Search

This study aims to examine the key economic indicators with private sector credit and economic growth in Nepal. Commercial banks and other financial institutions lend money to businesses and individuals for investment and consumption. This study aims to analyze key economic indicators in Nepal, including Real Gross Domestic Product (RGDP), Private Sector Credit (PSC), and Gross Capital Formation (GCF), spanning from 1975 to 2022. Employing a quantitative methodology, econometric techniques such as time series analysis, regression modeling, and hypothesis testing were utilized to examine the long-term relationships among these variables, with a particular focus on the impact of Private Sector Credit on Economic Growth (EG). The findings, derived from comprehensive econometric analyses including co-integration tests and vector error correction models, reveal significant insights into Nepal's economic performance and financial system. Results indicate that Private Sector Credit plays a crucial role in stimulating economic growth, with approximately 40.07% of the previous year's imbalance influenced by the long-term elasticity of independent variables. These findings offer valuable insights for policymakers, businesses, and investors, facilitating informed decision-making to promote sustainable economic development in Nepal. Further research is recommended to explore the correlation between the diversification of Private Sector Credit types and economic growth more comprehensively.

We develop an approach to combining contextuality with causality, which is general enough to cover causal background structure, adaptive measurement-based quantum computation and causal networks. The key idea is to view contextuality as arising from a game played between Experimenter and Nature, allowing for causal dependencies in the actions of both the Experimenter (choice of measurements) and Nature (choice of outcomes). This article is part of the theme issue 'Quantum contextuality, causality and freedom of choice'. [ABSTRACT FROM AUTHOR]

Introduction: The fragility of agriculture makes the food supply chain vulnerable to external risks such as epidemic, conflict, disaster, climate change, economic and energy crisis. The COVID-19 pandemic has spread and continued globally in recent years, resulting in food supply chain disruption and insecurity, which triggers profound reflection on the impacts of public health events (PHEs). Studying the impacts of PHEs on the resilience of food supply chain has great significance to effectively reduce the risks of disruption and insecurity in the future. Methods: Based on the composition of PHEs and the division of food supply chain, this paper adopted the nonlinear Granger causality test to verify the nonlinear causal relationship between PHEs and proxy variables in the food supply chain; then the TVP-VAR-SV model was constructed and its three-dimensional pulse response results were matched with the sensitivity, recovery, and adaptation of the food supply chain resilience to deeply explore the dynamic impacts of PHEs. Results: PHEs has significant nonlinear conduction effects on the resilience of food supply chain, the impacts of PHEs on the partial sector resilience have significant dynamic characteristics in the whole sample period, and the impacts of PHEs on the recovery and adaptation aspects of food supply chain resilience have structural break characteristics. Discussion: The differences, dynamic characteristics and structural breaks of the impacts of PHEs on the resilience of food supply chain are caused by the infectivity and mortality of PHEs, attributes of food products, regulation of supply and demand in the market, behavioral decisions of all participants, changes in the policy environment, and coordination and upgrading of all sectors in the supply chain. [ABSTRACT FROM AUTHOR]

Mainland: This study investigates the intermedia agenda-setting (IAS) relationships among major newspapers in Hong Kong, the U.S. and mainland China concerning the 2019 anti-extradition bill protests. It builds on the protest paradigm to characterize the news attributes adopted by leading media outlets and examined the transfer of attribute agendas at both regional and international levels. A total of 9,646 news items were content analyzed. Granger causality tests revealed that mainland Chinese media were the primary agenda-setter among the three, contrary to previous findings that media agendas mostly flow from the U.S. to other countries. Hong Kong newspapers exerted a bottom-up influence on their U.S. counterparts, setting the attribute agenda regarding Chinese authority and international society. A breakdown of the Hong Kong press into three political camps showed diverse IAS relationships. The findings testify to the co-existence of dominant flows and contra-flows in regional and international news flows and imply that organizational and socio-cultural factors can potentially influence IAS. [ABSTRACT FROM AUTHOR]

The measure of partial mutual information from mixed embedding (PMIME) is an information theory-based measure to accurately identify the direct and directional coupling, termed Granger causality or simply causality, between the observed variables or subsystems of a high-dimensional dynamical and complex system, without any a priori assumptions about the nature of the coupling relationship. In its core, it is a forward selection procedure that aims to iteratively identify the lag-dependence structure of a given observed variable (response) to all the other observed variables (candidate drivers). This model-free approach is capable of detecting nonlinear interactions, abundantly present in real-world complex systems, and it was shown to perform well on multivariate time series of moderately high dimension. However, the PMIME presents some inefficiencies in its performance mainly when applied on strongly stochastic (linear or nonlinear) systems as it may falsely detect non-existent relationships. Moreover, and by construction, the measure cannot extract purely synergetic relationships present in a system. In the current work, the issue of false detections is addressed by introducing an improved resampling significance test and a procedure of rechecking the identified drivers (backward revision). Regarding the inability to detect synergetic relationships, the PMIME is further enhanced by checking pairs as candidate drivers for the response variable after having considered all drivers individually. The effects of these modifications are investigated in a systematic simulation study on properly designed systems involving strong stochasticity, regressor terms with synergetic effects, and a system dimension ranging from 3 to 30. The overall results of the simulations indicate that these modifications indeed improve the performance of PMIME and alleviate to a significant degree the issues of the original algorithm. Guidelines for balancing between accuracy and computational efficiency are also given, particularly relevant for real-world applications. Finally, the measure performance is investigated in the study of futures of various government bonds and stock market indices in the period around COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

The offshore RMB exchange rate is affected by the supply and demand relationship in the international market, investor sentiment, market liquidity, and other factors, while the onshore RMB exchange rate is mainly affected by government regulation and intervention. Therefore, the offshore RMB exchange rate may be a better reflection of the market's macroeconomic expectations and risk appetite for China. Stock index futures are mainly affected by macroeconomic factors, so studying the correlation between the offshore RMB exchange rate and stock index futures is helpful for risk management, hedging, and price discovery. In this study, we selected the offshore RMB exchange rate, the volume of stock index futures, and the absolute rate of return as variables of investor sentiment. Through the Granger causality test, impulse response function, and variance decomposition, we studied the correlation between the rate of return of stock index futures and the rate of return of the offshore RMB exchange rate. Furthermore, we constructed a GARCH conditional volatility model. It was concluded that the trading volume and the absolute rate of return of stock index futures could explain the price fluctuations of stock index futures very well. A change in the offshore RMB exchange rate yield causes a change in the yield of stock index futures. Policymakers need to pay close attention to changes in the offshore RMB exchange rate in order to better grasp market trends and manage risks accordingly. [ABSTRACT FROM AUTHOR]

