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Objective: To evaluate the impact on rural Veterans' access to social work services of a Department of Veterans Affairs (VA) national program to increase social work staffing, by Veterans' rurality, race, and complex care needs. Data Sources and Study Setting: Data obtained from VA Corporate Data Warehouse, including sites that participated in the social work program between October 1, 2016 and September 30, 2021. Study Design: The study outcome was monthly number of Veterans per 1000 individuals with 1+ social work encounters. We used difference‐in‐differences to estimate the program effect on urban, rural, and highly rural Veterans. Among rural and highly rural Veterans, we stratified by race (American Indian or Alaskan Native, Asian, Black, Native Hawaiian or Other Pacific Islander, and White) and complex care needs (homelessness, high hospitalization risk, and dementia). Data Collection: We defined a cohort of 740,669 Veterans (32,434,001 monthly observations) who received primary care at a participating site. Principal Findings: Average monthly social work use was 8.7 Veterans per 1000 individuals. The program increased access by 49% (4.3 per 1000; 95% confidence interval, 2.2–6.3). Rural Veterans' social work access increased by 57% (5.0; 3.6–6.3). Among rural/highly rural Veterans, the program increased social work access for those with high hospitalization risk by 63% (24.5; 18.2–30.9), and for Veterans experiencing homelessness, 35% (13.4; 5.2–21.7). By race, the program increased access for Black Veterans by 53% (6.1; 2.1–10.2) and for Asian Veterans by 82% (5.1; 2.2–7.9). Conclusions: At rural VA primary care sites with social work staffing below recommended levels, Black and Asian Veterans and those experiencing homelessness and high hospitalization risk may have unmet needs warranting social work services. [ABSTRACT FROM AUTHOR]

This article presents the gretl package BayTool which integrates the software functionalities, mostly concerned with frequentist approaches, with Bayesian estimation methods of commonly used econometric models. Computational efficiency is achieved by pairing an extensive use of Gibbs sampling for posterior simulation with the possibility of splitting single-threaded experiments into multiple cores or machines by means of parallelization. From the user's perspective, the package requires only basic knowledge of gretl scripting to fully access its functionality, while providing a point-and-click solution in the form of a graphical interface for a less experienced audience. These features, in particular, make BayTool stand out as an excellent teaching device without sacrificing more advanced or complex applications. [ABSTRACT FROM AUTHOR]

It is a well-known phenomenon that nonlinearities that are inherent in the relationship among economic variables negatively affect the commonly used estimators in the econometric models. The nonlinearities cause an instability of the estimated parameters that, in particular, are unable to capture a local relationship between the response and the covariate. The main aim of the paper is the simultaneous consideration of spatial effects as well as nonlinearities through an advanced semiparametric spatial autoregressive econometric model. The paper seeks to contribute to empirical studies of regional science focused on the application of semiparametric spatial autoregressive econometric models. We outline an approach that can be used to correct nonlinearities by incorporating a semiparametric idea within the framework of econometric models. We use an expansion by penalised basis splines that are highly flexible and are able to capture local nonlinearities between variables. In the empirical study, we fit different econometric models that attempt to explain the dynamics of the European Union's regional unemployment. The results show that regional unemployment exhibits significant spatial dependence, indicating interconnectedness among neighbouring regions and suggesting the adoption of a semiparametric spatial autoregressive model for improved modelling flexibility, surpassing traditional parametric approaches. [ABSTRACT FROM AUTHOR]

Innovation is the source of economic growth. Innovation in a region comes from its own knowledge creation and knowledge spillovers from other regions. Previous studies showed that R&D factors flow benefits knowledge spillover, thereby promoting economic growth. But these studies ignored the impact of a region's knowledge-absorptive capacity on knowledge spillovers. Ignoring the impact of regional absorptive capacity means that the knowledge spillover from the same R&D factors flow is the same, clearly inconsistent with reality. This thesis analyzes the impact of R&D factors flow on economic growth and explores the moderating effect of regional absorptive capacity on the relationship between R&D factors flow and economic growth from theoretical and empirical perspectives. First, we construct a knowledge creation and diffusion model of the new economic geography, including regional absorptive capacity, and analyze the theoretical logic of the flow of R&D factors and regional absorptive capacity influencing economic growth. Second, we employ spatial econometric models to examine the impact of R&D factors flow and regional absorptive capacity on economic growth, utilizing panel data of 30 provinces in China from 2008 to 2021. The results demonstrated a spatial positive correlation between regional economic growth in China. The R&D factors flow could have significantly promoted not just a region's direct economic growth, but also the economic growth of surrounding regions via spatial spillover effects. Furthermore, the stronger the regional absorptive capacity, the greater the direct effects and spatial spillover effects of the R&D factors flow on economic growth. The novelty of this article is to introduce regional absorptive capacity into the theoretical model, refine the methodology for assessing regional absorptive capacity in empirical research, and examine its moderating effect between the inflow of R&D factors and regional economic growth. This article reveals that the positive impact of the inflow of R&D factors on spatial spillovers and economic growth varies depending on regional absorptive capacity. According to the conclusions above, enhancing regional absorptive capacity is equally important as facilitating the flow of R&D factors. Therefore, it is vital for a region to strengthen its absorptive capacity for new knowledge while promoting R&D factors flow. The study provides valuable policy insights for accelerating the flow of innovation factors, enhancing regional absorptive capacity, and consequently promoting long-term sustainable economic development in the region. [ABSTRACT FROM AUTHOR]

