Causality Test Research Articles

Investigating the Nexus Between GHG Emissions and AFOLU Activities: New Insights from C-Vine Copula Approach

AbstractThe greenhouse gas (GHG) emissions from agriculture, forestry, and other land use (AFOLU) account for more than 10% of the total GHG emissions in Iran. To reduce the environmental impact, assessments of Iran’s GHG emissions status are critical for identifying the national policies to achieve Sustainable Development Goals (SDGs) in the bio-based industry. However, there is no study exploring the dependency between AFOLU and GHG emissions in Iran by using the Vine Copula approach. Hence, the study aims to examine the causality direction and correlation structure among selected horticulture, farming crops, livestock, and poultry products and carbon dioxide (CO2), nitrogen dioxide (N2O), and methane emissions (CH4) in the Iranian agriculture sector over the period 1961–2019, to determine which crops or products are more responsible to deteriorate the environment. The empirical strategy used a C-Vine Copula model to measure the correlations together with the Granger causality (GC) test to analyze the causality links. According to the empirical findings, several crops and products are the sources of emissions. Rice and vegetable cultivations, as well as meat and milk products (Kendall’s τ values of 0.37, 0.33, 0.31, and 0.31, respectively), are the leading sources of CH4 emissions. Legumes, eggs, maize, rice, and milk enhance N2O emissions, while CO2 emissions are caused by apple, potato, and apricot crops (Kendall’s τ values of 0.22, 0.18, and 0.16, respectively). Finally, based on the findings, policy implications are offered.

Does capital efficiency influence economic growth in Bangladesh? Application of the Harrod-Domar model

PurposeThe main purpose of this paper is to analyse the influence of capital efficiency on the economic growth of Bangladesh using the Harrod-Domar (H-D) model.Design/methodology/approachWe use annual data from 1980 to 2019 for this paper. Three steps are taken in the data analysis. First, to check the existence of a unit root, we use the augmented Dickey-Fuller (ADF) test and to determine co-integration among the variables, we use the Johansen-Juselius co-integration test. Next, for long-run estimation, we use the dynamic ordinary least square (DOLS) estimator. The sensitivity of the long-run estimations is further checked by the fully modified OLS (FMOLS) and autoregressive distributed lag (ARDL) estimators. Lastly, we use the Granger causality test to determine the long-run causality among the variables.FindingsThe long-run co-integration test validates the co-integrating relationship among the variables. DOLS estimations reveal that the economic growth of Bangladesh is negatively associated with the incremental capital output ratio (ICOR), validating the notion that capital efficiency matters for achieving higher economic growth. On average, an increase in ICOR by a unit tends to reduce economic growth in the long term by 0.75 percent. Our results also reveal no significant relationship between savings and economic growth when the model is extended. Finally, causality results indicate unidirectional causality between ICOR and economic growth.Practical implicationsBased on the results obtained, we argue that the enhancement of capital productivity could bring efficiency because ICOR is an inverse of capital productivity. Since Bangladesh’s capital productivity is considerably low compared with other neighbouring countries, it is suggested that firms should gradually move towards technological advancement and enhance economies of scale, etc. in the long run. Moreover, policies in favour of continuous skill development programmes could be highly effective in increasing capital productivity given that capital follows a vintage structure.Originality/valueThis is the first paper to analyse the economic growth pattern of Bangladesh using the traditional H-D model by incorporating variables such as savings and ICOR and also by relaxing the assumption of time-invariant (i.e. fixed) data of the variables. Moreover, this paper extends the traditional H-D empirical model by introducing key indicators and time breaks for Bangladesh’s economy through a stepwise regression process.

A comparative study of the nexus of natural resource investment in national economies: Effects on cultural communication and economic growth

This article provides a comparative analysis of the complex interplay between cultural exchange, national economic development, and investments in natural resources. This research aims to examine how rising Asian countries' economies have grown as a result of a green economic rebound from 2000 to 2020. The study makes use of the D-H causality test methodology as well as the CCE-MG It assesses the varied impacts of resource investment on employment, GDP growth, and income distribution by looking at case studies and empirical data. It examines how investments in natural resources impact economic pathways and cultural communication dynamics. It sheds light on the complex effects of resource investment on cross-cultural understanding and economic development via case studies and empirical data. It also delves into how resource investments impact economic development plans and how cultural communication patterns interact. The results highlight the need to comprehend these relationships for efficient policymaking and sustainable development.

