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Investors are using the stock market to invest their money for capital appreciation. Recent advancements in the information system have made stock market investments accessible to various investors. This study aims to examine the relationship between the stock price index and Gross Domestic Product (GDP) using empirical data, focusing on the short-term or long-term relationship, as well as the impact of available information on stock prices. The research is supported by theories such as the efficient market hypothesis, stock valuation theory, the balance sheet model, and Tobin's q theory. Secondary data from 1995-2022 were utilized, with quarterly data obtained from the Philippines Statistics Authority (PSA) and the Macrotrend. Net. Diagnostic tests were conducted to ensure there were no violations of normality, homoscedasticity, and serial correlation. Econometric tests were utilized to test the historical data, including the Augmented Dickey-Fuller (ADF) test for stationarity, the lag length test, the Johansen cointegration test, and the Granger causality test. The results indicate a positive long-term relationship between the stock price index and GDP. The Vector Error Correction Model (VECM) supports this finding, revealing that a 1.02% increase in the stock price index corresponds to a GDP increase. These findings support the balance sheet theory, indicating that wellperforming companies attract investment, leading to higher investment income and subsequent positive economic impacts. Additionally, the availability of information plays a role in stock prices, emphasizing the importance of information security for companies. Future research could explore the effects of specific stock classes on GDP, conduct comparative studies among different countries, or examine other economic indicators' influence on stock prices and the economy. Moreover, investigating the impact of information system advancements on stock prices and the overall economy presents an interesting avenue for further exploration. [ABSTRACT FROM AUTHOR]

Cryptocurrencies have become a trendy topic recently, primarily due to their disruptive potential and reports of unprecedented returns. Furthermore, social media has garnered attention for its predictive capabilities in various fields, including financial markets and the economy. In this study, we exploit the predictive power of sentiment from Twitter and Reddit, alongside Google Trends indexes, to forecast log returns for 10 cryptocurrencies, namely Bitcoin, Ethereum, Tether, Binance Coin, Litecoin, Enjin Coin, Horizen, Namecoin, Peercoin and Feathercoin. We evaluate the performance of LASSO Vector Autoregression using daily data from January 2018 to January 2022. In a 30-day recursive forecast, we achieve a mean directional accuracy (MDA) rate of over 50%. Moreover, we observe a significant increase in forecast accuracy in terms of MDA when using sentiment and attention variables as predictors, but only for less capitalized cryptocurrencies. This improvement is not reflected in the RMSE. We also conduct a Granger causality test using post-double LASSO selection for high-dimensional VAR models. Our results suggest that social media sentiment does not Granger-cause cryptocurrencies returns. [ABSTRACT FROM AUTHOR]

Value relevance can be defined as the ability of accounting variables to explain or predict financial outcomes. This study intends to examine the value relevance of accounting information, specifically the impact of accounting information on the stock prices of non-financial public companies listed on the Bulgarian Stock Exchange. The examined period is 01.01.2016 -- 31.12.2021. We apply the panel unit root test, descriptive statistics, the panel regression models, the correlation matrix, and the Granger causality test. The results collected demonstrate that the earnings per share, the dividend per share, and the book value of equity per share calculated for the analyzed companies have an impact on the stock price of these enterprises. A moderate positive correlation between stock price and other accounting variables is revealed. [ABSTRACT FROM AUTHOR]

The primary determinants of business cycle synchronization among OECD countries and their interactions are examined using panel Granger causality tests, single-equation panel regressions, simultaneous equations model error component three-stage least squares (EC3SLS), and cross-sectional 3SLS econometric techniques. The sample spans the years 1990 through 2021. The results demonstrate the direct and indirect effects of the key macroeconomic variables on the synchronization of the business cycles of the sample economies, as well as the complementary and competing nature of these variables. The magnitude and significance of these macroeconomic factors in relation to the business cycle synchronization before and after the global financial crisis of 2009 are also examined in this study. Furthermore, the analysis supports the ex-post argument for entering the euro currency union. The study article contributes by providing direction for future empirical research on the sources and propagation mechanisms of international business cycle transmission. [ABSTRACT FROM AUTHOR]

ABSTRACT This study examines the relationship between environmental quality, measured by carbon emissions (CO2), and life expectancy (LE) in 31 developing nations from 1981 to 2020. The analysis uses the panel autoregressive distributed lag (ARDL)/pooled mean group (PMG) approach. Panel cointegration tests suggest cointegration in the series. The results indicate that environmental quality has a significantly negative impact on LE, whereas real gross domestic product (GDP) per capita and the age dependency ratio have positive effects. The Dumitrescu–Hurlin Granger causality test shows a feedback relationship among all variables. These findings suggest the need for emission reduction policies to mitigate the adverse effects of CO2 emissions on public health. It is essential to address the negative impact of CO2 emissions on the environment and public health to achieve sustainable development goals. [ABSTRACT FROM AUTHOR]

