structural changes in the economy. Meanwhile, the LMDI decomposition analysis identified the principal drivers of alterations in CO2 emissions. The findings indicate that, despite advancements in sustainable energy development, Poland and the Czech Republic are confronted with distinctive challenges that necessitate the implementation of tailored policy responses. It is therefore recommended that further investment in renewable energy and the modernisation of energy infrastructure be made in order to achieve long-term sustainability goals.

Influence of green ICT and socioeconomic factors on sustainable development: Evidence from Chinese provinces

We examine the impact of green information communication technology, while considering the conditioning role of other seminal factors. Using panel quantile-quantile granger causality testing and method of moments quantile regression for Chinese provinces for 2000–2019, we find that green information communication technology promotion, human capital, and urbanization positively impact green growth, while globalization, energy intensity, and carbon emissions are not significant. Results highlight the importance of green information communication technology for sustainable development.

Determinants of environmental sustainability in the United States: analyzing the role of financial development and stock market capitalization using LCC framework

This research investigates how the USA's load capacity factor (LCF) has been impacted by trade openness, financial development, stock market capitalization, and industrialization over the period 1990–2022. This study also tests the “Load Capacity Curve (LCC)” hypothesis. Various unit root tests were conducted to determine the stationarity of the dataset, revealing that the variables are free from unit root problems and exhibit mixed orders of integration. The “Autoregressive Distributed Lag (ARDL)” bounds test confirmed co-integration among the variables. The results from the ARDL model validated the existence of the LCC hypothesis in the USA. The findings also demonstrated that industrialization is positively correlated with LCF, whereas financial development, stock market capitalization, and trade openness are negatively correlated with LCF. To ensure the robustness of the ARDL estimations, the study also employed several regressions, all of which confirmed the validity of the ARDL results. Additionally, pairwise Granger causality tests revealed unidirectional causal relationships between GDP and LCF, stock market capitalization and LCF, and industrialization and LCF, while no causal relationships were found between financial development and LCF and trade openness and LCF. The findings advocate that the USA should advance eco-friendly industrial practices to ensure environmental sustainability, alongside regulated financial markets and institutions that mandate the utilization of green investments.

Harbor in the storm: How Bitcoin navigates challenges of climate change and global uncertainties

Understanding Bitcoin's (BTC) performance and hedging effects under various uncertainties is crucial. This study combines time-varying Granger causality (TVGC) tests and wavelet time-varying parameter vector autoregression (TVP-VAR) methods to examine the relationship between Bitcoin (BTC) and climate uncertainty (SOI), geopolitical risk (GPR), economic policy uncertainty (EPU), crude oil volatility (OVX), and market volatility (VIX) across both time and frequency domains, from December 19, 2017, to October 16, 2023. The results show that in recent years, the long-term impact of SOI on BTC, as well as political and economic uncertainties, is hard to ignore, but BTC cannot serve as a safe-haven asset against SOI. Wavelet decomposition indicates that during extreme events, BTC exhibits a leading positive co-movement with EPU, GPR, VIX, and OVX in the long term, showing potential as a hedging asset. However, TVGC and wavelet decomposition spillover results further suggest that BTC's hedging capacity against different economic and political uncertainties may be limited during certain extreme events.

Dynamics of inflation in emerging markets: Is it local or global?

This paper investigates inflation dynamics in emerging markets. We apply a multi-country model (GVAR) and a time-varying Granger causality test (TVGC) to examine the drivers of inflation in Brazil, China, India, Indonesia, South Africa, and Türkiye over the period 1980Q1-2022Q4. The aim is to ascertain whether these drivers are locally or globally determined and vary over time. The GVAR model results indicate that local and global factors drive domestic inflation, collectively contributing to inflationary pressures in emerging countries. The TVGC results, on the other hand, provide strong evidence of the causality from local and global factors to domestic inflation. Local factors appear to have a stronger influence than global ones, suggesting that local dynamics play a more significant role in explaining inflation in these countries. Consequently, globalization and economic/trade integration, seen as tools to suppress inflation, can turn into factors that cause inflation in the face of a supply shock such as the one caused by the pandemic.