One of the most important hyper-parameters in duration-dependent Markov-switching (DDMS) models is the duration of the hidden states. Because there is currently no procedure for estimating this duration or testing whether a given duration is appropriate for a given data set, an ad hoc duration choice must be heuristically justified. In this paper, we propose and examine a methodology that mitigates the choice of duration in DDMS models when forecasting is the goal. The novelty of this paper is the use of the asymmetric Aranda-Ordaz parametric link function to model transition probabilities in DDMS models, instead of the commonly applied logit link. The idea behind this approach is that any incorrect duration choice is compensated for by the parameter in the link, increasing model flexibility. Two Monte Carlo simulations, based on classical applications of DDMS models, are employed to evaluate the methodology. In addition, an empirical investigation is carried out to forecast the volatility of the S&P500, which showcases the capabilities of the proposed model. [ABSTRACT FROM AUTHOR]

Mineral resources are the basic materials for global economic development. Assessing mineral resource supply chain resilience is an important pillar of mineral resource supply chain stability management. The globality, heterogeneity and complexity of supply chain bring challenges to the resilience assessment of global mineral resource supply chain. To solve this problem, a method based on multi-region input-output model, network percolation model and econometric model is proposed, which is able to measure the resilience of global mineral resource supply chain and its influencing factors from the perspective of the whole system. The percolation phase transition is introduced to measure the critical state of global mineral resource supply chain system collapse facing external disruption. Using the proposed method, this paper conducts an empirical study on the evolution of global mineral resource supply chain resilience from 2005 to 2014. The results show that the resilience of global mineral resource supply chain declined by 39.6% in 2005-2014. Most of the critical links that caused the collapse of the global mineral resource supply chain network are the manufacturing sector and its upstream and downstream sectors. The structure of supply chain network plays a key role in network resilience. Increasing the number of linkages in upstream and downstream could improve network resilience, but the increase of linkage strength would deteriorate network resilience. [ABSTRACT FROM AUTHOR]

The aim of this paper is to test the relationship between manufacturing production and economic growth in Portugal from the 1950s to the present day, following Nicholas Kaldor's (1908–1986) thinking, which assumes that manufacturing is the true engine of the economic activity of a country. By estimating a series of econometric models, including a Vector Autoregressive (VAR) model, it was possible to identify a positive relationship between growth and manufacturing production since the 1950s. The results also show that each increase of 1 percentage point (pp) in real growth rate of the production of basic metallurgical industries and manufacture of metallic products in a certain year t−1 for the period 1956–2019, caused an increase of 0.07 pp in the Portuguese economic growth during a following year t on average. We hope that these findings will stimulate new and interesting research regarding the important issues of manufacturing production and economic growth. [ABSTRACT FROM AUTHOR]

Stimulating sustainable FDI through the connection of financial development and its moderating relationships with environmental quality and regulatory quality emerges as a crucial agenda nowadays. This study investigates into this relationship using quarterly data from 1990Q1 to 2022Q4 for Canada; employing ARDL bound tests, Granger Causality, and FM-OLS econometric models. Foreign direct investment is the dependent variable of this study. The findings confirm significant long-run relationships among financial development, stock market development, and the moderating effects of environmental and regulatory quality on FDI inflows in the Canadian economy. Conversely, in the short run, financial development, stock market development, and economic growth exhibit bidirectional causal links with FDI, while environmental quality, regulatory quality, and trade openness demonstrate unidirectional causal links with FDI. The error correction mechanism indicates that all variables quickly return to equilibrium except trade openness. Robustness checks further confirm that all the variables have fully modified co-integrating relationship with FDI inflows including the moderating effects of environmental and regulatory quality which is the innovation in the FDI-Growth existing literature. Thus, policymakers are urged to prioritize environmental quality and regulatory quality, alongside other significant explanatory variables identified in this study to promote sustainable FDI inflows. [ABSTRACT FROM AUTHOR]

The aim of this article is to examine which selected internal factors influence the profitability (ROA) of companies in the energy sector in Poland and how they do so, over the period 2018–2021, taking into account two groups: all types of activities (984 companies) and electricity production (508 companies). This study uses Pearson correlation analysis, Wilcoxon pairwise rank test, descriptive statistics and multiple linear regression to build eight ROA econometric models, four for each group. The research shows that in the energy sector, in particular, variables relating to the capital structure (total equity/total assets, long-term liabilities/total assets, short-term liabilities/total assets and long-term liabilities/short-term liabilities ratios) have a statistically significant impact (positive or negative) on the profitability (ROA). The aforementioned ratios appear in various combinations in all eight ROA models. The use of equity to finance the activities of companies in this sector seems to be particularly beneficial, as the total equity/total assets ratio occurs in as many as seven out of eight models and, moreover, it always has a positive impact on the ROA. The remaining analyzed variables relating to the structure of assets (fixed assets/total assets ratio), financial liquidity (current ratio) and the age of the company appear in the models as statistically significant quite rarely, having a different impact on the ROA (positively or negatively). However, variables such as the fixed assets/current assets and total liabilities/total equity ratios do not have a statistically significant impact on the ROA at all in any of the studied groups of enterprises. The research results suggest that managers, in order to shape profitability (measured by ROA), should pay special attention to the capital structure, i.e., the proportions of the use of equity, long-term liabilities and short-term liabilities to finance the operations of energy companies as these independent variables appear most often in ROA models. Other analyzed factors, such as the assets structure (the share of fixed assets in total assets) or financial liquidity, also have an impact on the return on assets; therefore, their use in energy companies should also be considered. Moreover, the research shows a large diversity of factors shaping ROA in econometric models, the way they affect the dependent variable (positive or negative) and the degree of model fit (R2), both in individual years and in the two groups of companies studied. This proves that it is not possible to clearly and finally determine which factors and how (positive or negative) they affect the profitability. This influence can change over time depending on the circumstances, which indicates the need for the continuous involvement of decision makers in the management process and making decisions based on reliable and appropriate-to-the-situation analyses. [ABSTRACT FROM AUTHOR]

