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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]

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]

Supply chain instabilities and inflated material prices have had a disruptive impact on cost estimating of construction projects. While several research efforts used national macroeconomic indicators to forecast the prices of domestically produced construction materials, none of the existing studies investigated whether the lagged macroeconomic indicators of the main trading partners could enhance the predictability of the prices of cement, steel, and lumber in the US construction sector. This paper fills this knowledge gap. The authors adopted a multi-step methodology that included: (1) collecting data on the target variables and the candidate leading indicators; (2) identifying the structural breaks in the collected data sets; (3) conducting causality tests to identify short-term associations and cointegration tests to examine long-term relationships; (4) developing vector error correction (VEC) models to forecast the prices in the short and long terms; and (5) evaluating the performance of the proposed models against existing forecasting models in the literature. Results of the Granger test and Johansen test indicate that Canada's overall producer price index (PPI) is a consistent leading indicator of the prices of cement, and Mexico's overall PPI is a consistent leading indicator of the prices of steel. Findings indicate no statistical evidence to suggest that neither Canada's PPI nor Mexico's PPI can be leading indicators of lumber prices. Over an 18-month ahead of sample horizon, the presented VEC models of cement and steel prices outperformed existing models, particularly beyond the 1-year-ahead forecasts. Utilization of the proposed forecasting models can significantly enhance the accuracy of cost estimates and feasibility studies of construction projects. This provides proactive financial planning for construction contractors and project owners through improved short- and long-term forecasting of the prices of main construction materials. [ABSTRACT FROM AUTHOR]

Chiller fault diagnosis plays a crucial role in optimizing energy efficiency within heating, ventilation, and air conditioning (HVAC) systems. The non-stationary nature of chiller fault data presents a significant challenge, as conventional methodologies often fail to adequately capture the relationships between non-stationary variables. To address this limitation and enhance diagnostic accuracy, this paper proposes an improved graph deviation network for chiller fault diagnosis by integrating the sparse cointegration analysis and the convolutional block attention mechanism. First, in order to obtain sparse fault features in non-stationary operation, this paper adopts the sparse cointegration analysis method (SCA). Further, to augment the diagnosis accuracy, this paper proposes the improved graph deviation network (IGDN) to classify fault datasets, which is a combination of the output of a graph deviation network (GDN) with a convolutional block attention mechanism (CBAM). This novel architecture enables sequential evaluation of attention maps along independent temporal and spatial dimensions, followed by element-wise multiplication with input features for adaptive feature optimization. Finally, detailed experiments and comparisons are performed. Comparative analyses reveal that SCA outperforms alternative feature extraction algorithms in addressing the non-stationary characteristics of chiller systems. Furthermore, the IGDN exhibits superior fault diagnosis accuracy across various fault severity levels. [ABSTRACT FROM AUTHOR]

This paper addresses the controversial issue of the direct and indirect effects of public R&D on growth. We look at six variables of R&D-driven growth jointly for 14 OECD countries using methods of dynamic systems for panel data analysis: GDP, technical change, domestic and foreign businesses and public R&D. Cointegration tests suggest four long-run relations for the six variables. We estimate these relations using group mean versions of fully modified and dynamic OLS. Domestic public R&D has positive long-run regression coefficients for direct effects on productivity and indirect ones via private R&D. Here, we build a panel vector-error-correction model with these long-term relations. Shocks to domestic public R&D enhance domestic private R&D, technical change and the GDP. Permanent changes in foreign public and private R&D have positive growth effects, which are transitional for foreign public R&D. [ABSTRACT FROM AUTHOR]

Purpose: Based on the micro-electro-mechanical system (MEMS) technology, acoustic emission sensors have gained popularity owing to their small size, consistency, affordability and easy integration. This study aims to provide direction for the advancement of MEMS acoustic emission sensors and predict their future potential for structural health detection of microprecision instruments. Design/methodology/approach: This paper summarizes the recent research progress of three MEMS acoustic emission sensors, compares their individual strengths and weaknesses, analyzes their research focus and predicts their development trend in the future. Findings: Piezoresistive, piezoelectric and capacitive MEMS acoustic emission sensors are the three main streams of MEMS acoustic emission sensors, which have their own advantages and disadvantages. The existing research has not been applied in practice, and MEMS acoustic emission sensor still needs further research in the aspects of wide frequency/high sensitivity, good robustness and integration with complementary metal oxide semiconductor. MEMS acoustic emission sensor has great development potential. Originality/value: In this paper, the existing research achievements of MEMS acoustic emission sensors are described systematically, and the further development direction of MEMS acoustic emission sensors in the future research field is pointed out. It provides an important reference value for the actual weak acoustic emission signal detection in narrow structures. [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]

