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Purpose: The impact of financial inclusion on economic growth is a topic that is generating widespread interest among researchers and practitioners. In this paper, the authors review the existing literature to highlight the state of research in the literature and identify new opportunities for innovative research. Design/methodology/approach: The authors used a thematic literature review methodology which involves dividing the review along relevant themes. Findings: The authors find that significant research on the topic emerged in the post-2016 years. Most of the existing studies are from developing countries and from the Asian and African regions. Existing studies have not utilized relevant theories in explaining the impact of financial inclusion on economic growth. Most studies report a positive impact of financial inclusion on economic growth while very few studies show a negative impact. The most common channel through which financial inclusion affects economic growth is through greater access to financial products and services offered by financial institutions that increases financial intermediation and translates to positive economic growth. The common empirical methodology used in the literature are causality tests, cointegration and regression methods. Multiple proxies of financial inclusion and economic growth were used in the literature which partly explains the conflicting result among existing studies. The review paper concludes by identifying some directions for future research. Originality/value: This paper presents the first rigorous thematic review of the existing literature on the impact of financial inclusion on economic growth. It highlights the main approach that researchers have taken on this topic and identifies some important research areas for future investigation. Peer review: The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-05-2022-0339. [ABSTRACT FROM AUTHOR]

China, situated in the circum-Pacific seismic belt, experiences frequent seismic activity and faces diverse geological conditions, making structural stability of paramount importance, especially under seismic conditions. The majority of current earthquake generation methods do not consider the nonstationary nature of earthquakes. This paper introduces a spectral representation-random function model for generating nonstationary earthquakes, effectively simulating stochastic seismic ground motion. Furthermore, traditional slope stability analysis methods are deterministic and incapable of providing probabilistic assessments of slope instability. Therefore, this paper proposes a unified framework for static and dynamic structural reliability analysis based on the direct probability integration method, quantifying the impact of stochastic seismic ground motion on the dynamic reliability of slope stability. Finally, the proposed methods are applied to an excavation slope in Nanjing, using sliding displacement and safety factors as evaluation criteria to study the reliability of the slope under the influence of stochastic seismic events. [ABSTRACT FROM AUTHOR]

This paper aims to assess the impact of agricultural investment on agricultural output in the Democratic Republic of Congo using secondary data for the period 1980 to 2019. Using the Johansen and Jusseleus cointegration test, agricultural investment and agricultural output were observed to have a statistically significant long-run relationship. Additionally, the error correction model (ECM) shows that the short-run coefficient of agricultural investment has a statistically significant positive impact on agricultural output, implying that investment (machine, infrastructure etc.) does spur agricultural output in the Democratic Republic of Congo. The results show the presence of unidirectional causality between agricultural output toward agricultural investment. It was also observed that unidirectional causality from labour force to agricultural investment, labour force to education and agricultural investment to land use and a bi-directional causality between land use and labour force exists. The paper concludes that an increase in investment in the agricultural sector will result in agricultural output in the Democratic Republic of Congo. [ABSTRACT FROM AUTHOR]

The proposed mathematical methodology is based on the theory of first-passage time probabilities, studying the market model by means of a jump-telegraph process. The paper entitled I Nonparametric Estimation of Range Value at Risk i by Suparna Biswas and Rituparna Sen [[6]] focuses on the range value at risk, a quantile-based risk measure that includes, as special cases, the value at risk and the expected shortfall. The results were tested by assessing the ability of the obtained estimates for asset volatility and market leverage to predict the one-period ahead CDS spreads and show a strong improvement in reducing pricing errors. [Extracted from the article]

The paper focuses on investigating the correlation and volatility of fuel markets in four countries of the Visegrad region, namely Hungary, the Czech Republic, Poland, and Slovakia. The primary objective of the paper is to explore regional fuel markets and retail prices in these countries by employing VAR models and Johansen procedures to analyze the interrelationships between Visegrad fuel markets. Additionally, the paper uses multivariate dynamic conditional GARCH (DCC GARCH-M) models to examine the volatility and covariance of fuel prices in these nations. The results of the study indicate that there are no long-term connections between Visegrad gasoline prices. The performance of the domestic price is independent of other markets. The research also shows long-term relations of diesel prices only among some countries. Overall, the size of these relations is small and mostly statistically insignificant. These findings provide valuable insights into the fuel markets of the Visegrad countries and can be useful in formulating policies related to energy and fuel price regulation. Overall, the study contributes to the literature on fuel markets in the Visegrad region and provides policymakers and stakeholders with essential information necessary for making informed decisions. [ABSTRACT FROM AUTHOR]