Understanding the attribution of urban extreme precipitation is vital for disaster warning and emergency management. However, present attribution analysis mostly applies factors with interaction within drive forces and little consideration on larger-scale teleconnection. In this study, the annual 1-day (AM1X) and 3-day mean (AM3X) maximum precipitation in Zhengzhou city were focused. First, the driving forces for extreme precipitation are identified by correlation trails and Granger causality test from 130 teleconnection factors. Then, the reconstructed series by regression models and observed series are contrasted to distinguish the attributions considering four return periods. Final, quantitative attribution and the variation with frequencies are obtained. The results show that (1) Western Pacific Subtropical High Area Index (WPSHAI) and Western Pacific Subtropical High Intensity Index (WPSHII) are the dominant teleconnection factors. (2) The point estimation under climate control corresponding to 10%, 1%, 0.1%, and 0.01% is 110.95, 141.40, 166.45, and 189.13 mm for AM1X and 37.10, 46.66, 53.51, and 59.17 mm for AM3X, and the interval estimation is 8.28 ~ 44.92 mm and 2.97 ~ 12.94 mm correspondingly. (3) Extreme precipitation corresponding to 10%, 1%, 0.1%, and 0.01% is governed by climatic control, 70.78% ~ 88.02% for AM1X and 50.72% ~ 67.73% for AM3X. The contribution of human activities increases with increasing return period. It is hoped that this study could provide a new theoretical framework to quantify attribution of urban extreme precipitation. [ABSTRACT FROM AUTHOR]

A plethora of studies have investigated the impact of External Reserve on Exchange rate in Nigeria but the causal link between these variables have received less attention. It is on this basis that this study investigates the causal link between external reserve and exchange rate with an implication for monetary policy direction in Nigeria. Annual time series data from Central Bank of Nigeria (CBN) and World Development Indicators (WDI) were used in this study as we applied different econometrics analytical techniques such as the Unit root test, Johansen Cointegration test and the Granger causality test in our empirical investigation. The result from our study suggests that there exist a long run and unidirectional relationship between external reserve and exchange rate. External reserve was found to granger cause exchange rate. We therefore recommend the formulation and implementation of key policies that will aid in augmenting external reserve and proper implementation of a managed floating exchange rate regime. [ABSTRACT FROM AUTHOR]

Digital economy is a great route to promote the efficient utilization of natural resources and promote sustainability due to its high-tech, rapid growth, extensive penetration, deep integration and other characteristics. Existing study on the influencing factors of the digital economy is not deep enough and lacks the analysis on the relationship structure of factors influencing the digital economy, which is not conducive for an overall grasp of the digital economy. To correctly understand how to better develop the digital economy, this paper studies its influencing factors and the relationships between them. Based on the time-series data of China from 2002 to 2018, grey correlation analysis was applied to calculate the correlation between these influencing factors and the digital economy, and determine the major influencing factors of digital economy development in China. The Granger causality test and a review of existing research were used to judge the interrelationship of various factors. The interpretative structure model was utilized to determine the relationship structure of the main factors affecting the development of China's digital economy. The results show that the number of digital talents, state of the technology market, and degree of digitalization are direct influencing factors of the digital economy. The results help to better understand the development of the digital economy and will enable the implementation of policies to improve towards more sustainable cities. [ABSTRACT FROM AUTHOR]

This is a study of Norway's sustainable environment development amidst the interactions of natural resources, external investment (FDI) and economic development. Much has been done with respect to the study of Norway's economic performance in relation to the link between the natural resources and FDI with little emphasis on the environmental performance of the resource‐based economy. Also, Norway is classified as among the top countries in Europe with a greater percentage of adopting renewable energy, and no study has done a critical review of the impact of natural resources and FDI which are part of drivers of carbon emission that can counter the positive impact of renewable energy towards the Norway's sustainable environment. On this basis, this study adopts a time series data of Norway, 1970 to 2018 to study its environmental performance. Approaches such as structural break analysis, Autoregressive Distributed Lag (ARDL)‐bound testing and Granger causality estimations are utilised in this study for in‐depth analysis of the subject. Findings from ARDL confirmed a positive association between fuels and carbon emission, other indicators (economic growth and natural resources) are improving the quality of the country's environment. FDI even though shows positive sign remains insignificant in impacting the environmental performance in the short run reverted to a significant negative relationship with carbon emissions. This confirms the pollution halo hypothesis and rejects the pollution haven hypothesis (PHH) for Norway, and this trend can be sustained with the constant implementation of environmental rules in the country. Granger test confirms, a one‐way transition from fossil fuels to carbon emission, from carbon emission to growth, and from economic growth to fossil fuels. Also, a two‐way transmission is found between fossil fuels and FDI. These findings from Granger causality are consistent with the findings from ARDL, hence, two ways interactions between FDI and fossil fuel energy source consumption from Granger causality and the two variables (fossil fuels and FDI) are seen impacting on Norway's environmental performance. Findings from the estimates suggest that natural resources and FDI are mitigating pollution, hence, Norway's policy is expected to be resources and FDI driven in sustainable environment development. [ABSTRACT FROM AUTHOR]

Copyright of Çankırı Karatekin University Journal of the Faculty of Economics & Administrative Sciences is the property of Cankiri Karatekin University, Faculty of Economics & Administrative Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Consider two stationary time series with heavy-tailed marginal distributions. We aim to detect whether they have a causal relation, that is, if a change in one causes a change in the other. Usual methods for causal discovery are not well suited if the causal mechanisms only appear during extreme events. We propose a framework to detect a causal structure from the extremes of time series, providing a new tool to extract causal information from extreme events. We introduce the causal tail coefficient for time series, which can identify asymmetrical causal relations between extreme events under certain assumptions. This method can handle nonlinear relations and latent variables. Moreover, we mention how our method can help estimate a typical time difference between extreme events. Our methodology is especially well suited for large sample sizes, and we show the performance on the simulations. Finally, we apply our method to real-world space-weather and hydro-meteorological datasets. [ABSTRACT FROM AUTHOR]

The rainfall, stock, and agri-commodity indices are the economic indicators for Indian economy. Therefore, research is needed to explore the complex relationship between rainfall and economic indicators and its long- term implications. Only a few studies are available on this relationship, and no study gives complete, conclusive evidence on this relationship. Therefore, the present study focused on determining whether there is any relationship between the rainfall index and stock/agri-commodity indices. This study employed the advanced statistical tools like ADF test, correlation, Granger causality test, and GARCH (1,1) model. The research utilized the monthly average rainfall index and the Sensex index from 1991 to 2020. Similarly, the study employed the monthly average Guarex agri-commodity index for the period from 2018 to 2020. The study found that there is no relationship and causality relationship between the rainfall index and the Sensex index. The GARCH (1,1) model shows that the rainfall index has no impact on the volatility of the Sensex index. There is a moderately negative correlation between the rainfall index and the Guarex index, and rainfall affects the volatility of the Guarex index. This shows that the rainfall index can be a distinct asset class that can be traded through rainfall derivatives. In order to launch rainfall derivatives in India, NCDEX and Skeymet Industries have already approached the SEBI for permission. [ABSTRACT FROM AUTHOR]