The paper investigates the relationship between tourism, energy consumption, trade openness, economic growth, and CO2 emissions for 20 economies of the APEC region from 1995 to 2017. This paper employs cross-sectional dependence with heterogeneous panel estimation techniques. The data confirms cross-sectional dependence, and the CIPS panel unit root test shows that the variables are stationary at their first differences. The Westerlund panel cointegration test affirms a long-run relationship among the variables. Tourism and trade openness have significant positive effects on CO2 emissions while economic growth and energy consumption adversely affect CO2 emissions in the long-run. The panel non-causality test reveals that there is a one-way causality running from tourism to CO2 emissions and economic growth to CO2 emissions. [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]

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

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]

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]

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]

In order to improve the economic growth efficiency of industrial and commercial enterprises in coastal cities and realize the GDP growth of coastal cities, this paper studies the economic growth factors of industrial and commercial enterprises in coastal cities based on the unexpected super efficiency model. Based on the research and analysis of the previous economic growth theories, this paper finds out the main factors that affect the economic growth of industrial and commercial enterprises in coastal cities, and uses the advanced econometric method to establish the relevant test model to analyze the correlation between the time series of economic growth factors and the time series of coastal cities, so as to realize the economic growth factors of industrial and commercial enterprises in coastal cities Element study. The empirical results show that the main factors affecting the economic growth of industrial and commercial enterprises in coastal cities are capital and labor force, with labor force as the main body; Technical and institutional factors also contribute to the GDP of industrial and commercial enterprises in coastal cities, but the impact is not significant and needs further improvement. In general, these factors can promote the economic growth of industrial and commercial enterprises in coastal cities. The time series and time series of each factor variable are first-order non-stationary series with long-term cointegration relationship. [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]

It can be observed from the existing energy literature the previous papers investigating the influence of renewables consumption on GDP for the USA commonly ignore structural breaks in the US economy. Hence, the purpose of this paper is to examine the impact of renewable energy consumption on economic growth for the USA using quarterly data over the period 1977Q1-2019Q3 through unit root and cointegration methods based on different approaches in modelling structural breaks. Our empirical evidence confirms that all unit root tests give similar outputs and show all the variables become stationary at 1st differences. Besides, cointegration tests show highly different results in terms of the statistical significance of the coefficients. For instance, the cointegration test without structural breaks indicates that renewable energy consumption has no impact on economic growth. With sharp structural breaks in the cointegration approach, there is no cointegration between the variables. The empirical findings of the cointegration test with sharp and gradual breaks imply that renewable energy consumption has positive effects on economic growth. This paper discusses the implications of the empirical findings. [ABSTRACT FROM AUTHOR]

This paper investigates the all important issue of diagnostic tests, including unit roots and cointegration, in the tourism demand modelling literature. The origins of this study lie in the apparent lack in the tourism economics literature of detail concerning the diagnostic test aspect. Study of this deficiency has suggested that previous literature on tourism demand modelling may be divided into two categories: the pre-1995 and post-1995 studies. It was found that the pre-1995 and some post-1995 studies have ignored unit root tests and co-integration and, hence, are vulnerable to the so-called ‘spurious regression’ problem. In highlighting the key diagnostic tests reported by post-1995 studies, this paper contends that there is no need to report the autoregressive conditional heteroskedasticity (ARCH) test, which is applicable only to financial market analysis where the dependent variable is return on an asset. More generally, heteroskedasticity is not seen as a problem in time-series data. However, the reporting of a greater than necessary range of diagnostic tests — some of which do not have any theoretical justification with regard to tourism demand analysis — does not diminish the precision of the results or the model. This paper should appeal to scholars involved in tourism demand modelling. Copyright © 2003 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Summary: This paper undertakes both a narrow and wide replication of the estimation of a money demand function conducted by Ireland (American Economic Review, 2009). Using US data from 1980 to 2013, we show that the substantial increase in the money‐income ratio during the period of near‐zero interest rates is captured well by the log–log specification but not by the semi‐log specification, contrary to the result obtained by Ireland (2009). Our estimate of the interest elasticity of money demand over the 1980–2013 period is about one‐tenth that of Lucas's paper published in 2000, which used a log–log specification. Finally, neither specification satisfactorily fits post‐2015 US data. [ABSTRACT FROM AUTHOR]