Search

Quadratic Phase Coupling (QPC) serves as an essential statistical instrument for evaluating nonlinear synchronization within multivariate time series data, especially in signal processing and neuroscience fields. This study explores the precision of QPC detection using numerical estimates derived from cross-bicoherence and bivariate Granger causality within a straightforward, yet noisy, instantaneous multiplier model. It further assesses the impact of accidental statistically significant bifrequency interactions, introducing new metrics such as the ratio of bispectral quadratic phase coupling and the ratio of bivariate Granger causality quadratic phase coupling. Ratios nearing 1 signify a high degree of accuracy in detecting QPC. The coupling strength between interacting channels is identified as a key element that introduces nonlinearities, influencing the signal-to-noise ratio in the output channel. The model is tested across 59 experimental conditions of simulated recordings, with each condition evaluated against six coupling strength values, covering a wide range of carrier frequencies to examine a broad spectrum of scenarios. The findings demonstrate that the bispectral method outperforms bivariate Granger causality, particularly in identifying specific QPC under conditions of very weak couplings and in the presence of noise. The detection of specific QPC is crucial for neuroscience applications aimed at better understanding the temporal and spatial coordination between different brain regions. [ABSTRACT FROM AUTHOR]

In line with Africa's commitment to keeping up with the United Nations Framework Convention on Climate Change, achieving a sustainable future requires balancing economic growth with environmental sustainability. This study investigates the long-term impacts of foreign direct investment, economic growth, agricultural production, and energy consumption on CO2 emissions across 43 African nations from 1990 to 2021. Despite significant research on the individual effects of these factors, the combined influence on CO2 emissions remains underexplored. Addressing this gap, this study employs cross-sectional augmented distributed lag estimators (CS-DL and AMG) and updated estimation packages to effectively examine the relationships between variables. Our findings are as follows: firstly, economic growth and energy use was shown to have a significant positive influence on CO2 in the long term. Also, foreign direct investment significantly promotes CO2 emissions. Secondly, the causality test shows a unidirectional causal relationship between CO2 emissions and foreign direct investment. The test also revealed a bidirectional relationship between GDP and CO2 emissions, as well as between energy consumption and CO2 emissions. Again, a bidirectional causation was observed between agricultural production and CO2 emissions. Thirdly, the impulse response analysis shows that GDP will contribute more to emissions over the 10-year forecast period. This study also proposes policy implications to lessen CO2 across the continent and advocates for the judicious adoption of existing policy frameworks like the 2030 Agenda for environmental Sustainability. [ABSTRACT FROM AUTHOR]

In this article, we explore the relationships between economic growth and energy intensity in Bahrain, and we analyse how energy intensity, population, and other macroeconomic variables are associated with each in the short and long run. In the empirical section we use data span from 1980 to 2019 and we perform a vector error correction model and cointegration technique to detect causality between the variables used in the model. The main findings of this article show that economic growth, population and FDI increased energy intensity in Bahrain in the short run. Therefore, an urgent need to be embedded in the sustainable energy action plan to limit the harmful effects of economic activity on the environmental quality in Bahrain and to ensure environmental sustainability. [ABSTRACT FROM AUTHOR]

The current research examines the interlink within G-20 economies. Advanced econometrics methods were employed in the panel data collected from 1990 to 2019. Within an employed method, co-integration, panel unit tests, and vector error models were considered, unveiling long-term interactions and the casual interlink between proposed variables. This method is essential to highlight the complicated relationship between energy usage and economic factors. It also supports the global efforts to minimize environmental degradation and fossil fuel usage to achieve the Paris Agreement goals. The current study's findings show a significant and robust long-term equilibrium between primary energy consumption, financial growth, price of energy, and trade openness. The findings further suggest that fluctuations in primary energy consumption directly impact financial growth, with trade openness being essential to this relationship. Renewable energy sources also play a vital role in mitigating carbon emissions that precisely explain sustainable financial growth without comprising the protection of environmental integrity. For the member countries of the G-20, the findings emphasize improving energy efficiency, funding for renewable energy, and maintaining stable energy prices to promote sustainable financial growth. This paper contributes to the existing literature examining the co-integration between renewable energy consumption, financial growth, and trade openness for the long run, stresses the necessity of comprehensive energy policies, and employs superior econometric estimation techniques. [ABSTRACT FROM AUTHOR]

Coordinating the relationship between the digital economy, green innovation, and the urban-rural income gap is conducive to promoting common prosperity in high-quality development. This study aims to show that, in the short term, the development of the digital economy will promote the level of green innovation, which, in turn, will promote the development of the digital economy, but will also widen the urban-rural income gap. The study uses panel data of 273 prefecture-level cities from 2011 to 2019, and adopts the panel vector error correction model for quantitative analysis, combined with the theoretical analysis of the long- and short-term causal relationship among the digital economy, green innovation, and urban-rural income gap. The results reveal that, in the long term, the digital economy, green innovation, and urban-rural income gap demonstrate a double-circular causality that is positively promoting each other. The robustness test validates the conclusions. Therefore, while promoting the development of the digital economy and green innovation, the government should control the urban-rural income gap within a reasonable range, to provide theoretical and practical support for promoting the sustainable development of China's economy and realizing the goal of common prosperity. [ABSTRACT FROM AUTHOR]

EEG signals capture information through multi-channel electrodes and hold promising prospects for human emotion recognition. However, the presence of high levels of noise and the diverse nature of EEG signals pose significant challenges, leading to potential overfitting issues that further complicate the extraction of meaningful information. To address this issue, we propose a Granger causal-based spatial–temporal contrastive learning framework, which significantly enhances the ability to capture EEG signal information by modeling rich spatial–temporal relationships. Specifically, in the spatial dimension, we employ a sampling strategy to select positive sample pairs from individuals watching the same video. Subsequently, a Granger causality test is utilized to enhance graph data and construct potential causality for each channel. Finally, a residual graph convolutional neural network is employed to extract features from EEG signals and compute spatial contrast loss. In the temporal dimension, we first apply a frequency domain noise reduction module for data enhancement on each time series. Then, we introduce the Granger–Former model to capture time domain representation and calculate the time contrast loss. We conduct extensive experiments on two publicly available sentiment recognition datasets (DEAP and SEED), achieving 1.65% improvement of the DEAP dataset and 1.55% improvement of the SEED dataset compared to state-of-the-art unsupervised models. Our method outperforms benchmark methods in terms of prediction accuracy as well as interpretability. [ABSTRACT FROM AUTHOR]