This paper examines the relationship between commodity index returns and the Office of Financial Research Financial Stress Index (OFR FSI). Utilizing the S&P GSCI and its five sub-indices (agriculture, livestock, energy, industrial metals, and precious metals), we find that the causal relationship between financial market stress and commodity index returns is conditional on the sample period examined and the methodology employed. We also note that stress in financial markets has a negative relationship with commodity index returns during low commodity return states; however, during high commodity return states, financial market stress exhibits a positive relationship with commodity index returns. Our findings highlight the importance of considering a time-varying framework for analyzing commodity return dynamics. [ABSTRACT FROM AUTHOR]

Objective: The objective of the article is the identification and assessment of labour market operation efficiency and stability of matching mechanisms from the microstructure perspective of labour market dynamics via the relationship between distortions in the level and slope of the Beveridge curve. We studied the US labour market dynamics and identified potential structural changes in the job matching mechanisms. The longterm relationship between the level and slope of the Beveridge curve suggested that the underlying level of unemployment matched the labour market's elasticity in response to changes in job vacancies. Research Design & Methods: The main design of our article was a quantitative analysis of the Beveridge curve shifts. We used a data set consisting of monthly information on job vacancies and the unemployment rate of the US labour market from 2000 to 2022. Using a dynamic rolling scheme with a fixed window size, we extracted time series parameters of the Beveridge curve. We then proceed to apply the VECM(1) model to establish the relationship between these parameters. Findings: We have found the stability of the matching mechanism in the long term and the relatively weaker or instantaneous adjustment mechanisms in the short term. We also addressed the absence of evidence regarding Granger causality and its implications. Implications & Recommendations: The identification of elasticity of the Beveridge curve should guide policymakers towards integrated and adaptive strategies that address both structural and dynamic aspects of the labour market. Regular monitoring and evaluation will be crucial to ensuring that policies remain effective in the evolving economic landscape. Contribution & Value Added: Unlike previous literature on the subject, which primarily relied on static analyses and limited time frames, we took a pioneering step by incorporating monthly data on job vacancies and unemployment rate spanning over two decades, from 2000 to 2022. This inclusion allowed us to encompass the period of the COVID-19 outbreak, providing a comprehensive understanding of the matching mechanism's behaviour as a cornerstone for entrepreneurship during times of extraordinary economic stress. [ABSTRACT FROM AUTHOR]

This article presents a novel approach for reconstructing an equivalent underlying model and deriving a precise equivalent expression through the use of direct causality topology. Central to this methodology is the transfer entropy method, which is instrumental in revealing the causality topology. The polynomial fitting method is then applied to determine the coefficients and intrinsic order of the causality structure, leveraging the foundational elements extracted from the direct causality topology. Notably, this approach efficiently discovers the core topology from the data, reducing redundancy without requiring prior domain-specific knowledge. Furthermore, it yields a precise equivalent model expression, offering a robust foundation for further analysis and exploration in various fields. Additionally, the proposed model for reconstructing an equivalent underlying framework demonstrates strong forecasting capabilities in multivariate time series scenarios. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to examine the interrelationship between financial stability and economic growth with a comprehensive analysis. Design/methodology/approach: The panel Granger causality testing approach is carried out to the panels of the Fragile Five (F5) and the Group of Seven (G7) countries for the period 1998–2020. To capture the different aspects of financial stability the authors use eight different indicators. Findings: The findings reveal some important implications: the relationship between financial stability and economic growth is sensitive to the financial stability indicators for both the F5 and G7 countries. The stability indicators related to the credit market contain much more causality relationship with economic growth than the indicators related to the stock market. Z-score and provisions to nonperforming loans (NPLs) are among the two variables with the highest causality relationship with economic growth. The least number of causality link is found for the Regulatory Capital Ratio and Stock Price Volatility in F5 countries and Credit Ratio, NPLs and Stock Price Volatility in G7 countries. Economic growth affects financial stability through credit market stability indicators and mostly for the F5 countries. No causal relationship is found for any of the financial stability indicators of Canada, the UK and the USA from economic growth to financial stability. Originality/value: Since the linkages between financial stability and economic growth may vary due to country/group specific differences, apart from the previous studies, the authors select two different groups of countries in terms of financial stability and economic size. [ABSTRACT FROM AUTHOR]

Energy consumption is a crucial driver for economic development, improving the quality of life of the population of a country. This study attempts to contribute to the discussion by employing a systemic approach and methodology to examine the relationship between energy consumption (EC), gross domestic product (GDP) and carbon dioxide emissions (CO2) in Ecuador using time series from 1990 to 2018 with a mixed methodology (quantitative and qualitative). The energy balance and the enlarged Kaya identity are utilised to quantify the environmental impact of human activities. Furthermore, correlational cointegration and Granger causality tests are used to analyse the long-term and short-term relationships between variables in different sectors. The results reveal that there is no Granger causality between the variables in the agriculture and transport sectors, but there are unidirectional causality relationships in the industry and services sectors. In the industry sector, the study finds that EC Granger causes GDP (Wald test p value = 0.0038) and CO2 Granger causes GDP (Wald test p value = 0.0433). In the services sector, GDP Granger causes CO2 (Wald test p value = 0.0075), and EC Granger causes CO2 (Wald test p value = 0.0122), reinforcing the loop between GDP and CO2 in both the sectors. The analysed relationships help to inform policymakers about the likely impact of interventions. In addition, the study shows that Ecuador is in the initial phase of the Environmental Kuznets Curve, and provides strategies to manage sectoral energy consumption and valuable insights for other developing countries in Latin America seeking to pursue sustainable development. [ABSTRACT FROM AUTHOR]