Spring Meteorological Drought over East Asia and Its Associations with Large-Scale Climate Variations

East Asia is a region that is highly vulnerable to drought disasters during the spring season, as this period is critical for planting, germinating, and growing staple crops such as wheat, maize, and rice. The climate in East Asia is significantly influenced by three large-scale climate variations: the Pacific Decadal Oscillation (PDO), the El Niño–Southern Oscillation (ENSO), and the Indian Ocean Dipole (IOD) in the Pacific and Indian Oceans. In this study, the spring meteorological drought was quantified using the standardized precipitation evapotranspiration index (SPEI) for March, April, and May. Initially, coupled climate networks were established for two climate variables: sea surface temperature (SST) and SPEI. The directed links from SST to SPEI were determined based on the Granger causality test. These coupled climate networks revealed the associations between climate variations and meteorological droughts, indicating that semi-arid areas are more sensitive to these climate variations. In the spring, PDO and ENSO do not cause extreme wetness or dryness in East Asia, whereas IOD does. The remote impacts of these climate variations on SPEI can be partially explained by atmospheric circulations, where the combined effects of air temperatures, winds, and air pressure fields determine the wet/dry conditions in East Asia.

Reshaping energy horizon of Iran: investigating economic sanctions, export diversification, and environmental resilience.

Employing robust methodologies, including principal component analysis, autoregressive moving average, Fourier bootstrap dynamic autoregressive distributed lag, error correction model, and the Breitung-Candelon spectral Granger causality test, this study scrutinizes the impact of export diversification (EXD) on Iran's ecological footprint (EF) from 1997 to 2020, considering economic sanctions (ESI), trade openness (TOP), energy consumption per capita (ECpc), globalization (KOF), and real GDP per capita (RGDPpc). Findings consistently affirm a positive environmental impact of EXD, revealing a nuanced temporal pattern. Notably, the short-term impact (- 0.645) is more pronounced than its long-term counterpart (- .020). Increased industrial activities due to globalization (10% rise) lead to 4.26% and 1.64% EF degradation in the long and short term. Conversely, due to Iran's heavy reliance on fossil fuels, a 10% rise in ECpc correlates with 1.63% and 3.81% long- and short-term environmental quality reduction. ESI demonstrates a dual impact, improving short-term environmental quality but contributing to long-term degradation. Frequency-domain causality analysis highlights EXD and KOF as short- and long-term causes of EF, ESI, and TOP as medium- to long-term causes and RGDPpc as a long-term cause. These findings emphasize the need for sustainable policies, stringent environmental standards, and a balanced approach to fostering economic growth while preserving the environment.

An ARDL Method for Evaluating Bangladesh’s Economic Growth Concerning Female Secondary Education

The purpose of this study is to investigate the association between Bangladesh’s economic growth and female secondary education. For this reason, the effects of female secondary education, female labour force participation rate, and female fertility rate on Bangladesh’s Real GDP growth rate are investigated over the years 1995–2021, using a quantitative approach. A long-term relationship between the variables is demonstrated by the Auto Regressive Distributive Lag (ARDL) Cointegration Model, wherein the fertility rate is negatively significant and the female participation rate in the labour force and secondary education are positively significant on the real GDP growth rate. The study uses the Error Correction Model (ECM) to record the speed of adjustment. There is short-run adjustment throughout the entire system, as indicated by the statistically significant and theoretically correct sign of the error correction term coefficient. A more thorough examination is necessary as the Causality test reveals an unanticipated unidirectional association between female secondary education and economic growth.