This paper examines the asymmetric impact of financial development on economic growth in Mauritius during the period 1980–2021. The analyses were carried out using a nonlinear autoregressive distributed lag (NARDL) model in conjunction with additional complementary tests, including the NARDL bounds F-test for cointegration, the Brock-Dechert-Scheinkman (BDS) nonlinearity test, and the Wald test for asymmetries. The findings of the bounds F-test provide support for a nonlinear cointegration, whereas the BDS test shows the presence of nonlinearity in the data for all the variables. The Wald test results revealed an asymmetric relationship between financial development and economic growth in Mauritius, both in the short and long run. The NARDL findings show that, on average, positive changes in financial development lead to economic growth in the long and short run. Therefore, the paper encourages the government of Mauritius to continue pursuing policies aimed at the expansion of its financial sector since it has a significant positive impact on its economic growth. [ABSTRACT FROM AUTHOR]

Atmospheric motion vectors, which can be used to infer wind speed and direction based on the trajectory of cloud movement, are instrumental in enhancing atmospheric wind-field insights, contributing notably to wind-field optimization and forecasting. However, a widespread problem with vector data is their inaccuracy, which, when coupled with the mediocre effectiveness of existing correction methods, limits their practical utility in forecasting, often falling short of expectations. Deep-learning techniques are used to refine atmospheric motion vector data from the FY-4A satellite, notably enhancing data quality. Post-training data undergoes a thorough analysis using a quality evaluation function, followed by its integration into a numerical weather prediction system in order to conduct forecasting experiments. Results indicate a marked improvement in data quality post-error correction by the model, characterized by a significant reduction in root mean square error and a notable increase in correlation coefficients. Furthermore, refined data demonstrate a considerable enhancement in the accuracy of meteorological element forecasts, particularly for Asian and Western Pacific regions. [ABSTRACT FROM AUTHOR]

Energy, being one of the significant drivers of growth, has gained much focus in the last four decades. A connection between energy and growth exists in the economy: on the one hand, energy is a vital factor of production while growth improves living standards, which increases energy consumption on the other hand. Previous studies on the energy‐growth nexus paid less attention to sectorial differentials in the consumption of energy and growth. Also, the literature could not relate that to sustainable development. Thus, this study explores the sectorial differentials in energy consumption and how they affect the energy‐growth nexus within the context of sustainable development. The research employed secondary data for 46 sub‐Saharan Africa (SSA) countries for the period 2007 to 2020. Using the General Methods of Moments estimation technique, we found that all the measures of energy consumption positively affect economic growth. Also, CO2 intensity is positively related to growth in the economy while CO2 emissions from solid fuels negatively affect economic growth. Again, Foreign Direct Investment (FDI) is positively impacting economic growth and Gross Domestic Product (SSA) per energy use. Policy implications are discussed in the paper. [ABSTRACT FROM AUTHOR]

Quadratic Phase Coupling (QPC) serves as an essential statistical instrument for evaluating nonlinear synchronization within multivariate time series data, especially in signal processing and neuroscience fields. This study explores the precision of QPC detection using numerical estimates derived from cross-bicoherence and bivariate Granger causality within a straightforward, yet noisy, instantaneous multiplier model. It further assesses the impact of accidental statistically significant bifrequency interactions, introducing new metrics such as the ratio of bispectral quadratic phase coupling and the ratio of bivariate Granger causality quadratic phase coupling. Ratios nearing 1 signify a high degree of accuracy in detecting QPC. The coupling strength between interacting channels is identified as a key element that introduces nonlinearities, influencing the signal-to-noise ratio in the output channel. The model is tested across 59 experimental conditions of simulated recordings, with each condition evaluated against six coupling strength values, covering a wide range of carrier frequencies to examine a broad spectrum of scenarios. The findings demonstrate that the bispectral method outperforms bivariate Granger causality, particularly in identifying specific QPC under conditions of very weak couplings and in the presence of noise. The detection of specific QPC is crucial for neuroscience applications aimed at better understanding the temporal and spatial coordination between different brain regions. [ABSTRACT FROM AUTHOR]

The competition in the world has shifted from natural resources and capital resources to human resources. The human resources have become the real power source of the economic growth. Firstly, the price of human resources in China is calculated. Secondly, in order to measure the human resources quality adjustment index, the indicators system is constructed. Third, the Hedonic method is used to calculate the human resources "pure price" of 31 provinces (autonomous regions and municipalities directly under the Central Government) in China. The "pure price" has no the factor of human resources quality. Lastly, comparing the price of human resources before and after quality adjustment. The results show that: first, the number of human resources in China increased continuously during 1995–2015 and decreased during 2016–2020. Second, the price of nominal human resources in China keeps rising. In 2020, the nominal price is 39,087 yuan per person which is 15.44 times as many as in 1995. Thirdly, after the quality adjustment, the price of human resources has decreased significantly. The multiple between the actual and nominal price of human resources is between 1.75 and 2.12. Fourthly, the province with high human resource quality adjustment index generally have high quality human resource level or quantity. Fifth, the top five provinces of actual human resource prices are Shanghai, Beijing, Guangdong, Tianjin, Zhejiang, the bottom five provinces are Guizhou, Yunnan, Henan, Xizang, Gansu. Finally, the paper puts forward some policy recommendations: Improving the data collection mechanism of human resources accounting to provide a basic guarantee for the accurate accounting of human resources. Improving the price of human resources in the central and western regions to attract the talents to transfer to the central and western regions. Enhancing the skills training of human resources to improve the quality of human resources in the western region. [ABSTRACT FROM AUTHOR]

In this study, we aim at investigating dynamic relations among steel price indices of all seven regional markets in China, spanning the period of 2010M1–2021M4. With daily data, we apply techniques of vector error correction modeling and the directed acyclic graph to analyze the issue of contemporaneous causality among the seven indices, the latter of which is facilitated through the LiNGAM algorithm. We find via vector error correction modeling that each of the seven indices is part of cointegration relations and responses to long-run equilibrium disturbances. We arrive at causal paths via the LiNGAM algorithm for enabling innovation accounting analysis. With this analysis, we find sophisticated dynamics in price adjustment processes following shocks, for which east and northeast regions have the most significant impacts on other regions, followed by south and north regions, followed by the southwest region. Central south and northwest regions are not found to have significant impacts on others. These results suggest that policies on regional steel prices in the long run might be designed with most attention paid to five of the seven regions, particularly east and northeast regions. [ABSTRACT FROM AUTHOR]