Soil moisture‐Precipitation (SM‐P) coupling, especially the causality from SM to P (SM → P), is an important and highly debated topic. The causal inference from observational data provides a statistical approach for this issue, where the experimental research is infeasible. Various causal inference methods exist, each assuming distinct underlying systems for the targeted variables: pure stochastic or deterministic dynamic system (DDS), which means that these methods detect different types of causality: separable or non‐separable. Acknowledging the inherent deterministic dynamic nature of SM‐P coupling, this study employs a DDS‐based method: Convergent Cross‐mapping (CCM), to detect their non‐separable causality. Centering around SM‐P coupling, we also detect the causality between SMs in shallow and deeper layers, and the causality between evapotranspiration (ET) and SMs in all layers. Notably, before applying CCM, a preconditional procedure is required: verifying the DDS nature of targeted variables P, SM, and ET. Key findings of this research include: (a) In SM‐P coupling, only SMs in shallow layers but not deeper layers could render causalities toward P, while P renders causalities toward SMs in all layers. (b) The time‐delay of causality from SM1 to P in spring/summer is around 4–6 days, and that from P to SM1 is around 2–4 days. (c) Inside SMs, shallow SMs have obvious causalities downwards to deeper SMs, but deeper SMs seem harder to render causalities upwards to shallow SMs, explaining why they hardly render causalities to P. In summary, this study furnishes an indispensable DDS‐based complement to stochastic‐based methodologies for SM‐P coupling. Plain Language Summary: This study aims to investigate the causal interaction between soil moisture (SM) and precipitation (P), especially the impact from SM toward P, based on their observational data (i.e., time series), as experimental approaches are impractical given their large‐scale complex physical mechanisms. Many methods exist in the realm of causal detection, either assuming a stochastic underlying system or a deterministic dynamic system (DDS) for the targeted variables. In stochastic systems, causal interactions among variables are separable, whereas they are non‐separable in DDSs. Given that SM‐P coupling seems aligning with the description of a DDS, we employ a DDS‐based method: Convergent Cross‐mapping (CCM), to detect the non‐separable causality between SM and P. Specifically, we explore whether causality exists both from SM to P (SM → P, SM in different layers) and from P to SM (P → SM), and if so, what is the causal response time and how long could the causality last. Meanwhile, the causalities among SMs in different layers, and those between SMs and evapotranspiration (ET), are also detected. Through these inquiries, our findings highlight the DDS nature of SM‐P coupling and their non‐separable causality, underscoring the indispensable role of CCM as a complement to previous stochastic‐based studies for SM‐P coupling. Key Points: Soil moisture‐Precipitation (SM‐P) coupling is detected by Convergent Cross‐mapping (CCM) to investigate their non‐separable causalityThe deterministic dynamic system nature of SM‐P coupling is verified, as a precondition for applying CCM to detect their causalityWhile P could causally impact SMs in all layers; in turn, however, only shallow SMs but not deeper SMs could causally impact P [ABSTRACT FROM AUTHOR]

This paper explores the exact relationship between the development level of American public libraries and the output of science and engineering publications. We use the sample data from 1996 to 2019 to build a time series model, and analyze the long-term equilibrium, short-term correlation and Granger causality relationships between them with stationarity test, cointegration test, Granger causality test, and impulse response function. The results suggest that there are a long-term equilibrium relationship and a bidirectional Granger causality between them. The two interact and promote each other. The impact of the development level of American public libraries on the output of science and engineering publications can be seen in fewer time periods, while the impact of the output of science and engineering publications on the development level of American public libraries take more time periods to show. [ABSTRACT FROM AUTHOR]

This study investigates whether news sentiment plays a role in setting social media sentiment to explore the dynamics of sentiment develop and diffusion within the public agenda. Based on the agenda-setting theory, this study analyzed the public and media sentiments towards the 2016 US election and the candidates using data from Twitter, CNN, and Fox News. Focusing on the Twitter messages created by the supporters of Hillary Clinton and Donald Trump, over 1.3 million Twitter messages were collected associated with the election, employing hashtags as indicators of support. The Granger causality test between social media and news sentiments revealed that there is a mutual influence between social media and news sentiments; CNN's overall sentiment was influenced by the sentiment of Hillary Clinton's supporters, whereas Trump supporters' sentiment was influenced by Fox News' negative sentiment. The results suggest that public sentiment is formed in response to public agenda and mass media, indicating that sentiment is a critical component in understanding public opinion. Implications for future studies and limitations are also discussed. [ABSTRACT FROM AUTHOR]

Japan's recent enunciation of a "Free and Open Indo-Pacific" policy aims to promote principles such as free trade, freedom of navigation, and encouraging economic prosperity with building commitment to stability and peace connecting the economic hub of Asia to Africa (MFA, 2019). Natural gas use continues to dominate Japan's energy mix despite efforts to improve environmental protection and attain the Sustainable Development Goals (SDGs). Also, increasing production and investment typically leads to a better quality of life and more disposable income for the populace, which promotes economic growth. To this end, the study investigates the relationship between economic growth, trade openness, and natural gas consumption, as well as gross fixed capital formation and carbon dioxide emissions to overcome omitted variable problems to explore the natural gas-economic growth hypothesis in Japan over the period 1980–2020. Empirical results reveal a long-run relationship among the variables under consideration. By applying the Toda and Yamamoto approach to Granger causality testing, a two-sided causality running from CO2 and economic growth was revealed, while a one-sided causality from economic growth to natural gas consumption. This outcome suggests that the natural gas-economic growth hypothesis is not valid for Japan. According to these outcomes, policymakers in the energy sector should consciously diversify their energy portfolio in line with the Sustainable Development Goals (SDGs) for Japan. Also, it is recommended that there should be a provision of access to skilled labor to increase productivity and export finished products to boost a nation's economy. [ABSTRACT FROM AUTHOR]

This study strives to examine when and where most of the gold smuggling takes place in India. It further analyses the causal relationship between smuggled gold and other macroeconomic variables. Finally, it analyses how the smuggled gold affects the Indian bullion industry. The data related to gold smuggling has been sourced from the website of the Directorate Revenue Intelligence and analysed using graphs and the Granger causality test. The variables used in the study are the quantity of smuggled gold, exchange rates, the major stock indices in the world, the number of auspicious days in a month, domestic and international gold prices, India's jewellery export, the GDP, customs duty, and the domestic gold supply. The results revealed that most of the gold smuggling takes place on Fridays and mostly occurs in the months of October, November, and December. The states of West Bengal, Delhi, Maharashtra, and Tamil Nadu account for most of the gold smuggling in India. A positive correlation is observed between the smuggled gold, India's gold demand, the number of auspicious days in the month, India's jewellery export, India's GDP, India's domestic gold supply, and stock indices such as SENSEX, FTSE100, DFMGI. Gold smuggling in India is caused by India's gold demand, the level of jewellery export, the GDP, domestic and international gold prices, and India's customs duty. [ABSTRACT FROM AUTHOR]

Although the movement of people from rural to urban areas has caused the increased use of energy, the abundance of water resources can be made into a form of renewable energy known as hydroelectricity. As European countries are ranked as the first users and exporters of hydropower, the production of renewable energy in developed countries such as the Nordic region has caused great impacts on economic growth and human development. The importance of this paper is to investigate the relationship between hydroelectricity and the Human Development Index by depending on some variables such as urbanization, rule of law, corruption, trade openness, and GDP per capita from 2002 to 2021 in Nordic countries. The results were estimated depending on impulse response function after conducting the Vector autoregressive model (VAR) model and Granger causality test. Results showed a negative impact from hydro plants in the short run but a significant positive impact in the long run in Nordic countries. The long-term sustainment of Human Development Index (HDI) is due to policies limiting the immigration of labor as well as protection of energy use. Water batteries are gaining popularity across Europe and their implementation is near mandatory. [ABSTRACT FROM AUTHOR]