The objective of this study is to ascertain the asymmetric impact of energy consumption on economic growth in Somalia for the period 1985–2017. A novelty econometric method of the Nonlinear Autoregressive distributed lag (NARDL) technique and the Granger causality test are employed to confirm the aim of the study. The empirical findings revealed the presence of asymmetric co-integration between energy consumption and economic growth in the long run. Both positive and negative shocks in energy consumption enhance economic growth in Somalia in the long run, even though, the positive shock has a stronger significant effect on economic growth in the long run. In contrast, a negative shock in energy consumption causes economic growth, thus, confirming the energy-led growth hypothesis. Hence, this calls for the policymakers to devise investment policies that are aimed at the improvement of foreign and local investments in energy sector. [ABSTRACT FROM AUTHOR]

Decoupling the level of new-type urbanization from carbon emissions is an essential representation of regional low-carbon sustainable development and a key indicator to ease resource and environmental constraints. By constructing a new-type urbanization evaluation index system and carbon emission measurement model, this paper takes 260 cities in China as research objects to investigate their horizontal pattern and spatial evolution characteristics from 2010 to 2022. Based on the Tapio decoupling model, decoupling catch-up model, and cointegration method, we discuss the dynamic evolutionary relationship between new-type urbanization construction and carbon emission of cities and the decisive factors affecting the decoupling state. The result shows that: (1) Both new-type urbanization and carbon emission levels showed an increasing trend in time, and their spatial distribution characteristics showed a "high-low–high" spatial pattern from east to west. (2) The number of cities that realized the decoupling of new-type urbanization and carbon emissions showed a U-shaped distribution, and the proportion of ordinary cities that realized the absolute catch-up was the highest among Tier III cities. (3) The cointegration and Granger causality test show that industrialization and energy consumption structure positively affect the stability of the decoupling state in the long term. At the same time, there is a negative relationship between technological progress and the stability of the decoupling state. (4) The level of new-type urbanization in the five dimensions of economy, society, environment, population, and space shows a trend of collaborative evolution in time and a specific differentiation in space. (5) The overall decoupling trend of Chinese cities is good, and the individual decoupling state of most cities fluctuates significantly. Coastal cities maintain a decoupling state during the study period, and their performance is relatively stable. The result can provide quantitative reference and decision-making basis for the construction of new-type urbanization and the implementation of dual-carbon strategy and also help promote the realization of high-quality decoupling and coordinated development of cities. [ABSTRACT FROM AUTHOR]

In recent years, with the enhancement of pollution control and ecological environmental protection, the quality of the ecological environment in China has continued to improve, but the problem of haze pollution with PM2.5 characteristics should not be underestimated. Daily PM2.5 data of 11 prefecture-level cities in Jiangxi Province from January 1 to December 31 in 2022 was selected, and the spatial spillover effects of haze pollution from a network perspective. The main findings are as follows: (1) The spatial correlation network of haze pollution for the 11 prefecture-level cities in Jiangxi Province is constructed using the Granger causality test. (2) The network characteristics were obtained using social network analysis. Partial result Nanchang emerges as the most significant media city in this network. (3) The network can divided into four distinct sectors, namely a sector representing net benefits, a sector indicating net overflow, a bilateral overflow sector and a broker sector. These four sectors have distinct geographical distributional characteristics. (4) The spatial correlation network of haze pollution in the 11 prefecture-level cities was found to be correlated with the level of urbanization difference by Quadratic assignment procedure (QAP) regression analysis, but the single urban population proportions did not pass the significance level test of 10%. Based on the analysis above, this paper considers that it is an important strategy for the Jiangxi Province haze control to establish a regional collaborative prevention and control mechanism, implement the differentiated regional governance strategy, and achieve the high-quality development of the whole province. [ABSTRACT FROM AUTHOR]

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

While research has explored the connection between oil prices and macroeconomic performance, there is limited understanding of the interaction between overseas energy imports dependency and macroeconomic fluctuations. This work addresses the foregoing study using the augmented mean group (AMG) econometrics technique on panel data of 29 OECD net energy‐importing countries from 2001 to 2021. The long‐run finding unearthed that energy dependency intensifies macroeconomic instability, confirming that relying on overseas energy imports will hamper the smooth functioning of domestic economic machinery in the form of energy supply interruption. Besides energy dependency, this study uses government consumption spending, regulatory quality, and globalisation as control variables in macroeconomic instability function. The long‐run outcomes further depict that government consumption spending and globalisation aggravate macroeconomic instability while regulatory quality reduces it. These findings are further checked by applying the pooled mean group‐autoregressive distributed lag (PMG‐ARDL) method. In conclusion, this study provides policy implications to prioritise domestically produced energy use and diversify energy sources by increasing the share of clean energy in the total energy mix. [ABSTRACT FROM AUTHOR]

