Causality Test Research Articles

Impact of COVID-19 trade suspension on price discovery in FPO-traded agri-derivatives

Purpose This study aims to examine whether agricultural commodities exhibited deviations in the lead-lag relationship between future and spot prices of farmer producer organizations (FPOs) traded commodities in the Indian derivative market after trade suspensions during Covid-19. The study may help buyers and sellers to get a fair price for their commodities after lockdown-trade disruptions. Design/methodology/approach The study applied the Granger causality (GC) test and the vector error correction model (VECM) to analyse short-run and long-run lead-lag relationships. Moreover, the study examined the pre-post-trade suspension effect on the lead-lag relationship of commodity prices. Findings The GC test results show that five out of the 13 agri-commodities have changed their lead-lag relationship from future to spot in the short run. Simultaneously, VECM captured changes in the lead-lag relationship for the same five commodities in the long run due to trade suspensions. Practical implications The findings indicate a reverse lead-lag relationship between future and spot prices for aforesaid commodities after trade suspension. The stakeholders may use the lead prices for these commodities to perform a fair trade. The study may be helpful in structuring price discovery strategy to achieve optimal price and efficient derivative trading. Originality/value To the best of the authors’ knowledge, this is the first study examining the effects of trade suspension on price discovery in FPO-traded agri-derivatives caused by the COVID-19 pandemic.

Causality relationships between climate policy uncertainty, renewable energy stocks, and oil prices: a mixed-frequency causality analysis

This paper investigates the predictive relationships among climate policy uncertainty (CPU), oil prices, and renewable energy (RE) stock market returns, particularly highlighting the challenges posed by the varying data frequencies of these variables. The study utilizes a comprehensive dataset comprising monthly CPU, daily oil prices, and RE stock returns, sourced globally. By applying a mixed-frequency causality test (MFCT), the analysis reveals significant predictability across different time horizons, particularly highlighting the strong influence of oil prices on RE stock returns over short-term horizons, while CPU demonstrates a more pronounced effect over medium to long-term horizons. In contrast, the application of the classical Granger causality test on low-frequency (monthly) data indicates an insignificant relationship between CPU and RE stocks, suggesting that traditional models may overlook important predictive dynamics. The analysis was conducted using Matlab code, and the findings provide valuable insights for policymakers in designing effective climate policies and for investors in optimizing portfolio strategies and hedging against risks.

Foreign Portfolio Investment and Capital Market Development in Nigeria

This study investigates the impact of foreign portfolio investment (FPI) on the development of the Nigerian capital market from 1986 to 2023, addressing challenges such as exchange rate volatility and inadequate technological advancement. The research aims to explore the following questions: What is the relationship between foreign portfolio investments and capital market development in Nigeria? How do exchange rate volatility influence the capital market development in Nigeria? What role do technological advancements play in enhancing market performance and attracting foreign investors? Data were sourced from the Central Bank of Nigeria Statistical Bulletin and the National Bureau of Statistics. A quantitative methodology was employed, utilizing the Johansen co-integration test, Vector Error Correction Model (VECM), and Granger causality test to analyze secondary time-series data. The Johansen co-integration test revealed a long-run relationship among the variables, while the VECM indicated that FPI negatively impacts market capitalization. Additionally, the Granger causality test demonstrated unidirectional causality from capital market development to FPI. To address the identified challenges, the study recommends that policymakers create a more supportive environment that ensures foreign investors can repatriate capital and earnings. The government should prioritize exchange rate stability that reflects market realities to enhance investor confidence. Investments in advanced technology are crucial to improving efficiency and accountability in market transactions. By adopting these targeted recommendations, Nigeria can better leverage FPI to foster capital market growth and promote sustainable economic development. JEL Codes: G15, O16

Effects of Global Supply Chain Pressure on Sentiment, Expectation, and Uncertainty: A VAR Approach

This paper studies the relationship of global supply chain pressure with consumer sentiment, inflation expectation, and monetary policy uncertainty in the United States. A sample from January 1998 to January 2024 is used, and this paper uses a Vector Autoregression (VAR) approach based on the method proposed by Toda and Yamamoto (1995). The Granger causality test suggests that the predictions of inflation expectation based on its own past values and the past values of the global supply chain pressure are better predictions of inflation expectation than just using the past observations of inflation expectation. In contrast, Impulse Response Functions suggest that surprise increases in global supply chain pressure lead to increased inflation expectation and monetary policy uncertainty; this shock lasts up to two years. Meanwhile, the Impulse Response Functions suggest that surprise increases in the global supply chain pressure decrease consumer sentiment (confidence), lasting up to two and a half years. Afterward, the impact converges back to zero. Additionally, the Variance Decomposition results suggest that by the final period, the impulses of the global supply chain pressure explain over 22%, 7%, and 44% of the variation of consumer sentiment, monetary policy uncertainty, and inflation expectation, respectively.