Objective: Observational studies have shown an association between systemic autoimmune disease (AD) and multiple malignancies. However, due to the difficulty indetermining the temporal nature of the order, their causal relationship remains elusive. Based on pooled data from a large population-wide genome-wide association study (GWAS), this study explores the genetic causality between systemic autoimmune disease and renal malignancy. Methods: We took a series of quality control steps from a large-scale genome-wide association study to select single nucleotide polymorphisms (SNPs) associated with systemic autoimmune disease as instrumental variables(IVs) to analyze genetic causality with renal malignancies. Inverse variance weighting (IVW), MR- Egger, weighted median, simple model and weighted model were used for analysis. The results were mainly based on IVW (Random Effects), followed by sensitivity analysis. Inverse-Variance Weighted(IVW) and MR-Egger were used to test for heterogeneity. MR- Egger is also used for pleiotropic testing. A single SNP analysis was used to identify single nucleotide polymorphisms (SNPs) with potential impact. Odds ratio (OR) and 95% confidence interval (CI) were used to evaluate causality, and sensitivity analysis was performed to evaluate pleiotropy and instrumental validity. Results: Acute and subacute iridocylitis (P = 0.006, OR = 1.077), Ankylosing spondylitis (P = 0.002, OR = 1.051), and spondyloarthritis (P = 0.009, OR = 1.073) were positively associated with an increased risk of renal malignancy. Coxarthrosis (P = 0.008, OR = 0.483), Juvenile rheumatism (P = 0.011, OR = 0.897), and Systemic lupus erythematosus (P = 0.014, OR = 0.869) were negatively associated with an increased risk of renal malignancy. The results of sensitivity analysis were consistent without heterogeneity or pleiotropy. Conclusion: Our study suggests a causal relationship between different systemic autoimmune diseases and renal malignancies. These findings prompt health care providers to take seriously the potential risk of systemic autoimmune disease and provide new insights into the genetics of kidney malignancies. [ABSTRACT FROM AUTHOR]

To analyze the factors affecting road accidents involving hazardous materials, the Bayesian network (BN) model was used to fit the accident data. However, considering the possible overfitting phenomenon of the BN model, the model was optimised by combining Pearson's chi-squared test and Granger causality test (PG) methods. First, the data of hazardous materials accidents were preprocessed, and the index system of factors affecting hazardous materials road transport was constructed from five dimensions of "people, vehicles, hazmat, roads, and environment"; second, Pearson's chi-squared test and the Granger causality test were used to screen the factors affecting hazardous materials road transport accidents and to determine the causal relationship between the factors; finally, the BN model was constructed with accident severity and accident processing time as target nodes, and the results were analyzed and validated. The results show that the overall relative error rate of the model is less than 10% and can be used to explore the risk factors of hazardous materials transport accidents; weather, visibility, lighting, intersection type, road condition, road type, driver condition, vehicle type, etc. are all important factors affecting the severity of hazardous materials transport accidents. The study can serve as a reference for the safety supervision and management of hazardous materials transport enterprises and industrial management departments. [ABSTRACT FROM AUTHOR]

Causal relationships between time series are typically examined by testing for Granger causality. Although many studies have tested Granger causality in mean, non-causality in mean does not need to carry over to other distribution characteristics or different parts of the distribution. This scenario has motivated many researchers to investigate causal relations from the perspective of conditional quantiles. Several methods have been proposed for both parametric and nonparametric modelling frameworks. Parametric methods have limitations in detecting nonlinear causality, whereas nonparametric methods have difficulty selecting smoothing parameters that significantly affect detection performance. To overcome the difficulties of both parametric and nonparametric Granger causality tests in quantiles, we propose a vine copula Granger causality test in quantiles using the semiparametric time-series modelling technique. The proposed test overcomes shortcomings in parametric modelling and has a computational advantage over nonparametric tests. Our test shows good performance in terms of size and power when using various simulated data. Finally, we illustrate our test using recent cryptocurrency data. [ABSTRACT FROM AUTHOR]

Having previously analyzed the relationship between tourism and economic growth from distinct perspectives, this paper attempts to fill the void existing in scientific research on the relationship between tourism and economic development, by analyzing the relationship between these variables using a sample of 123 countries between 1995 and 2019. The Dumistrescu and Hurlin adaptation of the Granger causality test was used. This study takes a critical look at causal analysis with heterogeneous panels, given the substantial differences found between the results of the causal analysis with the complete panel as compared to the analysis of homogeneous country groups, in terms of their dynamics of tourism specialization and economic development. On the one hand, a one-way causal relationship exists from tourism to development in countries having low levels of tourism specialization and development. On the other hand, a one-way causal relationship exists by which development contributes to tourism in countries with high levels of development and tourism specialization. [ABSTRACT FROM AUTHOR]

Epidemiological studies have robustly linked lower birth weight to later-life disease risks. These observations may reflect the adverse impact of intrauterine growth restriction on a child's health. However, causal evidence supporting such a mechanism in humans is largely lacking. Using Mendelian Randomization and 36,211 genotyped mother-child pairs from the FinnGen study, we assessed the relationship between intrauterine growth and five common health outcomes (coronary heart disease (CHD), hypertension, statin use, type 2 diabetes and cancer). We proxied intrauterine growth with polygenic scores for maternal effects on birth weight and took into account the transmission of genetic variants between a mother and a child in the analyses. We find limited evidence for contribution of normal variation in maternally influenced intrauterine growth on later-life disease. Instead, we find support for genetic pleiotropy in the fetal genome linking birth weight to CHD and hypertension. Our study illustrates the opportunities that data from genotyped parent-child pairs from a population-based biobank provides for addressing causality of maternal influences. A Mendelian Randomization study with 36,211 mother-child pairs indicates that maternal genetic variants, influencing a child's birth weight, impact the child's risk for common diseases through inheritance and not by effects on intrauterine growth. [ABSTRACT FROM AUTHOR]

The government of any country can play a great role in promoting economic and environmental policy reforms in both normal and crisis periods, but during the crisis period, the role of the government should take the economy into a recovery position. The stock market is the backbone of the financial system that needs the government's attention, especially in the period of financial stress and environmental protection is the responsibility of every economy to live in a healthy environment. Combining this motive, this study analyzed the role of the government towards the stock market and carbon emission by using different approaches, including the wavelet approach, OLS regression, and the Granger causality test. The wavelet approach is useful for analyzing the role of the government at different time intervals by using the time horizon from 1993 to 2021. World governance's six indicators in terms of voice and accountability, control of corruption, rules of law, regulatory quality, political stability, and government effectiveness are used as the proxy for the role of the government. Our findings show that all WGI indicators have a positive relationship with the stock market of Malaysia except voice and accountability while concerning voice and accountability, the role of the government of Malaysia is negative on the stock market. Similarly, our findings also show that the effective government governance mechanism through WGI indicators has a significant positive impact on CO2 emission due to industrialization. Furthermore, findings of the Granger causality test reveal that all the WGI indicators cause to stock market of Malaysia, and political stability has bi-directional causality indicating stock market index is also a factor that caused the political stability within Malaysia. In the Granger causality results of the CO2 and WGI indicators, there is unidirectional causality found between rules of law and regulatory quality with CO2 emission. This study advocated strong implementations for the investors for investment decisions in effective governance countries and implications for the government to remove their weakness by making effective governance related to the economy and as well as the environments within the country. [ABSTRACT FROM AUTHOR]