Urban heat island (UHI) has posed a threat to sustainable development, and the identification of the spatial characteristics of UHI is a prerequisite for the mitigation of UHI effects. Taking Kunming City and Guiyang City in the karst region of Southwest China as two examples, this study utilized spatial econometric modeling and random forest regression to identify and compare the spatial distribution and impact of urban heat islands related to topographic and urban morphology. The results indicated that the UHI in Kunming and Guiyang mainly monocentrically and polycentrically distributed, respectively, and the urban heat island intensity (UHII) in Kunming was significantly higher than that in Guiyang. The spatial error model effectively reflected the influence of topography and urban form on UHII, and the random forest regression model objectively measured the contribution of different influencing factors to UHII. Enhanced vegetation index (EVI), population density (PD), percentage of impervious surface (PIS), ground sky view factor (GSVF), building density (BD) and percentage of water bodies (PW) had similar effects and contributions to the UHII in the two cities, while urban surface roughness (USR) contributed less than topographic slope (SLOPE) on UHII in Kunming, and the SLOPE contributed less than USR in Guiyang. In the karst ecologically fragile zone, the morphology of Kunming and Guiyang are mainly shaped by the topography, which can be characterized as monocentric and polycentric, respectively. And the UHI distribution and UHII were also closely related to topographic patterns. EVI, SLOPE and PW, all showed inhibitory effects on UHII, and EVI contributed the most. PD, PIS, USR and BD, exacerbate the UHII, with PD contributing the most. The above conclusions can provide a reference for policymakers and urban planners to optimize urban morphology and mitigate urban heat island. [ABSTRACT FROM AUTHOR]

The rapid increase in population and economy, coupled with accelerated urbanization, is placing immense pressure on the water–energy–food (WEF) system. In this context, the water–energy–food nexus framework has emerged, recognizing the interdependencies and interactions among water, energy, and food systems, with the aim of optimizing resource management through cross-sectoral collaboration to promote sustainable development. Understanding the spatio-temporal differentiation patterns of the WEF nexus and elucidating the driving mechanisms behind changes in their coupling relationships is essential. This knowledge is crucial for ensuring the security of each subsystem and enhancing the overall sustainability of interconnected systems through coordinated efforts. To address these challenges, this study first established evaluation indicators for water, energy, and food security to quantify their levels and spatio-temporal dynamics. Subsequently, the degrees of coupling coordination within the WEF nexus were calculated. Finally, the WEF nexus's spatial correlations were analyzed by using a spatial autocorrelation model. Spatial econometric models then identified key factors affecting its coordination. The results revealed significant spatial heterogeneity in water, energy, and food security across mainland China's provinces. From 2002 to 2022, water security improved substantially in 87% of the provinces, while energy security began to improve in the eastern regions following a phase of high consumption. Food security saw significant enhancements, particularly in Inner Mongolia and the northeastern provinces. The overall coupling coordination of the WEF nexus improved across 30 provinces, progressing toward primary coordination. However, Henan and Anhui provinces experienced fluctuations in WEF nexus coordination. Spatial correlation analysis showed upward trends and increased clustering in WEF nexus coordination. Factors such as economic development and population positively influenced coordination, while economic agglomeration, education, and effective irrigation area had negative effects. This study elucidates the complex interconnections and key influencing factors within the WEF nexus, providing a reference framework and practical recommendations for equitable resource management. [ABSTRACT FROM AUTHOR]

This study elaborates on a typology of demographic change and tests this definition at the lowest granular level (LAU2, municipality) with official data. This typology distinguishes between fragile and resilient municipalities based on population dynamics (in terms of duration and intensity) over 1991–2021. This study's second aim is to elaborate a spatial autoregressive econometric model to evaluate to what extent and in which direction the rate of participation of potential beneficiaries of the Rural Development Programmes (RDPs) of 2014–2020 is affected by demographic change and other explanatory variables. Regression models compare the results of the OLS (aspatial) and spatial autoregressive models (SAR) of four types of participation rates (all RDP schemes; all LEADER schemes; sectoral schemes of RDP and LEADER; non-sectoral schemes of RDPs and LEADER). This comparison makes it possible to understand the differences between centralised and decentralised management and between sectoral and broader rural-targeted schemes. The results of the models appear attractive in interpreting the role of RDP instruments in different regions and local areas. First, the rate of participation is strongly dependent on macro-regional differences. Regarding the demographic factors at the local level, this study highlights that demographic fragility does not necessarily hamper the use of RDP measures. Conversely, the participation rate in RDP policy schemes seems particularly significant in very fragile areas, whereas significance has yet to be proved in other demographic typologies. This result holds particularly true for the policy uptake of non-sectoral schemes. Furthermore, LEADER decentralised interventions fit the fragile areas more than resilient and vital ones due to the territorially targeted approach followed by the Local Action Groups. [ABSTRACT FROM AUTHOR]