In the field of development and spatial-economic literature, urbanization is considered a significant factor that brings about positive changes, stimulating welfare and economic advancement. However, there is a growing body of research that challenges this perspective, questioning the idea that urbanization leads to growth. Consequently, a number of recent studies have discovered limited or minimal evidence supporting the assumed positive impact of cities and urbanization on a nation's economic development. Our paper begins by considering the concept of "reverse causality" and aims to examine whether economic growth plays a significant role in driving structural urbanization within a country. In the current study, we utilize data spanning from 1971 to 2019 for 106 countries with varying economic levels and demographic dividend groups. We employ an augmented bootstrap panel Granger causality method to investigate the relationship between economic growth and urbanization. Our findings reveal a prevalent unidirectional causality, specifically from economic growth to urbanization, in a significant number of high- and upper-middle-income countries. Conversely, we observe an absence of such causal relationships in low- and lower-middle-income countries, as well as in post, late, and pre-demographic dividend countries. The results of the study suggest that urbanization might serve as more of an indicator rather than a driving force of economic development, as stated by Bloom et al. (Science 319:772–775, 2008). These findings carry important implications for both theoretical understanding and policy-making, particularly for developing countries in the global south that are currently experiencing rapid urbanization. These countries are allocating a significant portion of their resources toward the establishment of new cities and the revitalization of existing ones, potentially at the expense of investments in rural areas, for economic growth. The study's findings call into question the effectiveness of this approach and strategies in order to strike a balance between urban development and rural investment. [ABSTRACT FROM AUTHOR]

The link between the price of polluting energy supplies and the renewable energy stock index may be intricate and subtle. Investors need to conduct thorough inquiries and analyses to completely understand the correlation between these industries. This study aims to explore the intricate dynamics between renewable energy stocks and conventional fossil fuel-based resources. Our analysis encompasses daily data spanning from June 9, 2012, to March 31, 2023, extracted from the WilderHill New Energy Global Innovation Index (NEX), Crude Oil (WTI OIL), Coal (COAL), and Natural Gas (NGAS). Key statistical tests include the Johansen co-integration test, the fully modified ordinary least squares (FMOLS) test, and the Granger causality test. The study shows a long-term co-integration relationship between renewable energy stocks and polluting energy sources. Oil prices positively impact the renewable stock index, while coal prices negatively affect it. Natural gas prices also positively correlate with the index, and short-term relationships exist with oil prices. The study provides valuable insights for investors, state authorities, and policymakers on optimising ESG portfolio diversification and sustainable development policies in energy, environment, and climate. It helps stakeholders understand investor behaviour, market trends, and policy impacts, enabling effective long-term investment strategies. [ABSTRACT FROM AUTHOR]

This study aims to examine the relationship between interest rates and inflation in Pakistan, specifically considering the Fisher effect and the non-Fisher effect. The ongoing debate regarding whether raising interest rates contributes to higher inflation rather than effectively controlling it has motivated our research. To investigate this, we utilize time-varying vector auto-regressive and Granger causality tests. Our findings reveal the significance of the neo-Fisher effect, particularly from 2015 onwards, compared to the traditional Fisher effect in Pakistan. [ABSTRACT FROM AUTHOR]

In recent years, the quality of the environment has declined dramatically as a result of human activities, which threaten the sustainability of our ecosystem. In this context, a number of earlier studies have investigated the environmental problems through the lens of different environmental indicators such as ecological footprint and carbon dioxide emissions. However, it is possible that by taking these factors into account, the supply side of environmental quality will be neglected. This research seeks to address this deficiency by investigating the impact of green growth, energy efficiency, green technology innovation, economic growth, trade openness, and human capital on the level of load capacity factor. This indicator places equal emphasis on the supply and demand sides of the ecosystem by taking into account both ecological footprint and bio capacity. In this setting, the present study makes use of CS-ARDL model for the top 10 manufacturing countries from the years 1990 to 2019. The short- and long-run findings of the CS-ARDL model unveil that green growth, energy efficiency, green technology innovation, and human capital all positively influence the load capacity factor, suggesting that these factors help the top 10 manufacturing countries to improve their sustainability limits. In contrast, trade openness and economic expansion in underlying countries increase environmental degradation by lowering the level of LCF. These outcomes are also validated by the AMG and CCEMG approaches and the granger causality test. On the basis of the findings, important policy recommendations are presented with the aim of assisting the top 10 manufacturing economies in enhancing the quality of their environment and fulfilling the United Nations Sustainable Development Goals. [ABSTRACT FROM AUTHOR]