Tourism’s Influence on Economic Growth and Environment in Saudi: Present and Future

Reports from the World Tourism Organization indicate that tourism activity has been increasingly booming; this sector is essential for economic growth and may affect the environment. Tourism is one of the key strategic sectors for planned growth in Saudi Arabia’s Vision 2030. This study is designed to evaluate the long-termning association between tourist arrivals, growth domestic product (GDP), and CO2 emissions in the Kingdom of Saudi Arabia (KSA). The data related to these variables were assessed for the period 2010 to 2020. The autoregressive distributed lag (ARDL) bounds results revealed that there are long-established relations between tourist arrivals and growth domestic product and tourist arrivals and CO2 emissions. The dynamic ordinary least squares (DOLS) and fully modified ordinary least squares (FMOLS) model outcomes were compatible with the outcomes of the ARDL model. In reference to the Granger causality test, tourist arrivals cause (affect) the GDP. Such a result confirms the conception that tourism encourages economic growth. No causality runs from tourist arrivals towards CO2 accumulation. This result may reflect the governmental effort to reduce CO2 accumulation and/or to perform tourism activities in a sustainable way. The results predicted that the growth rate of tourist arrivals, GDP, and CO2 accumulation equal 0.0023, 0.048, and 0.0169, respectively, during the forecast period (2021–2030), which appeared to be increasing for tourist arrivals and GDP and decreasing for CO2 accumulation. The study recommended that, to increase economic growth, tourist arrivals should be increased alongside performing tourism activities in a sustainable way. These findings point to the benefits of governance in ensuring effective policies to decarbonize the environment, and policy proposals are put forward accordingly.

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.

Can Oil Prices Volatility Explain Economic Growth?

The research focuses on the relationship between oil price volatility and economic activity in the context of a sample of six GCC countries. The study derives the results from an annual data set (1998-2021) and involves correlations, linear regression, curvilinear regression, panel data regression and causality tests to draw conclusions. It was observed that panel data analysis with random effects was best able to explain the positive relationship between oil price volatility and economic activity. Kuwait was observed as an exception country in the study as it demonstrated insignificant relationships and differentiating behavior. The global financial crisis was observed as a significant event on global oil volatility.

Toward Environmental Sustainability: The Nexus between Agriculture, Renewable Energy, and Economic Growth in Mitigating CO2 Emissions in Somalia

This research investigates the intricate relationships between agriculture, renewable energy, economic growth, and carbon dioxide (CO2) emissions in Somalia from 1991 to 2022. This study uses the Autoregressive Distributed Lag (ARDL) model and Granger causality tests to understand their impact on environmental sustainability. ARDL results reveal a negative role of agriculture and renewable energy on CO2 in the short and long term. The country's reliance on fossil fuels and biomass further intensifies CO2 emissions. While economic growth is essential for improving living standards, it positively correlates with CO2 emissions in the short and long term, emphasizing the challenge of decoupling economic development from environmental degradation. Domestic investment has a short-run relation only to CO2 emissions. In Granger causality tests, the results indicated that Agriculture and domestic investment have bidirectional causality to carbon dioxide emissions. On the other hand, renewable energy, economic growth, and domestic investment have unidirectional causality to Agriculture in Somalia, while domestic investment has bidirectional causality to renewable energy and economic growth. The study recommends implementing comprehensive and integrated approaches to prioritize sustainable development strategies, clean energy alternatives, and efficient agricultural practices. Promoting economic growth while also focusing on capacity-building and awareness campaigns is essential. Collaborating with the international community on climate change mitigation and sustainable development initiatives can further support Somalia's journey toward environmental sustainability.

CATOM : Causal Topology Map for Spatiotemporal Traffic Analysis with Granger Causality in Urban Areas.

The transportation network is an important element in an urban system that supports daily activities, enabling people to travel from one place to another. One of the key challenges is the network complexity, which is composed of many node pairs distributed over the area. This spatial characteristic results in the high dimensional network problem in understanding the 'cause' of problems such as traffic congestion. Recent studies have proposed visual analytics systems aimed at understanding these underlying causes. Despite these efforts, the analysis of such causes is limited to identified patterns. However, given the intricate distribution of roads and their mutual influence, new patterns continuously emerge across all roads within urban transportation. At this stage, a well-defined visual analytics system can be a good solution for transportation practitioners. In this paper, we propose a system, CATOM (Causal Topology Map), for the cause-effect analysis of traffic patterns based on Granger causality for extracting causal topology maps. CATOM discovers causal relationships between roads through the Granger causality test and quantifies these relationships through the causal density. During the design process, the system was developed to fully utilize spatial information with visualization techniques to overcome the previous problems in the literature. We also evaluate the usability of our approach by conducting a SUS(System Usability Scale) test and traffic cause analysis with the real-world data from two study sites in collaboration with domain experts.