The increasing trend of transforming fertile land for non-agricultural uses, particularly at the rural–urban interface and city outskirts, has seriously threatened food security in developing countries. However, in an agrarian nation like, Nepal where productive land is minimal due to its challenging geographical terrain, the agricultural land transformation trend and its motivating factors have not been extensively studied. This study emphasizes the shifting land use patterns and factors shaping farmers’ choices for conversion of farmland into non-farm uses in Dhading, Nepal. Land use land cover data analysis revealed that productive agricultural land decreased by 60.28% while non-farm use of land increased by 93.14% in Dhunibeshi Municipality, Dhading between 2013 and 2022. Further analysis using farm-level cross-sectional data shows that 36% of the households are eager to switch from farming to non-farm use. The results from econometric models indicate that socio-economic factors (gender, education, economically active family members, land proportion), vegetable farm attributes (farm income, crop diversification), farm proximity to urban areas, and non-farm firms, and land conversion proposal collectively influence farmers’ decisions regarding agricultural land use change. Promoting widespread adoption of low-cost production technology and making the agriculture sector lucrative could contribute towards sustainable agricultural land management in the future. [ABSTRACT FROM AUTHOR]

We argue that the 40‐year‐old Feldstein–Horioka "puzzle" should have never been labeled as such. We discuss two problems with the literature. First, we show that the series of investment and saving rates typically used in empirical exercises to test the Feldstein–Horioka thesis are not appropriate. The correct series to properly test it are not collected. Second, we show that the Feldstein–Horioka regression is not a model in the econometric sense, that is, an equation with a proper error term (a random variable). The reason is that by adding the capital account to their regression, one gets the accounting identity that relates the capital account, domestic investment, and domestic saving. This implies that the estimate of the coefficient of the saving rate in the Feldstein–Horioka regression can be thought of as a biased estimate of the same coefficient in the accounting identity, where it has a value of 1. Because the omitted variable is known, we call it pseudo bias. Given that this (pseudo) bias is known to be negative and less than 1 in absolute terms, it should come as no surprise that the Feldstein–Horioka regression yields a coefficient between 0 and 1. [ABSTRACT FROM AUTHOR]

Single-vehicle crashes, particularly those caused by speeding, result in a disproportionately high number of fatalities and serious injuries compared to other types of crashes involving passenger vehicles. This study aims to identify factors that contribute to driver injury severity in single-vehicle crashes using machine learning models and advanced econometric models, namely mixed logit with heterogeneity in means and variances. National Crash data from the Crash Report Sampling System (CRSS) managed by the National Highway Traffic Safety Administration (NHTSA) between 2016 and 2018 were utilized for this study. XGBoost and Random Forest models were employed to identify the most influential variables using SHAP (Shapley Additive Explanations), while a mixed logit model was utilized to model driver injury severity accounting for unobserved heterogeneity in the data collection process. The results revealed a complex interplay of various factors that contribute to driver injury severity in single-vehicle crashes. These factors included driver characteristics such as demographics (male and female drivers, age below 26 years and between 35 and 45 years), driver actions (reckless driving, driving under the influence), restraint usage (lap-shoulder belt usage and unbelted), roadway and traffic characteristics (non-interstate highways, undivided and divided roadways with positive barriers, curved roadways), environmental conditions (clear and daylight conditions), vehicle characteristics (motorcycles, displacement volumes up to 2500 cc and 5,000–10,000 cc, newer vehicles, Chevy and Ford vehicles), crash characteristics (rollover, run-off-road incidents, collisions with trees), temporal characteristics (midnight to 6 AM, 10 AM to 4 PM, 4th quarter of the analysis period: October to December, and the analysis year of 2017). The findings emphasize the significance of driving behavior and roadway design to speeding behavior. These aspects should be given high priority for driver training as well as the design and maintenance of roadways by relevant agencies. [ABSTRACT FROM AUTHOR]

Introduction: This article presents a model of political competition in which political parties, through clientelism strategies, vie for control of legislative seats. Parties exercise political violence to prevent potential rivals from gaining power and threatening their position within the hybrid political regime. The theory suggests that the degree of political violence exerted by parties in hybrid regimes will increase (decrease) as they concentrate more (less) power in the legislature. Methods: Using the methodology of analytical narratives, we examine the narrative on political violence in the Colombian political regime to identify key actors, strategies, information sets, and institutional changes. From these identified elements, we construct a theoretical model of political competition within the mathematical theory of games to explain the institutional changes highlighted in the narrative. Finally, we develop an econometric model to find statistical evidence supporting the predictions of the theoretical model derived from the narrative. Results: The narrative and the analytical narrative indicate that, in a hybrid regime, a higher degree of political power concentration causes a higher rate of political violence. The estimation of the Vector Auto-regression model allows us to analyze the response of the violence rate to a shock in the concentration index. Following an unexpected increase in the concentration of political power, the violence rate shows an initial increase of approximately 0.3 percentage points above its initial value. Subsequently, the effect attenuates slightly and stabilizes at around 0.2 percentage points above the baseline, maintaining this level throughout the period. Discussion/Conclusion: The theoretical model proposed in this paper suggests an explanation of political violence that diverges from the explanations offered by traditional theories. We suggest that the theoretical model proposed here captures the historical logic of the relationship between violence, political clientelism, and exclusion in Colombia, a country with a relatively long tradition as a formal democracy (since 1958), leading us to interesting conclusions that have not been proposed so far in the literature on violence in Latin America. [ABSTRACT FROM AUTHOR]