West Africa has been grappling with various security challenges prompting governments' intervention via military expenditure both at the country and regional level. Given the sporadic surge in military expenditure in the region and the potential effect such expenditure may exert on investment which is a sin qua non to the development process of any economy or region, this study utilizes the augmented mean group (AMG) approach and the Granger non-causality test in investigating the impact and causal relationship between investment and military expenditure in the Economic Community of West African States (ECOWAS) between 1980 and 2020. With country-specific cross-dependence and heterogeneity adequately accounted for, the study found that military expenditure has a dampening impact on investment at both the panel and country levels; unemployment adversely impacts investment; whereas economic growth stimulates investment (catalytic effect) in the ECOWAS, although at differing levels of significance. The study also established a long-run relationship among the variables; with only economic growth Granger causing investment. The study thus recommends that country-specific and regional-based military policies be established to glean economic growth through viable investment. [ABSTRACT FROM AUTHOR]

This paper investigates the nature of the associations and the potential existence of both short-run and long-run relationships between the stock market indices of Morocco, France, Germany, the United Kingdom, China, and the United States from January 2014 to January 2024. The purpose of analyzing dynamic interconnections and contagion effects is to determine how the stock markets of these countries influence and relate to each other. The study employs a time series Vector Error Correction Model (VECM) approach, incorporating stationarity, cointegration, and Granger causality tests. Additionally, the Impulse Response Function (IRF) is used to analyze the response of variables to shocks. The bivariate Granger causality test reveals significant causal influences: from France, Germany, and the USA to Morocco; from the USA to the DAX and France; and from the UK to Germany. After establishing the Granger causal relationships, long-run and short-run relationships are further examined. Using the Johansen multivariate cointegration approach, the study suggests a long-term equilibrium among the six stock market indices over time. The short-run adjustments are analyzed using the VECM, which reveals that adjustments in the CAC 40, DAX, and MASI tend to correct deviations from equilibrium, indicating a tendency to move towards equilibrium. For the FTSE 100, S&P 500, and SSEC, the VECM captures the speed and direction of adjustments as these indices respond to short-term disruptions and work towards restoring equilibrium. The findings underscore the importance of closely connected global stock markets, which means that international regulators must coordinate their efforts to reduce the risks of contagion. Policymakers should prioritize improving financial stability through integrated frameworks considering short-term disruptions and long-term equilibrium trends. [ABSTRACT FROM AUTHOR]

AbstractIn this paper, we consider the Europe oil prices, Brent, West Texas Intermediate, WTI, American stock market index based on the market capitalizations of 500 large companies having common stock listed on the NYSE or NASDAQ, S&P500 , and the real gross domestic product, GDP. Based on the rolling test of Granger causality, the Europe oil prices data does Granger-cause the real gross domestic product data and S&P500 data. So, the vector autoregressive model can be suggested. The one-step prediction method selects the vector autoregressive model as the optimal model for GDP data and S&P500 data. The main purpose of this paper is to select the optimal model (and variable) based on Vuong’s model selection test. So, we consider the estimation and model selection of the vector autoregressive time series model with Normal innovation. The maximum likelihood method is proposed for the estimation of unknown parameters. We provide Vuong’s test for order and model selection, i.e. for selecting the order and a suitable subset of regressors, in a vector autoregressive model. Our results show that Vuong’s test confirms the causality test for GDP and S&P500 data. Also, the results of Vuong’s test confirms the results of the predictive method. [ABSTRACT FROM AUTHOR]

In today's increasingly interconnected globe, there continues to be a pressing need for thorough research that can shed light on the causes of HIV-related mortality and the primary factors at play in this epidemic. By using an annual dataset, this study analyzed the impact of behavioral risk factors, income, and human capital on HIV-related deaths (HIVD) across all countries and regions. The empirical findings of the panel regression demonstrate that drug use and unsafe sex have a statistically significant and positive effect on HIVD. On the other hand, mean years spent in school and gross national income per capita both have a statistically significant and negative influence on HIVD. The findings of the variance decomposition suggest that drug use should be confronted in wealthy nations to lower HIVD, but both human capital and economic development have a greater influence on the battle against HIVD in underdeveloped and developing countries. In addition, the findings of the panel Granger causality test demonstrate that factors other than unsafe sex can be used as leading indicators in the process of forecasting HIVD. According to these findings, economic variables, as well as behavioral risk factors in emerging nations, and in industrialized countries, should be taken into account when formulating strategies for preventing HIV/AIDS. [ABSTRACT FROM AUTHOR]