Maize stands as a cornerstone of Indonesia's agricultural landscape, serving both as a vital food source and an essential fortifier. However, the marketing process of smallholder maize in Indonesia has yet to reach an optimal level of efficiency. This research endeavors to delve into the vertical integration of smallholder maize in the Indonesian agricultural sector. To conduct this analysis, we employ a forward-looking predictive model, applying the Vector Error Correction Model (VECM) to analyze time series data related to smallholder maize in Indonesia. Our findings yield critical insights that shed light on the intricate dynamics of smallholder maize markets in the archipelago. Notably, our research underscores the long-term integration between producer-level smallholder maize markets and consumer-level smallholder maize markets in Indonesia. This integration implies that changes in producer-level smallholder maize prices are intrinsically linked to shifts in consumer-level smallholder maize prices in the country. These findings provide a valuable foundation for collaborative efforts within the agricultural sector, guiding stakeholders toward more effective strategies for optimizing smallholder maize markets in Indonesia. [ABSTRACT FROM AUTHOR]

The power laws of network geometric properties are widely adopted to characterize the critical phenomena of complex systems, which may not describe the diverse interactions in systems effectively. By comparison, the causality represents the complicated interactions. Therefore, in this work, the power law of the causal effects in systems is studied to reflect the critical states from the perspective of causality. The causal Bayesian networks and effective information are adopted to construct the causal relationships and quantify the causal effects. The study on 20 systems from different fields shows that for a considerable proportion of systems, the causal effects of the factors in systems follow the power laws. For such causal power laws, an explanation based on the principle of maximum entropy is proposed and verified. The causal power laws may imply some critical states of systems, and can provide the basis for the quantification of systems' states and functions. [ABSTRACT FROM AUTHOR]

High level of greenhouse gases emission in fossil fuels has induced a significant transition from conventional energy sources to renewable energy. However, using renewable energy in electricity grids has limitations, such as intermittency and lack of efficient energy storage. This paper focuses on constructing the quanto options contract to help renewable energy producers hedge the risk against low photovoltaic (PV) production and electricity prices. To achieve our objective, we first model the interday PV power production with a combination of a deterministic and stochastic model. We analyse empirical data for daily solar production in three operators in Germany ranging from 1 January 2016 to 31 December 2020. We discovered that all estimated parameters of the deterministic process are highly significant. Meanwhile,we use an autoregressive (AR) process to describe the random behavior in the production. The results demonstrated that AR(2) is suitable enough to explain its stochastic factor. For the error terms analysis, we observed a clear sign of seasonal heteroskedasticity supported by the left skewed density. In addition, we observed a clear seasonal pattern in the squared error terms, where we suggest using seasonal variance and skewed normal distribution to describe its dynamic. As for an application, we embed the PV model in the power price modeling. We found that AR(3) process is sufficient to explain the price behavior and that normal distribution best fits the error terms. Using the suggested PV and power price model, we construct the quanto options contract for renewable energy producers using Monte Carlo simulations. We discovered that the electricity price payoffs are consistent throughout the year, whereas PV and quanto options payoffs vary greatly depending on the season. The quanto options price result shows that the prices vary during four seasons, with the highest variation in July. [ABSTRACT FROM AUTHOR]

In this paper we further develop a notion of causal predictability defined in [A. Merkle, Predictability and Uniqueness of Weak Solutions of Stochastic Differential Equations, Analele Stiintifice ale Universitatii Ovidius Constanta, 2022] as a concept of dependence which is based on Granger's definition of causality. More precisely, in [A. Merkle, Predictability and Uniqueness of Weak Solutions of Stochastic Differential Equations, Analele Stiintifice ale Universitatii Ovidius Constanta, 2022] causal predictability is defined between filtrations, but now we introduce causal predictability between stochastic processes and filtrations. Also, we provide some properties of this new concept. Then we apply the given causality concept to the uniqueness of weak solutions of the stochastic differential equations and in financial mathematics. Granger [Investigating causal relations by econometric models and cross spectral methods, Econometrica. 37 (1969), pp. 424–438] has considered causality concept between time series. In this paper we consider continuous time processes, since continuous time models represent the first step in various applications, such as in finance, econometric practice, neuroscience, epidemiology, climatology, demographic, etc. [ABSTRACT FROM AUTHOR]

Measurements of systems taken along a continuous functional dimension, such as time or space, are ubiquitous in many fields, from the physical and biological sciences to economics and engineering. Such measurements can be viewed as realisations of an underlying smooth process sampled over the continuum. However, traditional methods for independence testing and causal learning are not directly applicable to such data, as they do not take into account the dependence along the functional dimension. By using specifically designed kernels, we introduce statistical tests for bivariate, joint, and conditional independence for functional variables. Our method not only extends the applicability to functional data of the Hilbert–Schmidt independence criterion (hsic) and its d-variate version (d-hsic), but also allows us to introduce a test for conditional independence by defining a novel statistic for the conditional permutation test (cpt) based on the Hilbert–Schmidt conditional independence criterion (hscic), with optimised regularisation strength estimated through an evaluation rejection rate. Our empirical results of the size and power of these tests on synthetic functional data show good performance, and we then exemplify their application to several constraint- and regression-based causal structure learning problems, including both synthetic examples and real socioeconomic data. [ABSTRACT FROM AUTHOR]

The development of non-cash payments is predicted to have an effect on real money demand, not only in Indonesia. The use of electronic money in Indonesia itself continues to increase especially in the covid-19 pandemic and it supported by technological developments. The purpose of this study was to analyse the effect of non-cash payments on real money demand in Indonesia. The method used is Vector Error Correction Model (VECM) using secondary data from Januari 2019 to December 2022. The independent variable is the amount of money circulated by Bank Indonesia, and the independent variables used are the value of debit / ATM card transactions, credit cards, and e -money. The results indicate that in the long run debit cards and emoney have a significant negative effect on money supply, while credit cards have positive effect on money supply but it’s not significan. While in the short term, all variables debit cards, credit cards and e-money are not significant on money supply. [ABSTRACT FROM AUTHOR]