This article presents a study conducted in Turkey that examines the relationship between nonpharmaceutical interventions (NPIs) and COVID-19 cases and deaths. The study analyzes daily data from March 11, 2020, to March 8, 2022, and uses the Toda and Yamamoto causality analysis to determine the causal relationship between the stringency index (SI) and new cases and deaths. The results show that an increase in new cases and deaths leads to an increase in the stringency level, but the stringency level does not significantly affect new cases and deaths. However, increases in the stringency level do lead to a significant reduction in COVID-19 incidence in the subsequent 28 days. The findings suggest that there is a one-way causal relationship between NPIs and COVID-19 cases and deaths in Turkey, emphasizing the importance of considering the lagged effect of the stringency index on the number of cases and deaths. This research can help decision-makers understand the consequences of pandemic interventions and develop appropriate strategies. [Extracted from the article]

This paper examines the relationship between structural change, as measured by sectoral composition of employment and value-added (VA), and aggregate productivity growth for sub-Sahara African (SSA) Least Developed Countries (LDCs), using panel data from 1991 to 2018. First, a Granger causality test finds that the relationship between structural change and economic growth is bidirectional. Next, the Pooled Mean Group, a dynamic panel data estimator teases out short-run and long-run effects of structural change on economic growth. The results suggest that the magnitude of labor and VA reallocation across sectors is not large enough to have a significant impact on economic growth, implying that the direction of structural change is important when analyzing its impact on growth in the SSA LDCs. The paper also explains why labor in the SSA LDCs moves from one low productivity sector to another and how that pans out for development. [ABSTRACT FROM AUTHOR]

Objective: To establish a time series model correlating vanilla production with temperature variation to forecast vanilla production in Mexico. Design/methodology/approach: Data on vanilla production in Mexico (Tons) and the annual average temperature were obtained for the period 1985-2020. An ARIMA model was constructed; Granger causality test was conducted to determine the effect of temperature on vanilla production, in addition to evaluating the orthogonal response of the model. A forecast for vanilla production was made for the period 2020-2040. Results: ARIMA (1,1,1) model was found, and the influence of temperature on vanilla production was determined. Both thermal variation and the production of the last three years determine current production. A reduction in the quantity of tons of vanilla produced in the coming years is expected. It is considered that this cultivation is highly sensitive to sudden increases or decreases in temperature. Limitations on study/implications: Vanilla cultivation is sensitive to temperature variation; therefore, in the face of climate change, it is considered necessary to take a series of actions in the present. These actions encompass a genetic perspective, new cultivation methods and locations, as well as technological investment. Findings/conclusions: Vanilla production is influenced by temperature variation and is sensitive to sudden increases or declines. If actions are not taken in the present, a reduction in the national production of vanilla in Mexico is expected due to climate change. [ABSTRACT FROM AUTHOR]

This paper investigated the dynamic and causal relationship among energy intensity, economic growth, urbanization, energy consumption, FDI, and PM2.5 in 1995–2019 period for China, India, Germany, Canada, USA and United Kingdom countries through Panel Fourier Bootstrapping ARDL (PFBARDL) model and Panel Fourier causality test. According to PFBARDL results, the evidence of cointegration between the variables was detected. In the PFBARDL model, the error correction coefficient is determined as -0.169. Results showed that there is a bidirectional causality between economic growth and PM2.5 release, between fdi and PM2.5 release, between energy intensity and PM2.5 release. Rising economic growth, energy consumption structure, and urbanization will have an adverse effect on PM2.5 concentrations. Policy makers must carefully balance the connection between mitigation of PM2.5 and diminishing energy consumption, and increasing economic growth, urbanization, and energy intensity for determining policies to control PM2.5 concentrations. Thus, in order to reduce PM2.5, the governments must explore a new economic pattern to decouple the linkage between PM2.5 and economic growth. [ABSTRACT FROM AUTHOR]

The impacts on food prices of temperature, the oil price, the exchange rate and wages in the agricultural industry were examined via a structural vector autoregression model and panel Granger causality test, using monthly data between January 2003 and December 2020 for Latin American countries. The paper concerns how much the determinants affect food prices. Empirical findings show that the oil price and temperature can be significant factors for reducing food inflation. According to the result of variance decomposition, in general, a considerable part of food inflation was explained by the exchange rate, but its effect did not show any significant change in the long term. The impacts of the oil price and temperature were limited in the early months, but they created larger changes over time. Impulse response function and the Granger causality test also indicated that exchange rate was a crucial dynamic in explaining food inflation in all countries except Ecuador. This country successfully mitigated the negative effect of the exchange rate, but the oil price and temperature had an impact on food inflation. All results indicate that both monetary and fiscal policies are essential to control food prices. These countries can accomplish this by conventional policies or by radical institutional changes. Nevertheless, the oil price and temperature are external dynamics, and crucial in creating alternative policies to control food inflation. [ABSTRACT FROM AUTHOR]

Objective: The primary objective of this research endeavor was to examine the underlying genetic causality between the age at first birth (AFB) and four prevalent esophageal diseases, namely oesophageal obstruction (OO), oesophageal varices (OV), gastro-oesophageal reflux (GOR), and oesophageal cancer (OC). Methods: We conducted a two-sample Mendelian randomization (MR) analysis to examine the causal association between AFB and four prevalent esophageal disorders. We employed eight distinct MR analysis techniques to evaluate causal relationships, encompassing random-effects inverse variance weighted (IVW), MR Egger, weighted median, simple mode, weighted mode, maximum likelihood, penalized weighted median, and fixed-effects IVW. The randomeffects IVW method served as the primary approach for our analysis. Furthermore, we executed several sensitivity analyses to assess the robustness of the genetic causal inferences. Results: The random-effects IVW analysis revealed a significant negative genetic causal association between AFB and both GOR (P < 0.001, Odds Ratio [OR] 95% Confidence Interval [CI] = 0.882 [0.828-0.940]) and OC (P < 0.001, OR 95% CI = 0.998 [0.998-0.999]). Conversely, there was insufficient evidence support to substantiate a genetic causal link between AFB and OO (P = 0.399, OR 95% CI = 0.873 [0.637-1.197]) or OV (P = 0.881, OR 95% CI = 0.978 [0.727-1.314]). The results of sensitivity analyses underscore the robustness and reliability of our MR analysis. Conclusion: The findings of this investigation substantiate the notion that elevated AFB confers a protective effect against GOR and OC. In addition, no causative association was discerned between AFB and OO or OV at the genetic level. [ABSTRACT FROM AUTHOR]