This paper examines the relationship between political instability, economic growth, and Greece's overall stock market index (ASE) returns from 1970 to 2018. The aim is to measure and quantify the impact of political instability on economic growth and the overall stock market returns. The analysis includes six different variables as proxies that quantify political instability. A new Socio-Political Instability index is constructed from these variables by employing the principal component analysis. The empirical part employs GARCH, GARCH-M, Granger causality tests and OLS regressions. The paper provides new insightful results and a new measure of political instability for Greece. [ABSTRACT FROM AUTHOR]

This paper examines the relationship between insurance development, population, economic growth, and health expenditures for a panel of 31 OECD countries over the period 1995–2021. A dynamic panel data analysis based on cointegration, FMOLS, DOLS, VECM, and Granger causality tests is employed to suggest for the existence of a long-run relationship among variables. The long-run results show that insurance development, national income, and population exhibit positive impacts on health expenditures. The results reveal that insurance factor has larger income effects than substitution effects on health expenditures. Regarding the short-run causal relationship between the variables, the empirical results suggest that economic growth strengthens health expenditure growth, while insurance growth reduces it. In the short-run, insurance development has a crowding-out effect since it produces larger substitution effects than income effects. The results provide political implications that governments need to concern the short-run crowding-out effect of private insurance sections on health expenditures when making fiscal policies on public health expenditures. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Testing for Granger causality relies on estimating the capacity of dynamics in one time series to forecast dynamics in another. The canonical test for such temporal predictive causality is based on fitting multivariate time series models and is cast in the classical null hypothesis testing framework. In this framework, we are limited to rejecting the null hypothesis or failing to reject the null – we can never validly accept the null hypothesis of no Granger causality. This is poorly suited for many common purposes, including evidence integration, feature selection, and other cases where it is useful to express evidence against, rather than for, the existence of an association. Here we derive and implement the Bayes factor for Granger causality in a multilevel modeling framework. This Bayes factor summarizes information in the data in terms of a continuously scaled evidence ratio between the presence of Granger causality and its absence. We also introduce this procedure for the multilevel generalization of Granger causality testing. This facilitates inference when information is scarce or noisy or if we are interested primarily in population-level trends. We illustrate our approach with an application on exploring causal relationships in affect using a daily life study. [ABSTRACT FROM AUTHOR]

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

Given the critical role of the clean energy market in the global economy and environmental sustainability, this paper investigates the impact of the U.S. Business Conditions Index (ADS) on the risk of segmented clean energy markets across different time scales and market conditions, as well as its spillover mechanisms. By using wavelet coherence and wavelet quantile analysis, we examine how the Aruoba–Diebold–Scotti (ADS) Business Conditions Index affects the risk levels of segmented clean energy indices under varying market conditions. To further understand this impact mechanism, we also employ the quantile Granger causality test to analyze the spillover effects of ADS on the clean energy market. The results show that the ADS index significantly influences the risk levels of segmented clean energy markets, with notable differences across various time scales and market conditions. The contributions of this study include: (1) segmenting the measurement of clean energy market risk into the Solar Index (SOLAR), Renewable Energy Index (RE), Biomass Index (BIO), Wind Energy Index (WIND), and Clean Energy Index (WILDER); (2) providing new evidence on the impact of the ADS Business Conditions Index on segmented clean energy market risk; and (3) offering new perspectives for different clean energy market participants to better navigate complex business environments and develop effective risk management strategies. [ABSTRACT FROM AUTHOR]

China has taken sustainable development strategies seriously in recent years, aiming at reducing energy consumption and environmental pollutants emissions. This research empirically evaluates the sustainable innovation efficiency (SIE) in Chinafrom the perspective of energy and environmental constraints. Furthermore, the relationship between SIE and economic growth is tested through Granger causality test. The results indicate that SIE in China varies obviously in different regions. Granger causality runs only from economic growth to SIE and not the other way round. Economic growth is causative factor of sustainable innovation, indicating that China's sustainable innovation has not yet achieved coordinated development with the economy. Our findings also provide useful decision-supporting insights for Chinese policymakers to promote coordinated development of regional sustainable innovation and economic growth. [ABSTRACT FROM AUTHOR]