Testing for Granger causality in heterogeneous panels with cross-sectional dependence

AbstractThis paper proposes a panel Granger causality approach for heterogeneous panels with cross-sectional dependence. We define a panel VAR model with unobserved common factors and apply the PANIC procedure to obtain the de-factored data. We then estimate the lag augmented (LA)-VAR model for each cross section and construct the panel statistics based on the meta-analytic approach that combines the p-values of the individual statistics. The Monte Carlo simulations indicate that the combination tests show good size and power properties and appear suitable for the panels where cross sections may have different unit root or co-integration properties. We finally re-investigate Granger causality between export and economic growth in OECD countries. The results shed light on the importance of accounting for cross-sectional dependence within a factor model framework in determining direction of Granger causality for country-specific analysis. The results further reveal that export and economic growth do not cause each other in the majority of the European Union countries.

Indian energy policies amidst urban population and economic growth: Sourcing evidence from time-frequency domain analysis

Relying solely on fossil fuel-driven economic growth could prove unsustainable, particularly in India, where the adverse environmental impact of this growth trajectory is increasingly affecting overall economic development. This challenge may hinder the realization of the sustainable development goals (SDGs) in the country. Therefore, to address this policy concern, an empirically investigation on the influence of various growth drivers on India's economic growth from 1965 to 2019 was conducted by this present study. Leveraging time-frequency estimators like gradual shift causality tests and Wavelet Coherence, the study yields a range of findings that can inform the formulation of an SDG-oriented policy framework. These outcomes not only acknowledge the detrimental environmental consequences of economic growth but also provide insights into addressing energy security issues. Ultimately, this research enhances to the literature by suggesting a comprehensive and adaptable policy framework aimed at advancing SDGs 7, 8, 12, and 13.

Can board funds, bank credit, and economic development improve food production? Evidence from South Asia

PurposeThis study investigates the impact of board funds, banking credit, and economic development on food production in the context of South Asian economies (India, Pakistan, Bangladesh, Sri Lanka, and Nepal).Design/methodology/approachThis study used data from the World Development Indicators covering the years 1991–2019. To investigate the relationship between the variables of the study, we employed the panel unit root test, panel cointegration test, cross-sectional dependence test, fully modified least squares (FMOLS), and panel dynamic least squares (DOLS) estimators.FindingsThe empirical results indicate that board funding significantly increase food production; however, banking credit had a negative impact. Furthermore, the findings indicate that economic development, Arable land, fertilizer consumption, and agricultural employment play a leading role in enhancing food production. The results of the Dumitrescu-Hurlin causality test also show substantiated the significance of the causal relationship among all variables.Practical implicationsSouth Asian countries should prioritize board funding, bank credit, and economic development in their long-term strategies. Ensuring financial access for farmers through micro-credit and public bank initiatives can spur agricultural productivity and economic growth.Originality/valueThis study is the first to combine board funding, banking credit, and economic development to better comprehend their potential impact on food production. Instead of using traditional approaches, this study focuses on these financial and developmental aspects as critical determinants for increasing food production, using evidence from South Asia.

Linkage between Crude Oil, Gold, INR-USD Reference Rate and Sensex amidst Covid-19 Pandemic: A Cointegration and Causality Analysis

The primary purpose of this paper is to analyse the shortrun and long-run association between crude oil prices, gold prices, USD-INR reference rate, and stock market index across the COVID-19 pandemic. We have employed Johansen's cointegration test to measure the long-run association among the variables. To measure the short-run association of variables, we employed vector autoregression, and then, to find the direction of causality, we used the Granger causality test. The cointegration test results show that variables are not cointegrated in the precrisis as well as during the crisis period; hence, there is no long-run association among the variables. The VAR results show that gold price was influenced by the stock market index in the pre-crisis era; however, during the crisis, no association was observed. Similar results can also be found in crude oil, USD-INR reference rate, gold price, and stock market. The results also show that the relationships among the variables are dynamic and ever-changing in the preCOVID and during the COVID-19 pandemic.

Unlocking the path to environmental sustainability: navigating economic policy uncertainty, ICT, and environmental taxes for a sustainable future.