China's reform began in the countryside, and rural reform began in Anhui. This study uses Anhui Province, China as an example in analyzing the impact of agricultural science and technology innovation resource allocation on rural revitalization, particularly by constructing an econometric model. Results indicate that the allocation of agricultural science and technology innovation resources has a steady positive impact on rural revitalization. Improvements in the allocation of agricultural science and technology innovation resources can effectively promote the development of rural revitalization and the spatial spillover of rural development, thereby facilitating the promotion of the spillover of neighboring areas to local rural development. A nonlinear relationship exists between allocation of agricultural science and technology innovation resources and rural revitalization. Lastly, rural revitalization can only be promoted substantially when the allocation of agricultural science and technology innovation resources reaches a certain condition. [ABSTRACT FROM AUTHOR]

In this study, we investigate an appointment sequencing and scheduling problem with heterogeneous user delay tolerances under service time uncertainty. We aim to capture the delay tolerance effect with heterogeneity, in an operationally effective and computationally tractable fashion, for the appointment scheduling problem. To this end, we first propose a Tolerance-Aware Delay (TAD) index that incorporates explicitly the user tolerance information in delay evaluation. We show that the TAD index enjoys decision-theoretical rationale in terms of Tolerance sensitivity, monotonicity, and convexity and positive homogeneity, which enables it to incorporate the frequency and intensity of delays over the tolerance in a coherent manner. Specifically, the convexity of TAD index ensures a tractable modeling of the collective delay dissatisfaction in the appointment scheduling problem. Using the TAD index, we then develop an appointment model with known empirical service time distribution that minimizes the overall tolerance-aware delays of all users. We analyze the impact of delay tolerance on the sequence and schedule decisions and show that the resultant TAD appointment model can be reformulated as a mixed-integer linear program (MILP). Furthermore, we extend the TAD appointment model by considering service time ambiguity. In particular, we encode into the TAD index a moment ambiguity set and a Wasserstein ambiguity set, respectively. The former captures effectively the correlation among service times across positions and user types, whereas the latter captures directly the service time data information. We show that both of the resultant TAD models under ambiguity can be reformulated as polynomial-sized, mixed-integer conic programs (MICPs). Finally, we compare our TAD models with some existing counterpart approaches and the current practice using synthetic data and a case of real hospital data, respectively. Our results demonstrate the effectiveness of the TAD appointment models in capturing the user delay tolerance with heterogeneity and mitigating the worst-case delays. History: Accepted by Pascal Van Hentenryck, Area Editor for Computational Modeling: Methods & Analysis. Funding: S. Wang was supported by the National Natural Science Foundation of China [Grants 71922020, 72171221, and 71988101, entitled "Econometric Modeling and Economic Policy Studies"], the Fundamental Research Funds for the Central Universities [Grant UCAS-E2ET0808X2], and the Major Program of National Natural Science Foundation of China [Grant 72192843]. S. Wang was also supported by a grant from MOE Social Science Laboratory of Digital Economic Forecasts and Policy Simulation at UCAS. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information (https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2023.0025) as well as from the IJOC GitHub software repository (https://github.com/INFORMSJoC/2023.0025). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/. [ABSTRACT FROM AUTHOR]

The assurance of universal access to electricity refers to not only physical access to electricity, heating, and/or cooling services but also making them affordable to families. This is the case in Brazil, where physical electricity access reaches more than 99% of the urban population, but a high proportion of households are disconnected due to lack of payment. Affordability is a key factor in planning the expansion and maintenance of the electricity grid. In this paper, therefore, we propose the development of a strategy, called the Payment Capability Indicator (PCI), that provides new information about the relationship between energy cost and family income. The classic indicator, the Ten Percent Rule (TPR), was the base, and empirical research was conducted in two low-income neighborhoods in Paraná state, southern Brazil. The results consider variables that add new information to guide local public policies, combining interventions to improve affordability, energy efficiency, alternatives to energy services and consumer behavioral changes. [ABSTRACT FROM AUTHOR]

The objective of this article is to know the relationship between tax collection and tax progressivity in Mexico. To this aim the progressivity level of Income Tax (IT) and Value Added Tax (VAT) is calculated, using the Kakwani index. These calculations show that IT is a progressive tax, while VAT is a regressive tax. Subsequently, by means of an econometric model, a positive relationship between tax collection and the level of tax progressivity is verified, which means that greater the progressivity, greater the tax collection and viceversa. [ABSTRACT FROM AUTHOR]

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This paper examines the predictive ability of volatility in time series and investigates the effect of tradition learning methods blending with the Wasserstein generative adversarial network with gradient penalty (WGAN-GP). Using Brent crude oil returns price volatility and environmental temperature for the city of Sydney in Australia, we have shown that the corresponding forecasts have improved when combined with WGAN-GP models (i.e., ANN-(WGAN-GP), LSTM-ANN-(WGAN-GP) and BLSTM-ANN (WGAN-GP)). As a result, we conclude that incorporating with WGAN-GP will' significantly improve the capabilities of volatility forecasting in standard econometric models and deep learning techniques. [ABSTRACT FROM AUTHOR]

The effects of land transfer on agricultural carbon emissions and their underlying mechanisms must be investigated if we are to achieve sustainable development and environmentally friendly high-quality agricultural development. This research experimentally investigated the spatial impacts of land transfer on agricultural carbon emissions and their underlying causes using multiple econometric models based on provincial panel data covering the years 2010 to 2022. The results allow us to draw the following conclusions: (1) Land transfer significantly inhibits agricultural carbon emissions. This conclusion remained valid after various robustness checks, including a reduction in sample size, change model type, and adjustment of geographical regions. (2) Agricultural socialized services play a positive moderating role in the process of land transfer to curb agricultural carbon emissions. (3) Land transfer has a substantial spillover effect on agricultural carbon emissions, resulting in significantly reduced emissions in the immediate area and nearby regions. [ABSTRACT FROM AUTHOR]