Collaborative research is increasing in terms of publications, skills, and formal interactions, which certainly makes it the hotspot in both academia and the industrial sector. Knowing the factors and behavior of dynamic collaboration network provides insights that helps in improving the researcher’s profile and coordinator’s productivity of research. Despite rapid developments in the research collaboration process with various outcomes, its validity is still difficult to address. Existing approaches have used bibliometric network analysis with different aspects to understand collaboration patterns that measure the quality of their corresponding relationships. At this point in time, we would like to investigate an efficient method to outline the credibility of findings in publication—author relations. In this research, we propose a new collaboration method to analyze the structure of research articles using four types of graphs for discerning authors’ influence. We apply different combinations of network relationships and bibliometric analysis on the G-index parameter to disclose their interrelated differences. Our model is designed to find the dynamic indicators of co-authored collaboration with an influence on the author’s behavior in terms of change in research area/interest. In the research we investigate the dynamic relations in an academic field using metadata of openly available articles and collaborating international authors in interrelated areas/domains. Based on filtered evidence of relationship networks and their statistical results, the research shows an increment in productivity and better influence over time. [ABSTRACT FROM AUTHOR]

Discrepancies between several South-Eastern European (SEE) countries and Nordic countries are investigated in this paper using an econometric analysis. Its aim is to examine the relationship between CO2 emissions, GDP per capita, urban population (URB) and electricity production from Renewable Energy Sources (RES) - EPREN, excluding hydroelectric for the two groups of EU countries located in the North and S-E of Europe. The data covers a period from 1990 to 2022, providing a comprehensive view over three decades. The relationship between the four variables is determined by various causality and cointegration tests. We check the unit root tests and conclude that the analyzed time series are stationary at first difference. Further, we estimate two models: Fully Modified and Dynamic Ordinary Least Squares and study causality and cointegration between variables. The results show that CO2 emissions are impacted by GDP, URB and EPREN for both regions. Testing causality, for SEE and Nordic countries, the bidirectional and causalities do exist. [ABSTRACT FROM AUTHOR]

Power systems with a high proportion of inverter-based sources like photovoltaics require a substantial short-circuit current ratio to ensure strong voltage support capabilities. However, this also increases the system's short-circuit current capacity and levels, which may potentially affect the safe operation of system equipment and current-carrying conductors. To evaluate the operational risks, this paper proposes a quantitative calculation model for the critical integration proportion of grid-connected inverter-interfaced power sources based on short-circuit current over-limit probability. Firstly, according to the verification criterion about short-circuit current during the selection of the switching equipment and conductors in the power system, the short-circuit current over-limit probability evaluation system with five indices is established. Secondly, considering the impact of the increased grid integration proportion of inverter-based power sources on short-circuit currents, an evaluation process for operational risk probabilities is proposed. Based on this, the critical access proportion for inverter-based power source integration in the power grid can be calculated. Finally, the proposed model is used to assess the operational risk probability for a 220 kV system which includes a high proportion of inverter-based power sources in the Anhui area, China. Through the analysis of cumulative probability distribution curves, the critical grid integration proportion interval for the transition from normal to high proportion stages of inverter-based power sources is determined. [ABSTRACT FROM AUTHOR]

This paper investigates the long-run and short-run relationship between money supply and inflation in Pakistan, utilizing annual data spanning from 1981 to 2021. The key objective is to assess the impact of monetary policy, specifically money supply, on inflation dynamics in the country. To achieve this, the Autoregressive Distributed Lag (ARDL) bounds testing approach is employed, which is suitable for analyzing cointegration among variables with mixed integration orders. The results reveal both short and long-run cointegration between inflation, money supply, unemployment, and interest rates. Notably, unemployment demonstrates a negative correlation with inflation, while money supply and interest rates exhibit a positive relationship. These findings underscore the importance of dedicated policy measures to manage inflation effectively. The paper concludes by recommending the establishment of a policy implementation body and collaboration between the government and the central bank to ensure financial stability and control inflation through well-calibrated monetary and fiscal policies. [ABSTRACT FROM AUTHOR]