The study aims to gauge the impact of economic policy uncertainty, ICT, and environmental tax on environmental sustainability, which is measured by carbon emission and ecological footprint in a panel of 22 nations from 1997 to 2021. The present study has implemented the advanced panel data estimation techniques, including continuously updated fully modified (CUP-FM) and continuously updated bias-corrected (CUP-BC), dynamic seemingly unrelated regressions (DSUR), and nonlinear autoregressive distributed lagged (NARDL) in documenting the elasticities of target variables. Moreover, the directional causality has been tested through the D-H causality test. Study findings documented a positive and statistically significant linkage between EPU and environmental degradation. That is, EPU amplifies the emission of CO2 and ecological instability. The effects of ET and ICT are positively associated with environmental sustainability; that is, ET and ICT control the emission of CO2 and bring ecological improvement. This study contributes to the existing body of literature by conducting a thorough analysis of the relationship between various factors and their impact on environmental degradation. The study emphasizes the significance of every factor in influencing environmental outcomes. It provides policy suggestions to reduce CO2 emissions and promote ecological sustainability. The findings add valuable insights to the ongoing conversation about how to tackle environmental challenges in our constantly evolving world.

Beyond the basics: mapping the inflation response to fiscal deficit in India with smooth transition autoregressive model

PurposeIndia’s historical fiscal performance has featured elevated deficit levels. Driven by the imperative need for fiscal stimulus measures in response to the crisis, efforts toward fiscal consolidation from 2003 to 2008 were reversed in 2008–2009 due to the financial crisis. These stimulus actions are believed to have wielded a notable influence on inflation dynamics. Presumably, a high inflation rate hinders growth and inflicts severe welfare costs. Accordingly, the principal objective of this paper is to scrutinise the threshold effects of fiscal deficit on inflation within the context of the Indian economy.Design/methodology/approachWe employed the Smooth Transition Autoregressive (STAR) Model, a robust tool for capturing non-linear relationships, to discern the specific threshold level of fiscal deficit. Our analysis encompasses annual data spanning from 1971 to 2020. Additionally, we have leveraged the Toda-Yamamoto causality test to establish the existence and direction of a causal connection between fiscal deficit and inflation in the Indian economy.FindingsOur analysis pinpointed a critical threshold level of 3.40% for fiscal deficit, a value beyond which inflation dynamics in India undergo a marked transition, signifying the presence of significant non-linear effects. Moreover, the results derived from the Toda-Yamamoto causality test offer substantiating evidence of a causal relationship originating from the fiscal deficit and leading to inflation within the Indian economic framework.Research limitations/implicationsThe findings of our study carry significant implications, particularly for the formulation and execution of both fiscal and monetary policies. Understanding the threshold effects of fiscal deficit on inflation in India provides policymakers with valuable insights into achieving a harmonious balance between these two critical economic variables.Originality/valueTo the best of our knowledge, this study is the first of its kind to empirically investigate threshold effects of fiscal deficit on inflation in India from a non-linear perspective using the Smooth Transition Autoregression (STAR) model.

Analyzing the impact of remittance, FDI and inflation rate on GDP: A comparative study of Bangladesh, Pakistan and Sri-Lanka using VAR and BEKK-GARCH approach

This study examines the impact, conditional correlation and volatility spillover effect of remittances, foreign direct investment and inflation rate on GDP in Bangladesh, Pakistan, and Sri-Lanka, three Asian nations that are particularly vulnerable. While numerous studies have examined the relationship between remittances, FDI and IR on GDP but this paper approaches advanced econometric techniques to capture spillover effect and the dynamic interactions between the variables. For estimation purposes the study employs different econometric techniques such as Augmented Dickey-Fuller (ADF) test, VAR model, Granger causality tests, Impulse Response function, Variance Decomposition and BEKK-GARCH model. Bangladesh and Sri Lanka's REM, FDI and IR have no significant effects on GDP according to the VAR model. BEKK-GARCH demonstrates that three countries have both unidirectional and bidirectional transmissions of volatility, with the exception of Pakistan's REM and Bangladesh's FDI. We find that VAR model may not be adequate in capturing the complex dynamics between variables, which can be better captured by BEKK-GARCH model. Our comparison research shows how these variables affect GDP differently and similarly in each of the three nations, giving policymakers information they can use to create customized policies that encourage economic growth.