In many developing economies, high and increasing public debt profile constitutes an essential means of financial risk. An appropriate debt management is germane for survival of business and good international reputation though its effect on private sector credit mobilization had been seldomly investigated. This study seeks to know whether strategic debt management approach exacts a significant effect on the Nigerian private sector and Africa at large resulting to higher credit availability for sustainable enterprise establishment. The study used a time-series observation spanning from 1981–2021. The method of data analysis employed the unit root test for stationarity. Johansen cointegration and vector error correction approach. The result of the unit root test indicates the series were all stationary after first difference and thus were integrated of order1. The Johansen cointegration test support the existence of a cointegrating series between the private credit and its determinants. More empirical evidence from the study shows that proper debt management and increase revenue generation through net taxes on products accounted for 0.93 and 1.32% increase in private sector credit mobilization, while total external debt stock was responsible for a significant negative influence of 0.60% on private sector credit mobilization. The study recommends that the government should always be proactive in their strategic and innovative approach to debt management, revenue generation and sources of funds. This will help not only to avoid crowding out of the private sector but will enhance adequate credit mobilization for effective operations of the private sector. [ABSTRACT FROM AUTHOR]

Accurate inventories of grasslands are important for studies of greenhouse gas (GHG) dynamics, as grasslands store about one-third of the global terrestrial carbon stocks. This paper develops a framework for large-area grassland mapping based on the probability of grassland occurrence and the interactive pathways of fractional vegetation and soil-related endmember nexuses. In this study, grassland occurrence probability maps were produced based on data on bio-climate factors obtained from MODIS/Terra Land Surface Temperature (MOD11A2), MODIS/Terra Vegetation Indices (MOD13A3), and Tropical Rainfall Measuring Mission (TRMM 3B43) using the random forests (RF) method. Time series of 8-day fractional vegetation-related endmembers (green vegetation, non-photosynthetic vegetation, sand land, saline land, and dark surfaces) were generated using linear spectral mixture analysis (LSMA) based on MODIS/Terra Surface Reflectance data (MOD09A1). Time-series endmember fraction maps and grassland occurrence probabilities were employed to map grassland distribution using an RF model. This approach improved the accuracy by 5% compared to using endmember fractions alone. Additionally, based on the grassland occurrence probability maps, we identified extensive ecologically sensitive regions, encompassing 1.54 (104 km2) of desert-to-steppe (D-S) and 2.34 (104 km2) of steppe-to-meadow (S-M) transition regions. Among these, the D-S area is located near the threshold of 310 mm/yr in precipitation, an annual temperature of 10.16 °C, and a surface comprehensive drought index (TVPDI) of 0.59. The S-M area is situated close to the line of 437 mm/yr in precipitation, an annual temperature of 5.49 °C, and a TVPDI of 0.83. [ABSTRACT FROM AUTHOR]

In this paper we propose an new approach to multivariate collective models based on asymptotic distibutions, since the modeling problems posed have large samples. Collective risk models play an important part in Risk Theory and in Actuarial Mathematics. Inference based on these models is centered on claims totals. However, the new approach has in mind a different kind of risk, the risk of forest fires, where the variables of interest are the number fires and the total burnt areas. As a result, a special case bivariate risk model is derived with this intent. Besides single models, structured families whose models correspond to the treatments of a base linear model are considered. This leads to an ANOVA-like situation where we don't have to estimate the error, which enable to test the influence of several factors on the multivariate collective models mean vectors. Instead of F tests, we use χ 2 tests, availing us of asymptotic distributions that lightens the treatment. To illustrate the approach, an application to forest fires in Portugal with real data is presented. [ABSTRACT FROM AUTHOR]

The fundamental stages in designing a stock portfolio are each stock's selection and capital weighting. Selection and weighting must be conducted through diversification and price movement analysis to maximize profits and minimize losses. The problem is how the technical implementations of both are carried out. Based on this problem, this study aims to design these selection and weighting mechanisms. Stock selection is based on clusters and price movement trends. The optimal stock clusters are formed using the K-Means algorithm, and price movement analyses are carried out using the moving average indicator. The selected stocks are those whose prices have increasing trends with the most significant Sharpe ratio in each cluster. Then, the capital weighting for each preferred stock is carried out using the mean-variance model with transaction cost and income tax. After designing the mechanism, it is applied to Indonesia's 80 index stock data. In addition, a comparison is conducted between the estimated portfolio return and the actual one day ahead. Finally, the sensitivity of investors' courage in taking risks to their profits and losses is also analyzed. This research is expected to assist investors in diversification and price movement analysis of the stocks in the portfolios they form. [ABSTRACT FROM AUTHOR]

According to energy economists, the energy factor underlies economic growth and development. In this approach, energy is accepted as an indispensable production factor in the production of all goods and services. In addition, it is known that agricultural activities are at the forefront at the beginning of development, and a structural transformation has taken place from agriculture to industry and from the industrial sector to the service sector. However, due to the developments in the world economy in recent years and the fact that the agricultural sector plays a key role in the country, many countries attach special importance to the development of this sector. The development of the agricultural sector brings the need to increase the amount of energy used by this sector. The increase in energy costs day by day puts the agriculture sector in a difficult situation, and how the increasing energy consumption will affect the economic growth of the country emerges as a research topic. The literature investigating the relationship between energy consumption and economic growth rarely addresses this issue on a sectoral basis. In this context; It is important whether energy consumption in the agricultural sector can affect economic growth. This study aims to investigate the relationship between agricultural energy consumption and economic growth for the Turkish economy in the period of 1990-2018. The results of the ADF and PP unit root tests used for the stationarity analysis of the variables reveal that the variables become stationary at the first difference. The long-term relationship between the variables is investigated using the Hatemi J-Irandoust hidden cointegration test, which is one of the new generation tests. The coefficients of the variables and the causality relationship are examined with the FMOLS technique and the hidden error correction model, respectively. The findings show the existence of a cointegration between the + component of agricultural energy consumption and the + component of economic growth. The findings also indicate the existence of a positive relationship between the + component of agricultural energy consumption and the + component of economic growth. Finally, there is a unidirectional causality running from the + component of economic growth to + component of agricultural energy consumption. These results show that agricultural energy consumption has an impact on Turkey's economic growth. The study can offer some policy recommendations related to the agricultural sector for the growth of the Turkish economy. [ABSTRACT FROM AUTHOR]