This paper contributes to the social science literature when analyzing survey or time series data social scientists use spurious regression without due consideration of its assumptions and the data structure. This results in misinterpretation and misleading conclusions about the population. The paper reviews basic statistical and econometrics literature which led to the development of modern time series analysis in the presence of spurious regression. It concludes that the term 'Spurious' was well known before the Granger and Yule's work in time series context rather than cross-sectional data. The same reasons can produce spurious regression today and surely the solution doesn't exist in the cointegration analysis. Social scientists and applied econometrician investigators need more serious thinking and care to avoid spurious regression, if it is necessary even if data is stationary or cross-sectional. In this study, we extended the Ghouse experiment which is based on simulated data by employing real-world data to assess the effectiveness of the newly proposed Ghouse Equation in comparison to conventional approaches. The findings demonstrate that the Ghouse Equation produces the lowest probability of spurious regression as compared to its counterparts. Moreover, in forecasting performance, Ghouse Equation outperformed its counterparts. These results highlight the Ghouse Equation as a valuable and better tool for econometric analysis for nonstationary time series. [ABSTRACT FROM AUTHOR]

The Portuguese First Republic (1910–1926) was marked by significant instability at the most diverse levels. With a special focus on the financial dimension of this period, the objective of this paper is to test the sustainability of the Portuguese fiscal policy, also referred to as the sustainability of public finances itself. The methodology involves testing the stationarity of public debt and budget balance and also the cointegration between state revenue and expenditure. The results obtained shows that the state's intertemporal budgetary constraint was violated during the First Republic regime, which denotes unsustainability. This conclusion is justified by the existence of a non-stationary budget balance and the absence of cointegration between state revenue and expenditure. These results are manifestly different from those that have already been obtained for other Portuguese regimes, namely for the Estado Novo (1933–1974) and democracy (1974–present), where sustainability existed. This paper is yet another demonstration of how important it is to maintain control of state's accounts. We hope that this paper can be useful to stimulate new research on Portuguese public finances and also on the important issue of fiscal policy sustainability. [ABSTRACT FROM AUTHOR]

Farmers face an increasing administrative burden as agricultural policies and certification systems of trade partners ask for more sustainability reporting. Several indicator frameworks have been developed to measure sustainability, but they often lack empirical operationalization and are not always measured at the farm level. The research gap we address in this paper is the empirical link between the data needs for sustainability reporting and the developments in data management at the farm level. Family farms do not collect much data for internal management, but external demand for sustainability data can partly be fulfilled by reorganizing data management in the farm office. The Farm Financial Accounts (FFAs) and Farm Management Information Systems (FMISs) are the main data sources in the farm office. They originate from the same source of note-taking by farmers but became separated when formalized and computerized. Nearly all European farms have a bank account and must keep financial accounts (e.g., for Value-Added Tax or income tax) that can be audited. Financial accounts are not designed for environmental accounting or calculating sustainability metrics but provide a wealth of information to make assessments on these subjects. FMISs are much less frequently used but collect more technical and fine-grained data at crop or enterprise level for different fields. FMISs are also strong in integrating sensor and satellite data. Integrating data availability and workflows of FFAs and FMISs makes sustainability reporting less cumbersome regarding data entry and adds valuable data to environmental accounts. This paper applies a design science approach to design an artifact, a dashboard for sustainability reporting based on the integration of information flows from farm financial accounting systems and farm management information systems. The design developed in this paper illustrates that if invoices were digitized, most data-gathering needed for external sustainability reporting would automatically be done when the invoices is paid by a bank transfer. Data on the use of inputs and production could be added with procedures as in current FMISs, but with less data entry, fewer risks of differences in outcomes, and possibilities of cross-checking the results. [ABSTRACT FROM AUTHOR]

Every country attempts to improve its balance of payments (BOP) position to enhance macroeconomic policy. Algeria, like many developing countries, is experiencing a BOP deficit. This raises concerns about the reasons for this deficit and prioritizes the quest for potential answers. In the context of the Monetary Approach to the balance of payments (MABP), this paper investigates the long-run relationship between domestic credit, inflation, interest rate, GDP, and net foreign assets to find whether BOP is a monetary phenomenon in Algeria from 1980 to 2019. The Autoregressive Distributed Lag (ARDL) bounds test, combined with the critical approximation p-values of Kripfganz and Schneider (2020), has been applied. The findings clearly showed a long-run relationship among the variables. The long-run estimates strongly supported MABP propositions, and domestic credit had a negative and statistically significant influence on net foreign assets. This suggests that Algeria's BOP is a monetary phenomenon, and excess money supply played a crucial role in Algeria's balance of payments disequilibrium. The contribution of this paper is the use of the ARDL bounds test in combination with the novel critical approximation p-values of Kripfganz and Schneider (2020), which provided more robust results. This study could help monetary authorities to elaborate the suitable policies in dealing with BOP problems in Algeria. [ABSTRACT FROM AUTHOR]