Foreign direct investment inflow and the economic growth nexus in Tanzania

PurposeA shortage of investment capital is one of the main issues facing most developing economies, including Tanzania. Policymakers, economists, and governments in these countries have been debating how to overcome this obstacle and improve economic growth through foreign direct investments (FDI). The present study endeavors to examine the relationship between FDI and economic growth in Tanzania during the period of 1991–2021. The analysis considers financial development, capital formation, and labor force involvement as intermediation variables.Design/methodology/approachThe study uses time series data to estimate an autoregressive distributed lag (ARDL) model, as well as Granger causality tests to investigate the relationship between the variables under study.FindingsThe findings show a long-run co-integration between the studied variables. However, only FDI and labor input have a significant impact on gross domestic product (GDP) growth in both the short and long run. Bidirectional causality exists between FDI, labor input, and GDP growth in the short and long run.Practical implicationsThis study contributes to an understanding of the relationship between inbound FDI, labor input, and economic growth in Tanzania, as well as updating the government to improve its financial system to gain more from FDI inflows and hence boost growth-enhancing effects.Originality/valueThis study provides insight into the relationship between inbound FDI and economic growth in Tanzania, which is a critical policy concern in developing economies. Extant studies’ results remain controversial, and this is the first study in Tanzania to investigate the FDI-growth nexus, by considering financial development, capital formation, and labor input simultaneously.

FDI Impact on China's Sectoral Performance: A Panel Cointegration and Causality Analysis

This study provides a thorough examine the influence of FDI inflows and outflows on China's economic growth across three major sectors. Using panel data from 1997 to 2022 in China, it utilizes tests for unit roots and cointegration to investigate the enduring connection between FDI and economic growth. The research results validate the existence of a long-standing cointegration relationship between FDI and economic expansion. Additionally, this research employs the Granger causality test to ascertain the direction of causality between FDI and economic growth. The study reveals a two-way causality between foreign direct investment (FDI) inflows and overall economic growth, which supports the hypothesis of feedback causality. Additionally, there exists a one-way causality originating from FDI inflows across various sectors, suggesting that growth attracts FDI. The government should encourage foreign investment to enhance the industrial structure and boost economic growth.

DEVELOPMENT-ECONOMIC FREEDOMS INTERACTION: A COMPARATIVE ANALYSIS OF D8 AND G8 COUNTRY GROUPS

One viewpoint, which has been gaining traction recently, posits that augmenting economic freedom within liberal markets while minimizing public intervention spurs development. Conversely, another standpoint suggests that as development progresses, there emerges a heightened demand for greater economic freedom, resulting in the implementation of liberal policies. In this context the primary objective of this study is to scrutinize the presence of causality between economic freedom and development. Additionally, the study delves into whether this causal relationship, if extant, is consistent across countries at varying stages of development. Consequently, an examination is conducted on two distinct country groups, namely D8 and G8, employing the Dumitrescu-Hurlin panel causality test. This method dissects the directional link between economic freedom and development for these country clusters spanning the period 2000-2019. The analysis reveals that within D8 nations, a reciprocal causality exists between economic freedom and development. In contrast, among G8 countries, no causal link emerges from economic freedom to development, while a unilateral causal relationship emerges from development to economic freedom.

Mutual Causality Between Urban Transport Superiority Degree and Urban Land Use Efficiency: Insights from County Cities in Gansu Province Under the Belt and Road Initiative