Ecotourism empowering harmonious and beautiful countrysides is conducive to enhancing residents' happiness. On the basis of defining the connotation of harmonious and beautiful countrysides and clarifying the internal mechanism of enhancing residents happiness through ecotourism empowering harmonious and beautiful countrysides, this paper explores the implementation path of ecotourism empowering harmonious and beautiful countrysides to enhance residents' happiness in Zhangjiajie from 2000 to 2022 using entropy method, Granger causality test, structural equation modeling and obstacle degree model. It is found as follows. ➀ From 2000 to 2022, the overall development levels of harmonious and beautiful countrysides and residents happiness in Zhangjiajie showed increasing trend. The evaluation value of harmonious and beautiful countrysides increased from 0.029 in 2000 to 0.852 in 2022, and the evaluation value of residents' happiness increased from 0.181 in 2000 to 0.738 in 2022. ➁ There is a long-term stable equilibrium relationship between harmonious and beautiful countryside and residents happiness, and harmonious and beautiful countrysides is the one-way Granger reason for residents happiness. Ecotourism can significantly improve the residents' happiness through employment growth, industrial upgrading, urban-rural integration empowering harmonious and beautiful countrysides. ➂ In terms of obstacle degree, the main obstacle factors to harmonious and beautiful countrysides are effective governance and rural civilization, with the average obstacle degrees of 39.25% and 25.33%, respectively. The main obstacle factor to residents' happiness is development, and the average obstacle degree is 30.92%. ➃ The implementation path of ecotourism empowering harmonious and beautiful countrysides to enhance residents' happiness is proposed as follows: the first is to coordinate the elements of rural ecological resources and enhance the absorption capacity of rural employment; the second is to promote the integrated development of agriculture and tourism and realize industrial transformation and upgrading; and the third is to build an ecotourism cooperation platform and promote the coordinated development of urban and rural areas. [ABSTRACT FROM AUTHOR]

This study examines the relationship between Poland's stock prices and three key macroeconomic factors: GDP growth, inflation, and interest rates. Using econometric tools like Granger causality tests, Vector Autoregression (VAR), and Impulse Response Functions (IRF), I assessed how these factors influence the Warsaw Stock Exchange (WIG Index) using quarterly data (2005-2024). The results showed a significant negative connection between interest rates, inflation, and stock prices: a 1% rise in interest rates leads to a 0.85% drop in stock prices in the following quarter. Inflation also negatively affects stock prices, with a 1% increase leading to a 0.40% decrease. Conversely, GDP growth positively impacts stock prices, where a 1% rise in GDP results in a 0.6% increase. The Granger causality test underscores the importance of monetary policy, showing that interest rate changes may predict stock market movements. Inflation is important; however, it is less predictive than GDP growth, which has a moderate positive impact on stock prices. These findings highlight the sensitivity of Poland's stock market to macroeconomic changes, offering valuable insights for analysts, investors, and policymakers. Future research could include additional factors like exchange rates or international trade to deepen the analysis. [ABSTRACT FROM AUTHOR]

Accurate prediction of the future evolution of observational time series is a paramount challenge in current data‐driven research. While existing techniques struggle to learn useful representations from the temporal correlations, the high dimensionality in spatial domain is always considered as obstacle, leading to the curse of dimensionality and excessive resource consumption. This work designs a novel structure‐aware reservoir computing aiming at enhancing the predictability of coupled time series, by incorporating their historical dynamics as well as structural information. Paralleled reservoir computers with redesigned mixing inputs based on spatial relationships are implemented to cope with the multiple time series, whose core idea originates from the principle of the celebrated Granger causality. Representative numerical simulations and comparisons demonstrate the superior performance of the approach over the traditional ones. This work provides valuable insights into deeply mining both temporal and spatial information to enhance the representation learning of data in various machine learning techniques. [ABSTRACT FROM AUTHOR]

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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. [ABSTRACT FROM AUTHOR]

It is well established that financial development and innovation promote economic growth through improving the allocation of capital, enhancing risk management, contributing to price discovery, and increasing market efficiencies. While a vast empirical literature is devoted to the nexus between financial development and economic growth, however, substantially less research has been done on the relationship between derivatives and growth, especially in the emerging-market context. Derivatives can be viewed as a specific category of financial innovation, which may advance economic growth through its specialised functions of risk management and price discovery. This paper contributes to bridging this gap in the literature by exploring the impact of exchange-traded futures derivatives on South African economic growth, output, and economic growth volatility. It employs ARDL bounds tests, Granger causality tests and GARCH volatility modeling to analyse the effects of exchange-traded futures derivatives on various measures of South African economic activity. The main result is that exchange-traded futures derivatives contribute positively to South African economic growth and economic activity. This may suggest that opportunities might exist in other emerging economies, with financial structures comparable to that of South Africa, to encourage the development of organised and well-regulated derivatives markets to unlock economic growth in these economies. [ABSTRACT FROM AUTHOR]

Clusters of knowledge-intensive industries and manufacturing industries form industrial agglomeration in Step I and activate innovation in Step II. Industry clusters are formed by building segments. "Construction sequencing" in the construction industry refers to the process of determining the sequence of segments to optimize a project's resources, budget, and scheduled timeline. The process usually begins by dividing a project into segments. Urban segments consist of public spaces, airports, factories, health, housing, etc. A "segment" is a component of a cluster; the organization of a cluster consists of constructing segments. These segments can be divided into four main categories: human resources, physical infrastructure, institutions, and the living environment. Each segment has a specific function in the process of building a cluster. This study focused on innovation in Step II and extended the Fujita–Thisse model of spatial economics to hypothesize that research expenditure per researcher leads to value being added. The Granger causality was tested for the knowledge and manufacturing industries in nine major countries including China and the U.S. The results showed that the hypothesis was significant in identifying the starting segment of innovation in Step II. Accordingly, it can be concluded that research funding is the start switch that triggers innovation. The policy implication is that activating innovation in cluster policies begins with the establishment of a research fund for researchers in its assigned clusters. [ABSTRACT FROM AUTHOR]