Purpose: An emerging body of literature has pinpointed the role of supply chain structure in influencing the extent to which supply chain members disclose information about their internal practices and performance. Nevertheless, empirical research investigating the effects of firm-level relational embeddedness on network-level transparency still lags. Drawing on social network analysis, this research examines the effect of relational embeddedness on supply chain transparency and the contingent role of digitalization in the context of environmental, social and governance (ESG) information disclosure. Design/methodology/approach: In their empirical analysis, the authors collected secondary data from the Bloomberg database about 2,229 firms and 14,007 ties organized in 107 extended supply chains. The authors employed supplier and customer concentration metrics to measure relational embeddedness and performed multiple econometric models to test the hypothesis. Findings: The authors found a positive effect of supplier concentration on supply chain transparency, but the effect of customer concentration was not significant. Additionally, the digitalization of focal firms reinforced the impact of supplier concentration on supply chain transparency. Originality/value: The study findings contribute by underscoring the critical effect of relational embeddedness on supply chain transparency, extending prior literature on social network analysis, providing compelling evidence for the intersection of digitalization and supply chain management, and drawing important implications for practices. [ABSTRACT FROM AUTHOR]

Greenhouse gas (GHG) emission-induced climate change, particularly occurring since the mid-20th century, has been considerably affecting short-term weather conditions, such as increasing weather variability and the incidence of extreme weather-related events. Milk production is sensitive to such changes. In this study, we use spatial panel econometric models, the spatial error model (SEM) and the spatial Durbin model (SDM), with a panel dataset at the state-level varying over seasons, to estimate the relationship between weather indicators and milk productivity, in an effort to reduce the bias of omitted climatic variables that can be time varying and spatially correlated and cannot be directly captured by conventional panel data models. We find an inverse U-shaped effect of summer heat stress on milk production per cow (MPC), indicating that milk production reacts positively to a low-level increase in summer heat stress, and then MPC declines as heat stress continues increasing beyond a threshold value of 72. Additionally, fall precipitation exhibits an inverse U-shaped effect on MPC, showing that milk yield increases at a decreasing rate until fall precipitation rises to 14 inches, and then over that threshold, milk yield declines at an increasing rate. We also find that, relative to conventional panel data models, spatial panel econometric models could improve prediction performance by leading to smaller in-sample and out-sample root mean squared errors. Our study contributes to the literature by exploring the feasibility of promising spatial panel models and resulting in estimating weather influences on milk productivity with high model predicting performance. [ABSTRACT FROM AUTHOR]

Professional football clubs invest vast amounts of money in the recruitment of players. This article presents the latest advances in statistical modeling of the factors that market actors take into consideration to determine the transfer prices of professional football players. It extends to a global scale the econometric approach previously developed by the authors to evaluate the transfer prices of players under contract with clubs from the five major European leagues. The statistical technique used to build the model is multiple linear regression (MLR), with fees paid by clubs as an independent variable. The sample comprises over 8000 transactions of players transferred for money from clubs worldwide during the period stretching from July 2014 to March 2024. This paper shows that a statistical model can explain up to 85% of the differences in the transfer fees paid for players. Despite the specific cases and other possible distortions mentioned in the discussion, the use of a statistical model to determine player transfer prices is thus highly relevant on a global scale. [ABSTRACT FROM AUTHOR]

In the context of rapid urbanization in third-world countries, many cities adopt high-density development, effectively using land but limiting open space, especially for children, impacting their spatial rights. This study focused on the Dongcheng and Xicheng districts of Beijing. It employed methods such as variance inflation factor, multiple linear regression, spatial autocorrelation, and spatial econometric models to investigate the impact of various configuration factors on children's satisfaction with outdoor activity space. The study also revealed the spillover effects of outdoor activity space configuration for children in high-density urban environments. The results showed that (1) children's satisfaction was significantly influenced by the configuration elements. The variables that had the most significant impact on satisfaction were the number of outdoor spaces, facilities' amusement, advertisements, and service management levels. (2) Using spatial econometric models, we determined that spatial dependency significantly enhances the model's explanatory power. The quantity of outdoor space had the greatest effect on children's outdoor activity space satisfaction, followed by facilities' amusement and advertisement impact, and service management had the least impact, though all categories positively affected satisfaction. This study held significant value and importance in improving the rights of children in mega-cities in developing countries and promoting the physical and mental well-being of children. [ABSTRACT FROM AUTHOR]

This paper assesses the National Recovery and Resilience Plans (NRRPs) of EU member states and regions to uncover commonalities and differences between green and digital transitions, focusing on the role of institutions, among additional socio‐economic drivers, in modeling them. To that end, relevant indicators have been assembled, and several econometric models have been developed and tested to evaluate institutional performance in relation to green and digital transformations. The study reveals discrepancies in the two explored transition fields and highlights the power of institutional factors in boosting them. Specifically, the findings demonstrate that the green transition in EU regions is positively associated with variables such as life expectancy, institutional quality, tertiary education attainment, and small and medium enterprises (SMEs) with innovative activities, while the fruits of digitalization are mainly allied to population with higher studies, core creative class employment, accountability of institutions, and innovative SMEs. These insights offer valuable guidance for decision‐makers to draw lessons from high‐performing or successful regions and strategically assign resources. This includes paying attention to regional financial allocations and their alignment with territorial planning and long‐term policies. [ABSTRACT FROM AUTHOR]

Copyright of Revista Latinoamericana de Estudios Urbano Regionales is the property of Pontificia Universidad Catolica de Chile 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.)