This study examines the nexus between disaggregated energy consumption (EC), CO2 emissions, and economic growth in emerging South and East Asian countries over the period of 1994 to 2019. The long‐run equilibrium relationship is determined by using the "Autoregressive distributed lag (ARDL) model" and the "Generalized least square (GLS) technique." The panel causality test developed by "Dumitrescu and Hurlin, 2012" determines the direction of causation between variables. Disaggregated EC and CO2 emissions positively affect economic growth in the research. The PMG estimate also validates the GLS findings, which produce the same results as the PMG estimation. To check the robustness, we also use FMOLS and DOLS estimators. The results confirm the feedback hypothesis for South and East Asian nations regarding energy uses, CO2 emissions, and economic growth. In contrast, there is unidirectional causality between industrial development and economic growth. These findings will help governments in South and East Asia craft effective energy policy regulations for their financial institutions. [ABSTRACT FROM AUTHOR]

In order to improve the market competitiveness of suppliers and their resilience to emergencies, it is of great significance to discuss the investment decision making of suppliers in developing new products under uncertain and competitive environments. In this paper, with the background of knowledge spillover, absorptive capacity, initial R&D investment, and innovation efficiency asymmetry, the uncertainty of price, sales, and cost are incorporated into the evaluation system as three important risk factors. On the basis of the existing real option investment evaluation model, a real option game model of R&D investment of supplier enterprises based on multiple random variables is established. The sensitivity analysis of parameters is carried out with an example of one enterprise's monitor R&D project. The results depict that the probability of R&D success has a great impact on the value of enterprise options, which depends on the R&D investment, innovation efficiency, and R&D performance of enterprises. Secondly, the drift rate of price, sales, and cost also has a significant impact on improvement in enterprise option value. [ABSTRACT FROM AUTHOR]

Inferring causal relationships from observational data is a key challenge in understanding the interpretability of Machine Learning models. Given the ever-increasing amount of observational data available in many areas, Machine Learning algorithms used for forecasting have become more complex, leading to a less understandable path of how a decision is made by the model. To address this issue, we propose leveraging ensemble models, e.g., Random Forest, to assess which input features the trained model prioritizes when making a forecast and, in this way, establish causal relationships between the variables. The advantage of these algorithms lies in their ability to provide feature importance, which allows us to build the causal network. We present our methodology to estimate causality in time series from oil field production. As it is difficult to extract causal relations from a real field, we also included a synthetic oil production dataset and a weather dataset, which is also synthetic, to provide the ground truth. We aim to perform causal discovery, i.e., establish the existing connections between the variables in each dataset. Through an iterative process of improving the forecasting of a target's value, we evaluate whether the forecasting improves by adding information from a new potential driver; if so, we state that the driver causally affects the target. On the oil field-related datasets, our causal analysis results agree with the interwell connections already confirmed by tracer information; whenever the tracer data are available, we used it as our ground truth. This consistency between both estimated and confirmed connections provides us the confidence about the effectiveness of our proposed methodology. To our knowledge, this is the first time causal analysis using solely production data is employed to discover interwell connections in an oil field dataset. [ABSTRACT FROM AUTHOR]

The Ethiopian Investment Commission was established to formulate suitable investment policy with good investment climate that enables investors to operate in a more macroeconomic friendly environment. Despite all efforts, domestic private investment in Ethiopia is still in its early stages. Therefore, this study aimed to investigate the trends and determinants of domestic private investment in Ethiopia using a time series data over the period 1992–2022. To meet its goal, the study employed an Autoregressive Distributed Lag (ARDL) bounds testing approach through E-views version 12. The findings show that domestic private investment was negatively and significantly affected by the inflation rate, public investment, and real effective exchange rate over a period of both the short- and long-run. While domestic credit to the private sector, foreign direct investment, real GDP and trade openness were found to have positive and significant effect on domestic private investment in long run. Unemployment rate was found to have positive and significant effect in short run but insignificant in long run. Annual interest rate was found negative significant effect in short run but insignificant in long run. While total government expenditure was insignificant in both short and long run. Inflation has a negative relation with domestic private investment in both short and long run, therefore the study suggested that policymakers should recognize the cause for fluctuations in inflation and keep in a stable manner. [ABSTRACT FROM AUTHOR]

Malaysia's economy suffers from high public expenditures, especially current expenditures, which has led to a high percentage of the budget deficit. The research aims to identify the macroeconomic variables affecting the budget deficit in Malaysia and to identify the existence of a long-term relationship and a causality relationship between the budget deficit in Malaysia as a dependent variable, and the independent variables explaining it. The research relied on descriptive and quantitative analysis methods, as based on a variety of statistical and econometrics methods, including the use of the growth function, the unit root test (ADF), cointegration analysis and the Granger causality test to ensure the existence of causality between variables and determine the direction of that relationship. The research was based on annual time series data covering the period 1990-2019. The study model included: the budget deficit variable in Malaysia (% of GDP) as a dependent variable, while the independent variables are: government spending, fixed capital formation, public debt, foreign direct investment, exchange rate, average interest rate, and economic growth rate. The stability of the variables was tested using the ADF test in order to determine the degree of stability of the variables. The results showed that the time series under study are not stable at the level, but they are all stable at their first differences I(1). The results of the multiple regression model showed that the signs of the coefficients of the independent variables are consistent with economic logic, and that together they explain about 87.1 percent of the changes in the budget deficit in Malaysia during the study period. The regression coefficients for the variables of government expenditure, fixed capital formation, and foreign direct investment were positive to express the existence of a direct relationship between these independent variables and the budget deficit in Malaysia, as a dependent variable, while it is noted that the regression coefficient for the variables of public debt and the exchange rate The average interest rate and economic growth rate were negative, which is consistent with economic theory. It also turns out that the most important relative variable in influencing the dependent variable in the previous estimated model is the public debt variable, while the average interest rate variable comes in last. The results of cointegration indicated the presence of three vectors of cointegration between these variables, which confirms the existence of a long-term equilibrium relationship between the public budget deficit in Malaysia and the independent variables affecting it. The results of the Granger causality test also indicated the existence of a unidirectional causal relationship heading from government spending to the budget deficit, meaning that increasing government spending causes a budget deficit, and the existence of a unidirectional causal relationship that goes from foreign direct investment to the budget deficit, from the average interest rate to the budget deficit, and from the budget deficit to the exchange rate, and it also shows the existence of a causal relationship. There is a bidirectional relationship between the size of the public debt and the public budget deficit, and between the economic growth rate and the budget deficit in Malaysia. [ABSTRACT FROM AUTHOR]