Exploring the coupled coordination and interaction between urban transport superiority degree (UTSD) and urban land use efficiency (ULUE) is the key to promoting efficient land use in cities and coordinated development. This paper adopts the improved UTSD model, super-efficiency slack-based measure–undesirable output model, coupling coordination degree model (CCDM), panel Granger causality test, random forest model, and the mixed geographically and temporally weighted regression model to reveal the spatial and temporal evolution and coupling characteristics of UTSD and ULUE in Gansu from 2005 to 2020 and to validate and explore the interaction mechanism between UTSD and ULUE. The results show that (1), from 2005 to 2020, the average UTSD in Gansu increased from 0.56 to 1.01 and the Belt and Road Initiative accelerated the construction of the transportation network in Gansu. The average ULUE increased from 0.52 to 0.62; the spatial distribution of ULUE was high in the west and north and low in the east and south. (2) From 2005 to 2020, the average CCDM of UTSD and ULUE in Gansu increased from slightly unbalanced (0.37) to slightly balanced (0.52). A spatially high UTSD and high ULUE agglomeration area can be found along the transportation arteries. (3) The UTSD and ULUE were mutually causal, with the degree of transportation arterial influence degree being the strongest driver of ULUE among the components of UTSD (30.41% contribution) and tax revenue being the strongest driver of UTSD among the components of ULUE (15.10% contribution). Overall, the connotation of ULUE puts forward the demand for improving the transportation infrastructure and, at the same time, provides the guarantee for UTSD upgrading, which in turn affects the ULUE. In the future, the Xinan region of Gansu should prioritize planning and construction of a transportation network. The results of this study can provide a scientific basis for the construction of transportation networks and the efficient use of urban land in Gansu and other regions.

Observational Causality Testing

ABSTRACTIn prior work, we have introduced an asymptotic threshold of sufficient randomness for causal inference from observational data. In this paper, we extend that prior work in three main ways. First, we show how to empirically estimate a lower bound for the randomness from measures of concordance transported from studies of monozygotic twins. Second, we generalize our methodology for application on a finite population, and we introduce methods to implement finite population corrections. Third, we generalize our methodology in another direction by incorporating measured covariate data into the analysis. The first extension represents a proof of concept that observational causality testing is possible. The second and third extensions help to make observational causality testing more practical. As a theoretical and indirect consequence of the third extension, we formulate and introduce a novel criterion for covariate selection. We demonstrate our proposed methodology for observational causality testing with numerous example applications.

Analyzing the Dynamics of Inflation, Exchange Rates and Economic Growth through the Gini Index: Modeling VAR in Morocco

In this study, we analyze the cross-impact of inflation, exchange rates and economic growth on income inequality in Morocco between 2000 and 2022, based on the GINI index as a measure of inequality. Its main objective is to understand how these macroeconomic variables influence income disparities. Our methodology is based on a VAR model to capture dynamic interactions between variables. To validate the robustness of the model, Granger causality tests and specification tests, including tests of homoscedasticity and autocorrelation of residuals, were used. The result is that inflation has a significant positive impact on income inequality, and exchange rate fluctuations directly influence inequality. Furthermore, economic growth helps to reduce inequality, although this effect depends on the distribution of the benefits of this growth. This study contributes to the existing literature by providing empirical evidence of the importance of macroeconomic stability and educational and fiscal policies in reducing income inequality.

Exploring the Linkage between Temperature and Economic Growth in Bangladesh: An ARDL Approach

This study is investigated the nexus between temperature and GDP in Bangladesh and how GDP is affected by carbon emission, rainfall and temperature. The ARDL bound test is employed to examine the cointegration relationship among the variables and findings suggest that variables are cointegrated in both cases of dependent variable temperature and dependent variable of GDP. Results mirror that economic growth affect negatively the temperature and statistically significant for short run and long run. On the other hand, carbon emission and rainfall have statistically significant short run positive impact on GDP growth. The ECM result indicates the equilibrium converges to steady state at 78% annually when dependent variable is temperature and equilibrium also converges to steady state at 0.0891% annually, when dependent variable is GDP. Granger Causality test finds a one-way causal relationship between GDP and carbon emissions in both situation of the different dependent variables. Aftermath, results would say that carbon emission and rainfall have short run salutary impact on economic growth and the economic growth is conducive to reduce temperature in Bangladesh that moves to sustainable development. It might address underlying economic transitions such as shifts to greener industries or government interventions that promote sustainability. It will help researcher and policymakers for further study to identify environmental friendly growth projects that lead to reduce temperature in Bangladesh.

The Interplay of Foreign Direct Investment in Unravelling the Nexus of Technological Innovation and Economic Dynamics: An Integrated Study Using Vector Error Correction Model

Most lower-middle income countries prioritise innovation, with foreign direct investment (FDI) as a key funding source for sustainable development. This article studies the interplay between FDI, technological innovation (measured by patent-granted residents), gross domestic product (GDP), research and development (R&D) expenditure (RDE) and inflation of the Indian economy from 2002–2003 to 2022–2023. It uses a vector error correction model, Johansen cointegration tests, Granger causality tests, the impulse response function and variance decomposition. The study finds two cointegration relationships between the variables. GDP and RDE have a significant positive effect on technological innovation in the long term. Neither FDI nor inflation shows such an impact. FDI policies should therefore consider factors such as a conducive regulatory framework, tax breaks and grants for R&D to enhance their impact on innovation. JEL Codes: O31, O32, F21, F23, E01, O47