Research on small and medium-sized enterprises (SMEs) access to bank finance is vital for the euro area economy. SMEs heavily represent the European business sector, employing around 100 million people and accounting for more than half of the Gross Domestic Product. Research studies in the field often rely on the ECB/EC Survey on the Access to Finance of Enterprises (SAFE). Many studies employ probit or logit models with categorical dependent variables derived from SAFE. The research findings show that hardly any study employs the simpler linear probability model (LPM), with a dominant lack of research providing evidence that justifies the model selection process and suitability. However, it is well known that different econometrics models can lack consistency and frequently yield different results. Yet, the literature has no consensus on the best econometric approach. In addition, there is a lack of robustness tests in the literature to ensure model validity, underlining the need for a comprehensive review of the methodological framework that dominates SAFE data use. This paper addresses the identified research gap by introducing a robust methodological framework that helps researchers identify and choose an appropriate categorical model when using SAFE data. The study adds significant value to the extant literature by identifying four criteria that need to be considered when selecting the appropriate model among three common binary dependent models: LPM, probit and logit models. The findings show that the probit model was appropriate is all cases but that the LPM should not be disregarded, as it can be used in two cases: when considering the interaction between monetary policy and debt to assets and monetary policy and innovation. The use of the LPM is justified as a less complex econometric model, allowing for clearer communication of the results. This innovative, robust approach to choosing the appropriate econometric categorical dependent model when employing SAFE data contributes to support policy effectively. [ABSTRACT FROM AUTHOR]

Background: An equal distribution of hospital structures represents an important factor to achieve Universal Health Coverage. Generally, the most diffused approach to measure the potential availability to healthcare is the provider-to-population ratio based on the number of beds or professionals. However, this approach considers only the availability of resources provided at regional or local level ignoring the spatial accessibility of interregional facilities that are particularly accessed by patients living at the borders. Aim of this study is to outline the distribution of the intra and interregional services in Italy to capture the level of equity across the country. Moreover, it explores the impact of the accessibility to these resources on interregional patient's mobility to receive care. Methods: To compute spatial accessibility, we propose an alternative approach that applies the enhanced two-step floating catchment area (ESFCA) to capture the level of attraction of intra and interregional hospitals to a given population. Moreover, the adoption of process and outcome indices captured to what extent the quality of structures influenced patients in choosing services located inside or outside their region of residence. Results: The study confirms that there is an unequal distribution of high-quality resources at regional and national level with a high level of inequality in the availability and accessibility of quality resources between the north and south part of Italy. This is particularly true considering the accessibility of intraregional resources in the southern part of the country that clearly influences patient choice and contribute to a significant cross border passive mobility to northern regions. This is confirmed by an econometric model that showed a significant effect of spatial accessibility with the propensity of patients of travel from the region of residence to receive care. Conclusions: The analysis of intra and interregional components of spatial accessibility may contribute to identify to what extent patients are willing to travel outside their region of residence to access to care services. Moreover, it can contribute to gain a deeper understanding of the allocation of health resources providing input for policy makers on the basis of the principles of service accessibility in order to contain patient mobility. [ABSTRACT FROM AUTHOR]

We investigated the relationship between supply chain disruptions (SCD) and the financial performance (FP) of European automotive companies across emerging and developed nations. Addressing gaps in the literature, it offers a comprehensive, industry-specific analysis using panel data from 73 automotive firms across 21 European countries from 2013 to 2022. We examined the impact of SCD on return on assets (ROA), return on equity (ROE), and stock returns (SR), while controlling for factors such as age, leverage, size, economic growth, inflation, and unemployment. Our findings revealed that disruptions related to industrial materials and iron prices (IRAW) positively influence stock market performance, with a 99.5% increase in SR per 1% rise in IRAW, suggesting increased demand. Conversely, precious metal prices negatively affected all financial metrics, reducing ROE by 17.3% and SR by 55.5% per 1% increase. Heightened shipping costs showed varied impacts on ROA and ROE but contributed to a 12.9% average increase in SR, indicating effective cost transfer to consumers. The pandemic years significantly decreased ROA, ROE, and SR, highlighting challenges faced by the automotive sector. Rising oil prices showed no significant association, underscoring the importance of hedging strategies. The control variable outcomes emphasize the need for detailed evaluation in assessing financial performance. This study's contribution lies in its detailed analysis of specific disruptions within the automotive industry and their distinct impacts on financial performance metrics, providing a nuanced understanding that addresses significant gaps in the existing literature. [ABSTRACT FROM AUTHOR]

An econometric model was developed to analyze the contribution of various factors to the gross value added to the agricultural, manufacturing, and service sectors in the United States. The study found that variables such as rare earth element mining, the employment it generates, the domestic consumption, imports, and prices of certain elements significantly impact economic sectors. The models showed a good fit and met the necessary statistical assumptions. Rare earth elements are essential for a wide range of technological products, with China being the leading producer and consumer. This has raised concerns about the dependence on other countries. These elements significantly impact the economy's primary, secondary, and tertiary sectors used in agriculture, manufacturing, and services. Rare earth elements' mining and processing are complex and expensive processes, and demand is expected to continue to increase in the future. [ABSTRACT FROM AUTHOR]

Copyright of Canadian Journal of Economics is the property of Wiley-Blackwell 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.)