Exploring the association between the female gender, education expenditure, renewable energy consumption and CO2 emissions: Empirical evidence from Nigeria

AbstractThe significance of women's contribution to attaining the Sustainable Development Goal of net zero emissions cannot be underestimated. Therefore, employing the ecofeminism theory, this research explores the effect of female gender, education expenditure and renewable energy consumption on carbon dioxide emissions in Nigeria between 1990 and 2019. The utilised methodologies include autoregressive distributed lag (ARDL), fully modified ordinary least square (FMOLS), dynamic ordinary least square (DOLS) and canonical cointegrating regression (CCR). The cointegration tests showed evidence of long‐run association. The ARDL result revealed that female gender, education expenditure, and renewable energy consumption have a negative relationship with carbon dioxide emissions in the long run. This indicates that a rise in these variables is beneficial to the environment. In the short run, female gender and renewable energy consumption have a negative link with carbon dioxide emissions. For robustness check, the results obtained from the FMOLS, DOLS and CCR methods validate the ARDL findings. Furthermore, the frequency domain causality test established that the female gender has a one‐way causal influence on carbon dioxide emissions in the long, medium, and short term. Based on these outcomes, policy recommendations are further discussed.

The role of shadow banking towards a sustainable growth path

This paper utilizes data from 26 countries to document the relationship between renewable energy consumption, shadow banking, traditional banking, and economic growth covering 2010–2021. Employing panel quantile approaches, the results indicate a negative and significant impact of renewable energy consumption on economic growth, highlighting the initial high costs inherent in transitioning to renewable energy sources. Moreover, the findings reveal that shadow banking, along with traditional banking, tends to weaken the negative impacts of renewable energy consumption on economic growth, particularly in countries with medium and high levels of economic development. Finally, panel causality tests confirm bidirectional causality between economic growth and renewable energy consumption, economic growth and shadow banking, and renewable energy consumption and shadow banking. These insights underscore the need for policymakers to consider the role of alternative financing mechanisms in the transition to renewable energy.

DNA Methylation in the Anti-Mullerian Hormone Gene and the Risk of Disease Activity in Multiple Sclerosis.

Multiple sclerosis (MS) has a complex pathobiology, with genetic and environmental factors being crucial players. Understanding the mechanisms underlying heterogeneity in disease activity is crucial for tailored treatment. We explored the impact of DNA methylation, a key mechanism in the genetics-environment interplay, on disease activity in MS. Peripheral immune methylome profiling using Illumina Infinium MethylationEPIC BeadChips was conducted on 249 untreated relapsing-remitting MS patients, sampled at the start of disease-modifying treatment (DMT). A differential methylation analysis compared patients with evidence of disease activity (EDA) to those with no evidence of disease activity (NEDA) over 2 years from DMT start. Utilizing causal inference testing (CIT) and Mendelian randomization (MR), we sought to elucidate the relationships between DNA methylation, gene expression, genetic variation, and disease activity. Four differentially methylated regions (DMRs) were identified between EDA and NEDA. Examining the influence of single nucleotide polymorphisms (SNPs), 923 variants were found to account for the observed differences in the 4 DMRs. Importantly, 3 out of the 923 SNPs, affecting DNA methylation in a DMR linked to the anti-Mullerian hormone (AMH) gene, were associated with disease activity risk in an independent cohort of 1,408 MS patients. CIT and MR demonstrated that DNA methylation in AMH acts as a mediator for the genetic risk of disease activity. This study uncovered a novel molecular pathway implicating the interaction between DNA methylation and genetic variation in the risk of disease activity in MS, emphasizing the role of sex hormones, particularly the AMH, in MS pathobiology. ANN NEUROL 2024.

The Relationship Between the German Current Account and Financial Account: Evidence from the Toda-Yamamoto Causality Approach

Abstract The paper examines the causality between the German current account and financial account. It contrasts with past research which assumes the current account and financial account to be jointly determined by a saving-investment imbalance. Our analysis decomposes the current account into exports and imports (real resource flows) and the financial account into domestic capital outflows and foreign capital inflows (gross capital flows). Evidence from the Toda-Yamamoto causality test shows that for Germany from Q1.1980 to Q2.2023, the causality runs from the financial account to the current account. It is not real resource flows but gross capital flows which exert significant impacts on the German real exchange rate. The finding implies that over the long run, strong German capital outflows and weak foreign capital inflows contributed to weak wage growth and stagnant investment in Germany, sustaining the persistent German current account surpluses. A reduction of the German current account surpluses requires a policy mix of fiscal expansion and monetary tightening which would expand the absorption of German and foreign capital in the German economy.