Objectives: Non-alcoholic fatty liver disease (NAFLD) has been linked to an increased risk of kidney stones in prior observational studies, However, the results are inconsistent, and the causality remains to be established. We aimed to investigate the potential causal relationship between NAFLD and kidney stones using two-sample Mendelian randomization (MR). Methods: Genetic instruments were used as proxies for NAFLD. Summary-level data for the associations of exposure-associated SNPs with kidney stones were obtained from the UK Biobank study (6536 cases and 388,508 controls) and the FinnGen consortium (9713 cases and 366,693 non-cases). MR methods were conducted, including inverse variance weighted method (IVW), MR-Egger, weighted median, andMR-PRESSO.MR-Egger Regression Intercept and Cochran's Qtest were used to assess the directional pleiotropy and heterogeneity. Results: cALT-associated NAFLD did not exhibit an association with kidney stones in the Inverse variance weighted (IVW) methods, in both the FinnGen consortium (OR: 1.02, 95%CI: 0.94-1.11, p = 0.632) and the UKBB study (OR: 1.000, 95%CI: 0.998- 1.002, p = 0.852). The results were consistent in European ancestry (FinnGen OR: 1.05, 95%CI: 0.98-1.14, p = 0.144, UKBB OR: 1.000, 95%CI: 0.998-1.002, p = 0.859). IVW MR analysis also did not reveal a significant causal relationship between NAFLD and the risk of kidney stone for the other three NAFLD-related traits, including imaging-based, biopsy-confirmed NAFLD, and more stringent biopsy-confirmed NAFLD. The results remained consistent and robust in the sensitivity analysis. Conclusions: The MR study did not provide sufficient evidence to support the causal associations of NAFLD with kidney stones. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) has significantly impacted international trade, benefiting national and international economies. It has increased trade and financial liberalization in many countries. Ecological and environmental protection have become critical issues due to the fast mobility of money and commodities. The purpose of this paper is to find a way to balance foreign trade growth with environmental protection. We aim to achieve this by using IoT to establish a co-integration model that takes into account the comprehensive environmental index, imports and exports, and the capital-labor ratio. We have collected data from Quanzhou City between 2011 and 2019, based on various environmental pollution indicators, import and export trade volume, capital-labor ratio, and other factors. Our analysis includes an examination of the empirical model of foreign trade and the ecological environment, as well as the impulse response. Additionally, we used prediction variance decomposition and the Granger causality test to understand the coordinated relationship between foreign trade and the ecological environment. Our results demonstrate that when the volume of exports (EX) increases by 1 percent, the ecological environment in Quanzhou City is negatively impacted by about 0.06%. On the other hand, when the volume of imports (IM) increases by 1 percent, there is a 0.26% positive effect on the ecological environment of the city. Finally, when the capital-to-labor ratio (K) increases by 1 percent, there is a 0.40% deterioration of the city's ecological environment. [ABSTRACT FROM AUTHOR]

This paper examines the extent to which uncertainty in the energy market, the financial market, the commodity market, the economic policy, and the geopolitical events affect crude oil returns. To consider the complex properties of time series, such as nonlinearity, temporal variability, and unit roots, we adopt a two-instrument technique in the time–frequency domain that employs the DCC-GARCH (1.1) model and the Granger causality test in the frequency domain. This allows us to estimate the dynamic transmission of uncertainty from various sources to the oil market in the time and frequency domains. Significant dynamic conditional correlations over time are found between oil returns—commodity uncertainty, oil returns—equity market uncertainty, and oil returns—energy uncertainty. Furthermore, at each frequency, the empirical results demonstrate a significant spillover effect from the commodity, energy, and financial markets to the oil market. Additionally, we discover that sources with high persistence volatility (such as commodities, energy, and financial markets) have more interactions with the oil market than sources with low persistence volatility (economic policy and geopolitical risk events). Our findings have significant ramifications for boosting investor trust in risky energy assets. [ABSTRACT FROM AUTHOR]

Background: Medical service prices play a crucial role in cost containment in China. This study aimed to assess the change in medical service price levels at the macro level and the relationship with relevant macroeconomic factors. Methods: Data from the 2022 China Statistics Yearbook, the 2022 China Health Statistics Yearbook, and the 2020 China National Health Accounts Report were used. Time trends of health price levels, utilization, and health expenditure were examined. A time-series regression model was employed to measure the impact of service utilization and medical service prices on total medical service expenditure growth from 2000 to 2021. The Johansen cointegration test was conducted to test the cointegrating relationship between medical service price levels and total medical service expenditure, average wage of employees and CPI. The Granger causality test was performed to observe the direction of causality. Results: Descriptive analyses showed consistent growth in utilization and medical service price levels from 2000 to 2021. The time-series model indicated that medical service expenditure was influenced by the rise in inpatient admissions and price levels of medical service and medicine. The Johansen cointegration test identified a long-term equilibrium relationship between medical service price levels and total medical service expenditure, average wage and CPI. The change in medical service price levels was the Granger cause of the change in medical service expenditure, but it had no impact on average wage and CPI. However, the change in medical service price levels was influenced by these three macroeconomic factors. Conclusions: The growth of medical service expenditure in China was driven by inpatient use and price level. There was a long-term equilibrium relationship between medical service price levels and relevant macroeconomic factors. However, medical service price levels only affected medical service expenditure and have no impact on average wage and CPI. It is necessary to improve the value transmission mechanism of medical service prices. [ABSTRACT FROM AUTHOR]

The present study examined the relationship between finance, government revenue, institutional quality and sustainable energy supply in West Africa countries over annual frequency period from 2012 to 2020. To achieve the outlined nexus between study variables, the present study leverages on a battery of panel analysis for robust inferences. The econometric estimators employed are panel random effect regression, generalized method of moment technique. Furthermore, panel Granger causality test is utilized to analyze the direction of flow among the variables for the study. Empirical results revealed that financial development is a significant determinant of energy supply in West Africa countries while a negligible effect was reported for institutional quality and sustainable energy supply. Thus, the present study concludes that finance from financial sector is important in ensuring sufficient energy supply. To this end, this study therefore, recommends that incentives should be given to financial institutions that fund energy generation and transmission as financial development is seen to be significant on energy supply. [ABSTRACT FROM AUTHOR]

Pakistan Railways is the largest federal organization to offer transport services for passengers and cargo. The significance of this enterprise is increased due to its characteristic of environment friendly mode of transportation. As the transportation work is run through some kind of fuel, mitigation of the adverse effects of the fuel on environment is a big challenge for the concerned authorities. Pakistan Railways is making great effort to play its role as a sustainable and environment-friendly mode of transport across the country. The sustainability of this organization solely depends on its revenue generation and this revenue generation is affected by certain factors. To analyze this sustainability, the relation between economic growth, inflation, and passenger revenue of Pakistan Railways is estimated. Time series data of twenty (20) years (1999-2018) is used, derived from the database of the World Bank's World Data Indicator. The Granger causality test is used to find out the correlation among any two or more variables. Empirical results indicate that there is no casual relation between economic growth and passenger revenue of Pakistan Railways. Whereas, bidirectional relation is found between Inflation and passenger revenue of Pakistan Railways. It indicates that Pakistan Railways can play a better role to increase the economic productivity of the country by adopting competitive passenger tariff and a consolidated transportation policy. That is equally beneficial for profitability thus sustainability of Pakistan Railways and the environment. [ABSTRACT FROM AUTHOR]