This methodology paper introduces a collective, team-based approach to constructing narratives in narrative research. The goal of the larger study was to explore the pedagogical belief and practices of engineering faculty members. The newly formed team of researchers ranged from novices to experts in the field of qualitative research, and this space created a unique opportunity to reflect on and explore the co-construction of Cody's narrative, the first narrative that the team constructed. The narrative was smoothed and constructed in a way that reduces some of the limitations inherent in narrative smoothing, through a deliberate and intentional negotiation process. We hope that this deeper exploration of our methods is helpful for other narrative researchers who are interested in team-based approaches to co-construction of narratives. [ABSTRACT FROM AUTHOR]

This study estimates the long-term impact of Chinese RMB exchange rate movements on the trade balance between China and the Eastern African Belt and Road Initiative countries. The Pedroni cointegration analysis and the fully modified and dynamic OLS techniques are applied to the dataset from UNComtrade and China Statistical Yearbook 2019. Some specification novelties are the use of imports and exports as independent variables. By so doing, the paper proposes an alternative measure of the trade balance. We find that the RMB exchange rate movements are stable during the sample period and slightly affect the trade balance between China and the Eastern African BRI countries. Additionally, the study does not find evidence supporting the J-curve effect in China-East Africa trade balance. The empirical results suggest that the Eastern African BRI countries would gain much more trading collectively with China than individually. Therefore, the study strongly recommends that the East African Community accelerate its integration process and establish Export Quotas for trade outside the bloc. [ABSTRACT FROM AUTHOR]

The primary objective of this paper is to examine the relationship between finance and economic growth in a cohort of 16 economies within the MENA region spanning a four-decade period from 1980 to 2021. This study employs panel unit root and panel co-integration analyses to investigate this long-term nexus. The fully modified and dynamic Ordinary Least Squares (OLS) approaches are utilized to estimate the long-run coefficients. The findings underscore the existence of cross-sectional interdependence among these nations. Furthermore, Pedroni's panel co-integration research robustly supports the idea of a long-term co-integrating relationship between financial development and economic growth. Our long-run panel estimations reveal a positive and statistically significant impact of financial development on GDP per capita income growth. In addition to this broad analysis, this paper conducts a detailed time-series examination focused on a specific country to validate the robustness of the results. These findings further substantiate the favorable influence of financial development on income growth in the majority of MENA nations. Notably, private sector participation in these economies is found to be alarmingly low. As a result, a significant policy implication of this study underscores the urgent need for policymakers to prioritize measures conducive to private sector expansion. Moreover, enhancing financial inclusion, addressing the crowd-out effect, and tackling non-performing loans are critical areas requiring attention within the MENA region. Furthermore, our research highlights the potential benefits of developing stock markets as part of an optimal strategy to enhance both economic and income growth rates. In conclusion, this study contributes valuable insights into the finance–growth nexus in the MENA region, emphasizing the importance of financial development as a driver of economic prosperity and the need for targeted policy initiatives to support private sector growth and financial stability. [ABSTRACT FROM AUTHOR]

China is a traditional agricultural country, and the integration of rural industries has become an essential policy direction for the current strategy of agricultural modernization in China. The purpose of this study is to evaluate the level of integrated rural industry development in China, identify problems in the development process, and combine analysis of related theories and practices of rural industry integration to propose strategies to enhance the level of integrated rural industry development in China. Hence, this paper, rooted in the integration of rural industries, cross-industry, industry extension, industry agglomeration, and industry penetration, sets up a measurement index system. Using provincial panel data from 2011 to 2020, the paper measures the level of integrated development in rural industries across China and its 31 provinces. Research indicates that during the sample period, the overall level of integrated development in rural industries in various provinces in China has shown an upward trend. Development among the eastern, central, and western regions is highly uneven, but this disparity has been gradually narrowing in recent years. Furthermore, the levels of integrated development in different provinces exhibit significant spatial agglomeration effects. The development level of neighboring regions significantly impacts the province's development status. [ABSTRACT FROM AUTHOR]