In our modern world, energy keeps the global economy running, and economic growth concerns are profoundly interrelated with environmental quality issues. Interestingly, scientists engage with empirical research to identify the impacts and causalities at the interface of economic activities, energy supply, and demand. The importance of the present study lies in a discussion of all contemporary research efforts bridging two strands of empirical literature in environmental economics: developments in energy growth nexus discussion and the environmental Kuznets curve. Furthermore, it highlights the inclusion of untested explanatory variables and the impacts on environmental degradation levels. In the context of the EKC hypothesis, the most popular indicators are greenhouse gas emissions (GHGs) and carbon dioxide emissions in conceptualizing environmental degradation. A review of relevant empirical studies disclosed additional research opportunities that can consider currently untested and less visible proxies of economic growth. For both strands in the literature, results differ based on the group of countries investigated, the econometric models adopted, the format of data, e.g., time series or panel analyses, the time frames due to data availability, and the proxies used to conceptualize energy, environmental degradation, and economic growth. Practical implications indicate that environmental degradation can be avoided or significantly limited within sustainable economic growth to reduce carbon dioxide emissions and increase the use of renewables in the energy mix. Furthermore, one particular implication is the concept of energy efficiency to reduce relevant demand to produce the same outcome or task. [ABSTRACT FROM AUTHOR]

Granger causality (GC) is a popular method in causal linkage recovery and has been applied to various fields, such as economics and neuroscience. While the conventional Granger causality model is capable of identifying symmetrical causal relationships among variables, it is the asymmetric Granger causality that provides a more comprehensive perspective of the short- and long-term interactions between variables, which is of greater value for empirical study. Traditional vector autoregressive models lack the ability to explore multiscale information flow and are affected by the moving average component. Therefore, by combining the wavelet-based approach and state space model, we propose a new Granger causality analysis method to overcome the inherent limitation of vector autoregressive models and extend to multiscale causality exploration. Two simulations were conducted to compare the proposed approach to an existing wavelet-based method, and five evaluation indicators were utilized. The results indicate that the proposed method efficiently identifies the accurate asymmetric causalities at varying scales, while improving accuracy and reducing bias as compared to the current wavelet-based method. In conclusion, the combination of the wavelet approach and state space method enhances the multiscale causality detecting capability and can potentially contribute to multiscale Granger causality research. [ABSTRACT FROM AUTHOR]

For the case with a single causal variable, Dawid et al. (2014) defined the probability of causation, and Pearl (2000) defined the probability of necessity to assess the causes of effects. For a case with multiple causes that could affect each other, this paper defines the posterior total and direct causal effects based on the evidence observed for post-treatment variables, which could be viewed as measurements of causes of effects. Posterior causal effects involve the probabilities of counterfactual variables. Thus, as with the probability of causation, the probability of necessity and direct causal effects, the identifiability of posterior total and direct causal effects requires more assumptions than the identifiability of traditional causal effects conditional on pre-treatment variables. We present assumptions required for the identifiability of posterior causal effects and provide identification equations. Further, when the causal relationships between multiple causes and an endpoint can be depicted by causal networks, we can simplify both the required assumptions and the identification equations of the posterior total and direct causal effects. Finally, using numerical examples, we compare the posterior total and direct causal effects with other measures for evaluating the causes of effects and the population attributable risks. [ABSTRACT FROM AUTHOR]

The article concerns the verification of links between the energy intensity of public urban transport, regional economic development and urbanisation in the light of sustainable development challenges and club convergence processes. Three research questions were formulated in the article: (1) Does the regional economic development of voivodeships affect the energy intensity of public urban transport? (2) Does urbanisation shape energy consumption patterns in public urban transport (and are there agglomeration effects)? (3) Does the level of urbanisation of voivodeships depend on their regional level? The study covered 16 subregions of Poland from 2010 to 2020. A spatial dynamic exploration of the relationships between energy intensity patterns of public urban transport and the phenomenon of urbanisation and regional economic development was carried out. Panel models of vector autoregression and panel causality testing were used. The influence of the economic development of regions and the level of urbanisation on energy intensity patterns of public urban transport was confirmed. The link between economic development and the process of urbanisation was identified. An additional effect was noticed (novelty knowledge), i.e., there is a delayed club convergence between voivodeships in the area of energy intensity of public urban transport, regional economic development and urbanisation (polyconvergence). [ABSTRACT FROM AUTHOR]

This study corroborates the importance of United Nations Sustainable Development Goal 7 (SDG‐7), intended to ensure access to affordable, reliable, sustainable energy for all, and SDG‐8, designed to promote decent work and sustainable economic growth. This article is motivated by the highlighted SDGs and empirically explores the long‐run and causality relationship between energy consumption, urbanization, trade openness, and economic growth for annual frequency data from 1965 to 2021 for the case of Mexico. To this end, we leverage the use of fully modified ordinary least squares, dynamic ordinary least squares, and canonical regression estimation methods, while for the direction of causality, the gradual shift and wavelet coherence methods are used. According to the Autoregressive distributed lag (ARDL), the bounds test traces a long‐run relationship between the outlined variables over the sampled period. Empirical evidence validates the energy‐induced growth hypothesis. This result resonates with the causality analysis, where energy consumption drives economic growth one way in Mexico. This suggests that Mexico cannot embark on energy‐conservative policies, as such actions will hurt economic progress. In addition, unidirectional causality is seen between urbanization, trade openness, and economic growth. These findings have far‐reaching implications for economic growth and macroeconomic indicators in Mexico. More insights are highlighted in the concluding section. [ABSTRACT FROM AUTHOR]