Hindrance or facilitator? The economic consequences of national demonstration eco-industrial parks in China.

Many countries attach great importance to the green, low-carbon, and circular development of industrial parks. China is one of them and has entered an exploration journey of national demonstration eco-industrial parks (NDEIPs). However, the impact of the transformation of industrial parks into NDEIPs on local economic development still remains a mystery. To address this issue, we develop an empirical study using a combination of the multi-period difference-in-differences method and the propensity score matching method based on the panel data for 266 cities in China from 2001 to 2021. The results show that industrial parks becoming NDEIPs promotes cities' economic development. This conclusion still holds after a series of robustness tests, such as the reverse causality test and the placebo test. Moreover, the park heterogeneity tests show that the economic consequences vary according to differences in levels, industry types, life cycle phases, and the degree of foreign firm agglomeration. The city heterogeneity tests show that the economic consequences differ based on administrative levels, innovation capabilities, technology industrialization, and environmental friendliness. The spatial heterogeneity tests show that the economic consequences differ according to geographical location and whether situated in the Yangtze River Economic Belt. The policy upgrading heterogeneity tests show that the economic consequences differ during the process of policy upgrading and transformation. In addition, the mechanism tests reveal that green innovation, human capital level, and firm attractiveness mediate the relationship between industrial parks becoming NDEIPs and cities' economic development. This study provides a new perspective for understanding the economic effects of the transformation of industrial parks into NDEIPs, and provides a reference for the government on how to maximize these economic effects.

A re‐assessment of the Resource Curse Hypothesis in top resource‐rich developing countries: Fresh insights using method of moments quantile regression

AbstractWhether mineral and natural resources (NR) are a blessing or a curse for economic growth (ECON_G) is still a debatable issue among scholars and policymakers. Therefore, the primary goal of the present study is to investigate the relationship between mineral resources (MR), NR, and ECON_G to re‐assess the “Resource Curse Theory.” For this purpose, a panel of six top resource‐rich developing economies (China, Brazil, Russia, Iran, the Republic of the Congo, and Saudi‐Arabia) over the 1995 to 2021 period is analyzed using dynamic ordinary least square, fully modifies ordinary least square and method of moments quantile regression approaches. The basic findings of the above‐mentioned techniques reveal the significantly positive impact of both NR and MR on ECON_G in the selected economies. The estimation outputs of the method of moments quantile regression also reveal the significant positive contribution of these resources in ECON_G at all quantiles (0.10–0.90). Thus, the study finds that the Resource Bless Hypothesis is valid as both NR and MR support the absence of the Resource Curse Hypothesis in these countries. Furthermore, the findings of the Granger Causality test reveal that bidirectional causal association is present between NR and ECON_G and MR and ECON_G. The study recommends that policymakers and governments in the resource‐rich countries should promote the development of the mining sector and sustainable resource extraction by properly monitoring the resource sector. More importantly, implementing resource governance initiatives are needed to be prioritized for transforming resources as a blessing for the studied countries.

Oil Prices and Economic Growth in China: A Time-Frequency Analysis

This study analyses the inherent evolution dynamics of economic activity and global oil prices in China using the tools of wavelet and wavelet-based VAR-GARCH-BEKK model. Besides, the Wavelet-Granger causality test of Olayeni (2016) provides us further insights into the magnitude and direction of causal connectedness between oil prices and economic activity in China over time and across different frequencies simultaneously. We find that the spillover effects between China’s economic activity and global oil prices are time-varying in different time and frequencies in terms of direction and strength. More accurately, the prices and volatility spillovers between them are significant in the short and medium run but eventually neutral toward the long run. The Wavelet-Granger causality provides us further insights into the lead-lag relationships between oil prices and China’s economic activity from an economic perspective. The dynamic time-frequency association findings suggest crucial implications that might assist policymakers and other market participants in mitigating risk.