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

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

Qatar has experienced a remarkable surge in all four components of gross domestic product (GDP)—consumption, government spending, investment, and net exports as well as significant growth in population over the past thirty years. Despite a potential link between increasing production and consumption and a corresponding rise in CO2 emissions in Qatar, there exists a significant gap in the literature addressing this linkage. Using sectoral data, this article investigates the long-term trends of GDP growth and expenditure and their nexus with CO2 emissions in Qatar. Least square regression analysis (multiple regression approach) and Granger Causality tests have been used to examine the long-term association between GDP-expenditure and its components and CO2 emission patterns. Additionally, the study discusses the environmental implications and the necessity for effective natural resource management in climate policy. Our analysis reveals consistent average annual increases in Qatar over the past three decades: 4.8% in total expenditure, 5.4% in GDP, 4% in government spending, 4.7% in household consumption, and 1.77% in CO2 emissions. During this period, production- and consumption-based emissions surged by 700%, while consumption-based emissions rose by 500%. The energy sector alone accounts for 86% of Qatar's total CO2 emissions. Significant Granger causality was found between CO2 emissions and various economic growth and GDP components, suggesting a potential inverted U-shaped Kuznets Curve, indicating a possible decline in CO2 emissions in future. However, achieving this decline in CO2 emissions in accordance with UN SDG Goal 12 requires additional policy measures focussed on carbon capture and storage, circular economy practices, carbon pricing, and complementary climate policies. Specific policy actions include establishing CO2 observatories, promoting clean energy initiatives, and engaging the private sector. [ABSTRACT FROM AUTHOR]

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

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

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

Climate data collected through Internet of Things (IoT) devices often contain high‐dimensional, nonlinear, and auto‐correlated characteristics, and general causality analysis methods obtain quantitative causality analysis results between variables based on conditional independence tests or Granger causality, and so forth. However, it is difficult to capture dynamic properties between variables of temporal distribution, which can obtain information that cannot be obtained by the mean detection method. Therefore, this paper proposed a new causality analysis method based on Peter‐Clark (PC) algorithm and modified local Granger causality (MLGC) analysis method, called PC‐MLGC, to reveal the causal relationships between variables and explore the dynamic properties on temporal distribution. First, the PC algorithm is applied to compute the relevant variables of each variable. Then, the results obtained in the previous stage are fed into the modified local Granger causality analysis model to explore causalities between variables. Finally, combined with the quantitative causality analysis results, the dynamic characteristic curves between variables can be obtained, and the accuracy of the causal relationship between variables can be further verified. The effectiveness of the proposed method is further demonstrated by comparing it with standard Granger causality analysis and a two‐stage causal network learning method on one benchmark dataset and two real‐world datasets. [ABSTRACT FROM AUTHOR]

During the past several decades, the world has been putting strong emphasis on the role of environmental technology innovation which can reduce environmental degradation and promote sustainable development. This research sheds light on the impact of green technology or eco innovation on sustainable development for China. Additionally, the effects of other variables such as governance, information and communication technology, trade uncertainty and urban population are also analysed. The study employs Fully modified ordinary least squares (FMOLS) and Canonical Cointegration Regression (CCR) as the long run tests and Breitung and Candelon Spectral Granger-Causality as the time varying granger causality test. The results suggest that green innovation promotes sustainable development in the long run. The roles of governance quality and urbanization are also found to be positive while the role of ICT is found to be insignificant yet negative. The role of trade uncertainty is positive yet insignificant. Based on these results, the study calls for the private and public organizations and industries to invest in eco innovation. Different incentives from the government can also encourage private industries to invest in green innovation technologies. [ABSTRACT FROM AUTHOR]