Causal inference is a fundamental research topic for discovering the cause–effect relationships in many disciplines. Inferring causality means identifying asymmetric relations between two variables. In real-world systems, e.g., finance, healthcare, and industrial processes, time series data from sensors and other data sources offer an especially good basis to infer causal relationships. Therefore, many different time series causal inference algorithms have been proposed in recent years. However, not all algorithms are equally well-suited for a given dataset. For instance, some approaches may only be able to identify linear relationships, while others are applicable for non-linearities. Algorithms further vary in their sensitivity to noise and their ability to infer causal information from coupled vs. non-coupled time series. As a consequence, different algorithms often generate different causal relationships for the same input. In order to achieve a more robust causal inference result, this publication proposes a novel data-driven two-phase multi-split causal ensemble model to combine the strengths of different causality base algorithms. In comparison to existing approaches, the proposed ensemble method reduces the influence of noise through a data partitioning scheme in a first phase. To achieve this, the data are initially divided into several partitions and the base causal inference algorithms are applied to each partition. Subsequently, Gaussian mixture models are used to identify the causal relationships derived from the different partitions that are likely to be valid. In the second phase, the identified relationships from each base algorithm are then merged based on three combination rules. The proposed ensemble approach is evaluated using multiple metrics, among them a newly developed evaluation index for causal ensemble approaches. We perform experiments using three synthetic datasets with different volumes and complexity, which have been specifically designed to test causality detection methods under different circumstances while knowing the ground truth causal relationships. In these experiments, our causality ensemble outperforms each of its base algorithms. In practical applications, the use of the proposed method could hence lead to more robust and reliable causality results. [ABSTRACT FROM AUTHOR]

Using World Bank and IMF data over the period 1990–2018, this research explores the determinants of economic growth in Bangladesh with particular attention to environmental degradation and export concentration variables. An ARDL (Autoregressive Distributed Lag) bound testing approach is employed as an estimation strategy with FMOLS (Fully Modified Ordinary Least Squares) and CCR (Canonical Cointegrating Regression) for cross-checking the results. The obtained results confirm that CO2 emissions, consumption expenditure, export concentration, remittances, and inflation are the main driving forces for the long-run economic growth in Bangladesh where the effects of the first two variables and the last three variables are positive and negative, respectively. The study also reveals the dynamic short-run relationships among the chosen variables. Environmental pollution and export concentration are found to be the barriers to economic growth; therefore, the country should take necessary steps to mitigate this problem for ensuring long-run sustainable economic development. [ABSTRACT FROM AUTHOR]

The elderly population and economic growth have been a contentious topic among researchers. Regardless of the economic growth rate, the population and its growth have a stimulating influence on economic development. This study aims to explore the relationship between the elderly population and economic growth in 15 Asian countries, based on secondary data gathered from the WDI (World Development Indicators) from 1961 to 2021. This research contributes to filling the empirical gap, capturing the Granger causality concerning the relationship between the elderly population and economic growth in the Asian context in a single study. The empirical findings highlighted a one-way Granger causality from economic growth to the elderly population for India, Japan, Malaysia, and Singapore while vice versa for Bangladesh, China, and Pakistan. Furthermore, for Nepal, there is a two-way Granger causality, while there is no Granger causality for remaining countries. To the best of the authors' knowledge, this study has been the first to investigate the relationship between the elderly population and economic growth for Asian nations, using a lengthy data series and a Granger causality test. The main findings will assist the governments, policymakers, and foreign investors in effective decision-making in this regard. [ABSTRACT FROM AUTHOR]

The study investigates key drivers of renewable energy transition in China using the quarterly data from Q1-2000 to Q4-2020. We employed the Bootstrap Autoregressive Distributed Lag method for long-run and short-run parameters. The results exhibit that financial development and research and development expenditures are the most prominent factors that encourage energy transition. In addition, human resources and information and communication technology (ICT) also contribute to stimulating the renewable energy transition in China. The overall long-run estimates have pronounced impact than the short-run results, except for the ICT, which has an insignificant effect in the short run. Moreover, the significant negative value of the error correction term converges the model by a 32.9% adjustment rate in case of any deviation. The empirical outcome recommends that the government of China should promote financial development resources and R&D spending to spur the energy transition. • Bootstrap ARDL applies to China's quarterly data. • Drivers of Renewable Energy Transition (RET) are explored. • Financial development and R&D expenditures are the most prominent drivers of RET. • Human development and ICT stimulate RET. [ABSTRACT FROM AUTHOR]

This study examines the dynamic connection between green energy, employment, fossil fuel energy, and human development index including additional variables such as education, life expectancy, and poverty in panel data of 30 developing countries from 1990–2017. Previous studies analyzed the total energy consumption on the human development index, whereas the differential effect of green and fossil fuel energy with employment and human development index has not been examined. The empirical exercise is based on the panel co-integration test, panel fully modified ordinary least squares, dynamic ordinary least squares, and vector error correction estimation approaches. The results reveal that all variables are co-integrated. The results of regression analysis indicate that green energy, education, life expectancy, and employment increase the human development index, but fossil fuel energy and poverty decrease the human development index by −0.016 and 0.023%. In addition, the vector error correction model designates that there is bidirectional causality between green energy consumption and the human development index. Therefore, for developing countries, the development and utilization of green energy sources (wind, solar, geothermal, etc.) are needed. Moreover, these countries should rely less on fossil fuel energy because it causes a decrease in the HDI. [ABSTRACT FROM AUTHOR]

The research on risk control during the construction stage of catenary is relatively limited. Based on a comprehensive analysis of the risk factors during catenary construction, this study determined the causal relationships between the risk factors and established a risk assessment model for catenary construction that analyzed the risks from a causal logic perspective. During the evaluation process, we identified six exogenous variables and twenty-one endogenous variables for risk factors in the construction of catenary based on a literature review in the field of catenary construction and expert opinions, described the cause-and-effect relationships between variables using structural equations and causal diagrams, and established a multi-level catenary construction risks structural causal model. Based on expert fuzzy evaluation and expert experience, the occurrence probability of exogenous variables and the conditional probability of endogenous variables were determined, respectively. Then, the risk assessment model of catenary construction stage based on fuzzy Bayesian Network was constructed to analyze the risk of catenary construction process. The results showed that the personal quality of the construction personnel and the sense of responsibility of the supervision unit had a great impact on the risk level of catenary construction. The findings can help construction personnel fully consider various weak points in catenary construction, thereby ensuring efficient and high-quality catenary construction. [ABSTRACT FROM AUTHOR]