At present, China's equipment manufacturing industry and producer service industry are gradually ushered in the leapfrog high-quality development of the rising period. It is particularly important how Shanxi, as the vanguard of the transformation and development, positively responds to the national call to break the shackles of the current integrated development of equipment manufacturing and producer services, for Shanxi Province and even the country's future industrial integration, transformation, and upgrading to accumulate "Shanxi Experience." Therefore, this paper measures and analyzes the integration level of equipment manufacturing and production services between 2012 and 2017 using the input-output method. Then, by constructingthe model of the factors influencing the integration of Shanxi's equipment manufacturing industry and producer service industry, selecting the relevant indicators and bringing in the relevant data from 2009 to 2019, by using the principal component regression analysis method, and thus clarifies the main factors that influence the convergence of the equipment manufacturing industry and production service industry in Shanxi. Finally, this paper has provided targeted countermeasures and suggestions to further promote the integration of the two industries in Shanxi Province. [ABSTRACT FROM AUTHOR]

This paper is aim to find out the relationship among stock markets of BRICS Countries during the COVID-19. International investors want different portfolio while investing in other countries and policy makers needs information about the economies of the countries so this paper help them to get the relationship among BRICS countries. To find out the Cointegration among these secondary data has been used from 31st December 2019 to 31st December 2020. ADF and PP Test used to check the stationarity in the data set and VAR Model used to find out the cointegration between these countries and Granger Causality test used to find out the Cause relationship among the stock prices of the BRICS Countries. After analysis this paper finds that a significant integration among the BRICS Countries. [ABSTRACT FROM AUTHOR]

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Despite the importance of financial development-international trade nexus and its implication for growth, studies have yet to look at the impact of new financial development indicators developed by the International Monetary Fund on international trade performance in Nigeria. This is one of the few attempts made to examine the elasticity of trade to financial development, given the inconclusive findings that permeate the literature. Therefore, this paper investigates the effect of financial development on aggregate trade performance in Nigeria using annual data from 1980 to 2021. The empirical framework is based on the ARDL approach to cointegration and error correction model. The findings revealed that (i) The ARDL bound test revealed the existence of a long-run equilibrium association between the variables; (ii) We find that a unit improvement in financial development induces a 1.1%-1.2% increase in trade performance in the short-run short term and a 9.7% in the long-run; (iii) The results show that a 1% increase in the exchange rate (depreciation) leads to a 0.01% improvement in trade performance in the short-term. The findings have important policy implications. The paper concludes that financial development contributes to trade performance. [ABSTRACT FROM AUTHOR]

For Internet information services, it is very important to closely monitor a large number of key time series data generated by core business for anomaly detection. Although there have been many anomaly detection models in recent years, its practical application is still a big challenge. The model usually needs repeated iteration and parameter adjustment; and for different types of time series data, we need to select different models. Therefore, this paper proposes an anomaly detection model based on time series. The model first designs the statistical features, fitting features, and time-frequency domain features for the time series, and then uses the random forest integration model to automatically select the appropriate features for anomaly classification. In addition, this paper presents an anomaly evaluation index ADC score with timeliness window, which adds the time delay factor of anomaly detection on the basis of F1-score. We use the KPI time series, a representative key performance index in the industry, as the experimental data. It is found that the ADC score of the anomaly detection model in this paper reaches the level of 0.7–0.8, which can meet the needs of practical application. [ABSTRACT FROM AUTHOR]

From the empirical energy literature, it is observed that studies focusing on the energy-economic growth nexus ignore the possible existence of gradual breaks as they employ methods without or with sharp structural breaks. Therefore, one can argue that they may yield biased and inefficient output in the presence of gradual breaks. The goal of this paper is to investigate the impact of solar energy consumption on GDP utilizing quarterly data over the period 1984–2018 for the USA. For this purpose, the paper performs a unit root test and a cointegration test that are based on the Fourier approximation to take gradual breaks into account. The paper also performs the dynamic ordinary least squares estimator to estimate long-run parameters. The findings document that there exists cointegration in the empirical model and that GDP is positively associated with solar energy consumption. Some implications based on the empirical findings are presented in the paper. [ABSTRACT FROM AUTHOR]