Financial cycles are widely regarded as key drivers of global financial conditions and major contributors to international financial crises. Understanding the extent to which these cycles are interconnected is essential for developing early warning systems to predict financial crises and for informing the design of macroprudential and monetary policies. In this context, a dynamic factor model and time-varying Granger causality (TVGC) are employed to assess the degree of financial cycle synchronization between advanced systemic economies (ASEs) and systemic middle-income countries (SMICs). Our data spans the period from 1980Q1 to 2023Q4. The analysis reveals that a common factor--shared by both advanced and systemic middle-income economies--plays a significant and persistent role in driving financial cycles across these regions. Notably, the influence of this common factor intensified following the 2007-2009 global financial crisis, highlighting the growing financial interconnectedness between these economies. These results strongly support the argument for closer coordination of macroprudential policies between advanced systemic economies and SMICs. The findings from the TVGC analysis further reinforce the conclusions drawn from the dynamic factor model. Specifically, the TVGC results indicate a bidirectional causality between the financial cycles of advanced systemic economies and SMICs, suggesting mutual influence. This bidirectional relationship underscores the importance of enhanced policy coordination across these economies to mitigate systemic risks. [ABSTRACT FROM AUTHOR]

Copyright of Bulletin of Accounting & Finance Reviews / Muhasebe ve Finans İncelemeleri Dergisi is the property of Muhasebe & Finans Incelemeleri Dergisi 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.)

Domestic and Foreign institutional investors are professional investors who aim to profit from price movements in the stock market. These investors not only profit from their investments but also drive the stock market of any country. The study examines the association between Sensex Nifty 50’s movements and investing patterns of FIIs, DIIS and the causal relationship between stock market returns and institutional investments (FIIS and DIIs). The study also analyses the impact of FIIs and DIIs in driving the Indian stock market. The study employed correlation analysis, vector autoregression model, Block exogeneity Wald-test and the Granger causality test for the objectives. Our results indicate the gradual fall in the FIIs-DIIs ratio in the Indian stock market, making domestic institutional investors a prominent counterforce to FIIs and establishing it as an equal driver. DIIs act as positive feedback traders, while FIIs act as neutral to negative traders. The causality test demonstrates that market return has a significant impact on both the players, but neither FIIs nor DIIs have a significant impact on market movement [ABSTRACT FROM AUTHOR]

Copyright of Environmental & Social Management Journal / Revista de Gestão Social e Ambiental is the property of Environmental & Social Management Journal 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.)

We examine the effect of options trading on an optioned firm's investment decisions. We find that active options trading improves an optioned firm's investment efficiency, and that this effect holds under several alternative empirical specifications and identification strategies, including fixed‐effects models, different matching methods, an instrumental variable approach, a Granger causality test and a quasi‐natural experiment based on the listing decisions of the options exchanges. This relation is mediated by two factors, namely, information asymmetry and uncertainty, consistent with the notion that options trading improves investment efficiency by providing information that facilitates external monitoring and managerial learning. The results of cross‐sectional analyses indicate that the effect of options trading on investment efficiency increases with firms' tendency to overinvest or underinvest, and with managers' risk‐taking and learning incentives. We also demonstrate that the effect of options markets on investment efficiency is distinct from the effect of stock markets. Overall, our findings suggest that options trading plays a nonnegligible role in improving an optioned firm's investment efficiency. [ABSTRACT FROM AUTHOR]

Recent advancements in cryptography and digital assets have triggered a profound transformation, causing a reorganization of the financial sector. The growing popularity of cryptocurrencies as investment instruments has raised concerns regarding the influencer mentions on social media impact on their price dynamics. This study assesses the discourse surrounding major cryptocurrencies and tokens within the decentralized finance (DeFi) and masternode sectors, examining its influence on prices and trading volumes during the period from 2019 to 2021. The study verifies the alignment between social media communications concerning these assets and their intended objectives. The analysis utilizes KHCoder software to explore topics with the highest public engagement, revealing that DeFi users frequently exhibit herd-like behavior by actively pursuing high-return farming trends, often at the expense of unique project attributes. In contrast, discourse surrounding masternode tokens more closely adheres to project objectives. The study employs panel models and Granger causality tests to investigate the relationship between cryptocurrency market performance, interactions on social media, and tweet sentiment. The findings substantiate a causal and temporal connection between micro-discourses on Twitter and asset prices, as well as trading volumes. [ABSTRACT FROM AUTHOR]

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

China's population has been aging quickly in recent years, putting pressure on the environment. Population aging has also amplified the role of industrial upgrading and research and development (R&D) on environmental quality. Against this backdrop, we investigated the long-run and short-run relationships among population aging, industrial structure upgrading, R&D, and air quality in 30 Chinese provinces from 2006 to 2020. The panel autoregressive distributed lag (ARDL) model was first applied in this study. The results revealed that population aging temporarily harmed air quality but improved it over time. The favorable impact of industrial upgrading on air quality was enhanced by population aging, but the positive impact of R&D investment on air quality was reduced. Furthermore, Dumitrescu and Hurlin (D-H) Granger causality tests were adopted to confirm that population aging had a single-way causal link with air quality, whereas industrial upgrading and R&D had dual-way causal associations with air quality. [ABSTRACT FROM AUTHOR]