Population growth and more recently, the Covid-19 pandemic, have increased the number of severely food insecure people in sub-Saharan Africa. Food security is one of the most prominent tasks facing the global environment especially in Asia and Africa. With the increase in the population, drought, floods, disease outbreaks, poor farm network systems, water poverty, weak political and economic institutions, selfish leadership and economic challenges in Africa, the food security crisis appears to be an important issue that will require strategic and urgent attention. There is no doubt that traditional ways of farming are not sustainable in Africa because the land available for agriculture reduces as the population increases over time. Investment in new technologies is one of the strategies to improve farm yields and livestock outputs, and reduces the risk and negative effects of weather. The role of agripreneurship, in this context, towards ensuring food security cannot be underestimated. This study is quantitative research which analyses the historical and current time series data in order to predict the future event using inferential statistics. This study investigates the relationship between food security (FST), agricultural research and technology innovation (RTI) and economic growth (GDP) in Nigeria between 1980 and 2018. The study utilizes cointegration and causality tests to determine the long run relationship among the variables and their causal directions so as to know which variable caused the others. Understanding this relationship among the variables and causal direction are vital to making appropriate suggestions for policy makers. The results of the Johansen cointegration test reveals an existence of a long run relationship between the three series (FST, RTI, GDP). The results of vector error correction model indicate a short run causality from GDP to FST and RTI, respectively. Furthermore, the results of long run causality show two-way causality between FST and RTI, one-way causality from GDP to FST and RTI without feedback. This implies that when the economic activities improve in Nigeria in the short run, this engenders agricultural technology innovation deployment in the country which facilitates food security in the long run, while food security also facilitates agricultural technology innovation. The two-way causality between RTI and FST established in this study implies that increase in agricultural research investment would improve food security and vice versa. Thus, government and other private players such as agripreneurs in agricultural sector should intensify their spending in agricultural research on the one hand, and government should also provide appropriate incentives to motivate private stakeholders for agricultural R&D investment on the other hand. [ABSTRACT FROM AUTHOR]

Universal Causality is a mathematical framework based on higher-order category theory, which generalizes previous approaches based on directed graphs and regular categories. We present a hierarchical framework called UCLA (Universal Causality Layered Architecture), where at the top-most level, causal interventions are modeled as a higher-order category over simplicial sets and objects. Simplicial sets are contravariant functors from the category of ordinal numbers Δ into sets, and whose morphisms are order-preserving injections and surjections over finite ordered sets. Non-random interventions on causal structures are modeled as face operators that map n-simplices into lower-level simplices. At the second layer, causal models are defined as a category, for example defining the schema of a relational causal model or a symmetric monoidal category representation of DAG models. The third layer corresponds to the data layer in causal inference, where each causal object is mapped functorially into a set of instances using the category of sets and functions between sets. The fourth homotopy layer defines ways of abstractly characterizing causal models in terms of homotopy colimits, defined in terms of the nerve of a category, a functor that converts a causal (category) model into a simplicial object. Each functor between layers is characterized by a universal arrow, which define universal elements and representations through the Yoneda Lemma, and induces a Grothendieck category of elements that enables combining formal causal models with data instances, and is related to the notion of ground graphs in relational causal models. Causal inference between layers is defined as a lifting problem, a commutative diagram whose objects are categories, and whose morphisms are functors that are characterized as different types of fibrations. We illustrate UCLA using a variety of representations, including causal relational models, symmetric monoidal categorical variants of DAG models, and non-graphical representations, such as integer-valued multisets and separoids, and measure-theoretic and topological models. [ABSTRACT FROM AUTHOR]

Since 2015, the United Kingdom (UK) has increased its investment in transportation infrastructure to compensate for years of underinvestment in comparison with peer countries. It is of critical importance to evaluate the effect of these recent investments, as well as historical ones, on economic development, both theoretically and empirically, to provide guidance on future infrastructure investment. However, few research looks into such policy adjustment on economic growth in the UK. To fill this gap, this paper investigates the relationship between transport infrastructure development and economic growth in the UK from different time spans. Principal component analysis (PCA) is used to construct a comprehensive measure of transport infrastructure development. This paper then applies Vector Error Correction Model (VECM) to investigate both long-run and short-run relationships between transport infrastructure development and economic growth from 1970 to 2017 in the UK. Empirical results suggest that transportation infrastructure has a long-run promotive effect on economic development. However, in the short run, this effect turns out to be significantly negative. The analysis of this paper indicates differentiated roles that the UK's transport infrastructure played in economic growth, which should be considered in future policy design of achieving economic sustainability in the UK. • This paper investigates the relationship between transport infrastructure development and economic growth in the UK. • Transportation infrastructure has a long-run promotive effect on economic development. • In the short run, transportation infrastructure fails to promote economic development. [ABSTRACT FROM AUTHOR]

The development of a country's economy is directly related to the use of energy in that country's economic sectors. Therefore, the energy–environmental Kuznets curve (EEKC) is often used when analysing a country's potential and challenges in sustainable development, green economy, and green growth. This hypothesis tests whether there is an inverse "U"-shaped relationship between energy use and economic growth and is especially important when analysing developing countries to assess if, at a certain point, energy use begins to drop, resulting in fewer greenhouse gas emissions, environmental degradation, and the consumption of fossil-based fuels. This study aims to examine the relationship between energy consumption and economic growth in the Baltic States from 1995 to 2019, with a focus on the agriculture sector. The study uses the non-linear autoregressive distributed lag (NARDL) model for individual and panel time series. Total energy use, as well as electricity use, is included in the study, whereas gross value added is employed as a measure of economic growth. Research data analysis reveals that energy use in all three Baltic countries stabilises as gross value added increases. However, there is insufficient evidence to show that after a certain point, energy use begins to drop; thus, the hypothesis for the inverse "U"-shaped energy–environmental Kuznets curve (EEKC) is rejected. Research results have important practical implications regarding countries' policies toward energy, including the use of electricity and sustainable development. [ABSTRACT FROM AUTHOR]