This study aims to econometrically investigate the relationship between non-performing loans and Euro and USD indices in Turkey. The data set of the study consists of the Turkish banking sector non-performing loan ratio and Euro and American Dollar (USD) indices variables for the period 01/2003-09/2023. Time series analyses were used to test the relationship between the variables. Augmented Dickey-Fuller (ADF) and Phillips-Perron (PP) unit root tests were applied to the data. Since the series became stationary at different levels, the ARDL (Autoregressive Distributed Lag Bound Test) test, one of the cointegration tests, was applied. Afterward, whether there is causality between the variables and if there is a causality relationship, the determination of the direction of causality is mutually tested with the "Granger Causality" method. According to the results of the research, a cointegration relationship was found between the non-performing loan ratios of the Turkish banking system and the Euro and USD indices. In the long run, it is concluded that a 1% increase in the Euro index is associated with a 2.93% decrease in non-performing loan ratios. On the other hand, in the short run, Euro and USD index values have no effect on non-performing loan ratios. According to the results of the Granger causality test, a unidirectional Granger causality relationship was detected between nonperforming loan ratios and the Euro index. A similar unidirectional causality relationship was also found between the Euro index and the USD index. [ABSTRACT FROM AUTHOR]

This article analyses the causality and volatility of the Colcap index of the Colombian Stock Exchange caused by Covid-19 infections and deaths. The methodology is econometric by estimating linear and non-linear Granger causality tests. The test results show that the stock market investors overreacted to COVID-19 infections and deaths. The non-linear causality test also determined that investors took the evolution of infections in the last three to 10 days and the number of deaths in the last 15 days into account for their investment decision. In contrast, the linear Granger test indicates that they considered the evolution of infections and deaths over the last 11 days. [ABSTRACT FROM AUTHOR]

The impact of shadow banking on financial stability remains a controversial issue today due to the size and complexity of these activities and the inadequate regulatory frameworks for systemic risks. In this context, shadow banking has become the focus of financial regulators due to its potential effects on financial stability. Therefore, this study was conducted to understand the impact of shadow banking on financial stability. In this study, the relationship between shadow banking and financial instability is examined using the VAR method for the example of the United States, covering the period 2000-2020. In order to create an accurate model, firstly unit root tests were performed, followed by autocorrelation and heteroscedasticity tests. The findings were found to be significant and Johansen cointegration test was applied. In the cointegration results, it was seen that the series were cointegrated, that is, they moved together in the long run. Finally, a Granger causality test was conducted between shadow banking and financial instability, and according to the empirical findings, it was concluded that there was a causality from shadow banking to financial instability for the period in question. [ABSTRACT FROM AUTHOR]

The study investigated the effect of exchange rate on economic growth in Nigeria using annual time series data from 1970 to 2021. The data were obtained from Statistical Bulletin published by the Central Bank of Nigeria 2021. The empirical studies reviewed had divergent views on the impact of exchange rate on economic growth in Nigeria. The study began with the test of unit root using Augmented Dickey-Fuller (ADF) and Philip-Peron (PP) unit root tests, followed by Johansen cointegration test, vector error correction model (VECM). The VECM indicated that exchange rate appreciation contracts output. The granger causality tests revealed that exchange rate and economic growth reinforce one another. However, based on these findings, it becomes imperative to build adequate foreign reserve to cushionoff trade shocks, promote import substitute industries, diversification of economy and enhance export oriented industries and reduction in cost of borrowing to enable domestic firms to benefit from the theoretical advantages of exchange rate variation. These measures promote the performances of macroeconomic variables in Nigeria. [ABSTRACT FROM AUTHOR]

This article aims to derive a consistent test of Granger causality at a given expectile. We also propose a sup-Wald test for jointly testing Granger causality at all expectiles that has the correct asymptotic size and power properties. Expectiles have the advantage of capturing similar information as quantiles, but they also have the merit of being much more straightforward to use than quantiles, since they are defined as least squares analog of quantiles. Studying Granger causality in expectiles is practically simpler and allows us to examine the causality at all levels of the conditional distribution. Moreover, testing Granger causality at all expectiles provides a sufficient condition for testing Granger causality in distribution. A Monte Carlo simulation study reveals that our tests have good finite-sample size and power properties for a variety of data-generating processes and different sample sizes. Finally, we provide two empirical applications to illustrate the usefulness of the proposed tests. [ABSTRACT FROM AUTHOR]

The purpose of this study is to examine the relationship between direct foreign investment and the competitiveness of the economies of the Western Balkan countries (Albania, Bosnia and Herzegovina, Macedonia, Montenegro, and Serbia) in the period between 2008 and 2017. The countries of the Western Balkan region were included in the analysis due to the fact that, in the last two decades, there has been a noticeable trend of increasing interest of foreign investors in investing in the countries of the region, both because of their good geographical position and because of the advantages they provide in terms of realising the basic, profit motivation of foreign investors. The research was conducted using the UNCTAD database, the international comparable base of data. First, an analysis of the dynamics of foreign direct investment flows in the countries of the Western Balkan region was performed. In order to examine the relationship between foreign direct investment and national competitiveness, Pearson’s linear correlation coefficient and Spearman’s rank correlation coefficient were applied. The Granger causality test was performed to assess the degree of influence of direct foreign investment on the competitiveness of the economies of the observed countries. The obtained research results not only contribute to the development of the existing literature on foreign direct investment and national competitiveness but also provide valuable knowledge to economic policy-makers about the possibilities of using the potential of foreign direct investments to improve the competitive and development performance of the national economy. [ABSTRACT FROM AUTHOR]

Causal inference aims to faithfully depict the causal relationships between given variables. However, in many practical systems, variables are often partially observed, and some unobserved variables could carry significant information and induce causal effects on a target. Identifying these unobserved causes remains a challenge, and existing works have not considered extracting the unobserved causes while retaining the causes that have already been observed and included. In this work, we aim to construct the implicit variables with a generator–discriminator framework named the Neural Causal Information Extractor (NCIE), which can complement the information of unobserved causes and thus provide a complete set of causes with both observed causes and the representations of unobserved causes. By maximizing the mutual information between the targets and the union of observed causes and implicit variables, the implicit variables we generate could complement the information that the unobserved causes should have provided. The synthetic experiments show that the implicit variables preserve the information and dynamics of the unobserved causes. In addition, extensive real-world time series prediction tasks show improved precision after introducing implicit variables, thus indicating their causality to the targets. [ABSTRACT FROM AUTHOR]