An Econometric Analysis of Inflation, Exchange Rate, and Interest Rate on Stock Market Performance in South Africa

The stock market is crucial for resource production, distribution, and mobilization because it provides listed firms with long-term funding. Despite the extensive research on the relationship between inflation and exchange rates, the influence of interest rates on stock market performance has received limited investigation. The goal of this study is to address this gap by examining extensive South African data spanning from the first quarter of 2010 to the fourth quarter of 2022. The study constructs a sample space of 48 quarters, utilizing the JSE All Share Price Index as a substitute for measuring stock market performance. Furthermore, it incorporates additional macroeconomic data from reputable sources like the Federal Reserve Bank of St. Louis and the South African Reserve Bank. We employ the auto-regressive distributed lag (ARDL) model to examine the relationship between the stock prices of the Johannesburg Stock Exchange (JSE) and various variables, including inflation, interest rates, and exchange rates. The unit root test results indicate that the exchange rate integrates at order zero (I(0)), while the share price index, inflation, and interest rate integrate at order one (I(1)). The model explains roughly 96.5% of the variability in the dependent variable. According to the findings, short-term interest rates have a significant and negative impact on the Johannesburg stock market’s performance. However, the study does not demonstrate the significant effects of exchange rates and inflation. Furthermore, the Granger causality test has proven that the independent factors have no ongoing impact on the dependent variable. Without establishing the generalizability of the findings outside these boundaries, the study limits its conclusions to South Africa and the studied period. Different methods, as the quantile autoregressive distributed lag (QARDL) or the non-linear autoregressive distributed lag (NARDL) methodology, could be used in future studies to look into these relationships. These studies could also look at other factors that affect stock market performance. These insights may help policymakers adopt the most effective monetary or fiscal policies and support investors in making well-informed decisions to diversify their portfolios and minimize risks.

A causality investigation into stock prices and macroeconomic indicators in the Indian stock market

Background of the study The systematic impact of macroeconomic variables on stock market returns makes it crucial to comprehend the link between macroeconomic variables and the stock market. Stock prices are closely linked to macroeconomic indicators, a crucial aspect for investors, policymakers, and researchers in emerging markets like India, influencing investment decisions and policy formulation. Methods The autoregressive distributed lag (ARDL) model was used in this study to examine the causal links between specific macroeconomic factors and Indian stock prices from April 2009 to March 2023. Results The outcomes of the research suggest that macroeconomic variables exert influence on the Indian stock market, across the short and long term. Moreover, the results of the paired Granger causality test suggest that the domestic macroeconomic variables possess predictive significance for stock prices in the Indian stock market. Conclusion The study reveals that macroeconomic variables significantly impact the Indian stock market, highlighting the need for investors and portfolio managers to monitor these conditions to optimize returns and mitigate risks. The Reserve Bank of India should maintain an optimal money supply to prevent inflation and exchange rate fluctuations, while bolstering the export sector and facilitating imports through initiatives like Atma-nirbhar Bharat Abhiyan and Make in India. Policies focusing on productivity, infrastructure, and a favourable business environment are also crucial. Therefore, it is crucial for investors and portfolio managers to consistently analyse the current macroeconomic conditions in order to maximize their profits and minimize risks. This research has extensive significance for comprehending the intricate connections between the stock market and macroeconomic issues.

Is the Romer Hypothesis valid for Newly Industrialized Countries? Evidence from panel ARDL

Purpose ― This study investigates the effect of trade openness on inflation, referred to as the Romer hypothesis, for Newly Industrialized Countries (NICs) from 1990 to 2022.Methods ― It uses a panel ARDL method and the Dumitrescu-Hurlin (2012) causality test. Economic growth, credit, and money supply are included in the model as independent variables. Findings ― The findings reveal no statistically significant long-term and short-term relationships between trade openness and inflation. However, money supply has statistically significant positive effects on inflation in the long run, while economic growth and credit exhibit no statistically significant impact. In the short run, money supply and economic growth reduced inflation. According to the Dumitrescu-Hurlin (2012) panel causality test, a bidirectional relationship exists between inflation and economic growth, money supply, and credit, while a unidirectional relationship is observed between inflation and trade openness. Implications ― Reducing the external dependency of sectors that rely on imported inputs is necessary to mitigate the adverse effects of trade openness on inflation in NICs. It is crucial to ensure that monetary policy helps align money supply and credit expansions with real sector trends.Originality ― This research is pioneering in its focus on testing the Romer hypothesis for Newly Industrialized Countries (NICs).