In this paper, we analyse the computational advantages of the spherical parametrisation for correlation matrices in the context of Maximum Likelihood estimation via numerical optimisation. By using the special structure of correlation matrices, it is possible to define a bijective transformation of an n × n correlation matrix R into a vector of n (n - 1) / 2 angles between 0 and π . After discussing the algebraic aspects of the problem, we provide examples of the use of the technique we propose in popular econometric models: the multivariate DCC-GARCH model, widely used in applied finance for large-scale problems, and the multivariate probit model, for which the computation of the likelihood is typically accomplished by simulated Maximum Likelihood. Our analysis reveals the conditions when the spherical parametrisation is advantageous; numerical optimisation algorithms are often more robust and efficient, especially when R is large and near-singular. [ABSTRACT FROM AUTHOR]

This study explores the effects of banking uncertainty on firms' debt financing. Employing data from 2007 to 2022 of Vietnam–a bank-based economy, we document that banking uncertainty negatively impacts corporate debt. The impact firmly holds across various debt maturities and sources, with the most predominant driver witnessed in bank debt. We also investigate the potential underlying mechanism linking banking uncertainty to debt financing, thereby validating the working of three crucial channels, including increased costs of debt, substitution of trade credit, and contractions in firm investment. Furthermore, conducting extended analysis, we find that debt financing exhibits more pronounced reactions to banking uncertainty for firms with closer ties to banks or during macroeconomic shocks, as captured by the financial crisis and the COVID-19 pandemic. Our findings survive after robustness checks by alternative measurement, static and dynamic econometric models, and endogeneity controls. [ABSTRACT FROM AUTHOR]

Household welfare is a big concern to all countries particularly in developing countries. However, if the aim of studying is not only improving the well-being of households who are currently poor, but also preventing people from becoming poor in the future, a new forward-looking perspective must be adopted. This study is applied descriptive statistics and binary logit model to investigate the impact of living in privately rented house on household poverty status (proxy to welfare) the logistic regression model has as dependent variable the poverty status (poor and non-poor). This study is based on cross section data. The result of the econometric model indicates that households living in privately rented house is significantly 3.571 times more likely currently poor than owner households; the married couples was found to be 5.228 times more likely poor compared to single household heads; income of the household negatively and household size positively and an education attainment of the household significantly negatively affect the household poverty status. [ABSTRACT FROM AUTHOR]

This study focuses on the economic opportunities that would interact with the migration patterns, based on the empirical evidence being retrieved from the data analysis. Migration is quite frequently connected with economics, as people search for better living conditions and larger job opportunities. Data and statistical analyses are used for the examination of economic variables like GDP growth, unemployment rates, income inequalities, and sectoral employment patterns that affect migration dynamics. Econometric models and regression analysis are used to explain the complex relationships between economic opportunities on the one hand and different types of migration, including internal and external migration, and on the other hand, labour mobility. The analysis also focuses on policy measures, globalisation, and technological advancements as aspects that influence migration trends. By depicting empirical evidence and case studies, this research provides the necessary depth of understanding of the complex interplay between economic forces and human mobility, which in turn offers important guidance to policymakers and stakeholders on how to handle the challenges of migration and to benefit from the opportunities it gives. [ABSTRACT FROM AUTHOR]

The rapid development of the digital economy is driving transformative changes in a multifaceted collaborative environmental governance system. From the perspective of collaborative governance between government and the public, this study employs double fixed-effects models, spatial econometric models, and instrumental variables methods to empirically explore how the digital economy influences environmental pollution, using panel data from 30 provinces in China spanning 2011 to 2022. The results demonstrate that the digital economy significantly lowers environmental pollution. The primary mechanism is through the government's environmental governance behaviors, which are positively moderated by public environmental concerns, enhancing effectiveness. Additionally, the digital economy induces a spatial spillover effect on environmental pollution. This promotion of collaborative management between the government and the public is poised to become a pivotal direction in future environmental governance. [ABSTRACT FROM AUTHOR]

Sustainable development and raising the domestic value-added rate of exports (DVARE) have become essential priorities in the pursuit of high-quality economic growth. An econometric spatial model is developed in this research using data on Chinese enterprises spanning 2008 to 2019. According to a study, exports' domestic value-added rate (DVAR) can be successfully increased using environmental, social, and governance (ESG) practices. Spatial spillover analysis demonstrates that adopting ESG practices boosts export DVAR both within and between regions. According to heterogeneity analysis, the sample's overall increase in DVARE as a result of ESG practices is mostly attributable to the mix and processing trade organizations, the eastern area, and large firms. An examination of the underlying mechanisms shows that businesses that implement advanced technologies are able to reinforce the favorable impact of ESG practices on DVARE. This article gives evidence from real-world studies that show how ESG practices help boost Chinese exports and advance sustainable development. The findings hold significant implications for other developing nations as they make the transition towards a pattern of economic growth. [ABSTRACT FROM AUTHOR]

The environmental impact of trade openness has been a subject of extensive research, but gaps exist in understanding how green financing interact with trade openness on carbon emissions in emerging economies. Thus, this research aims to investigate the moderating effect of green financing on the relationship between trade openness and carbon emissions in emerging countries. The study uses a balanced panel dataset comprising BRIC and CIVETS countries spanning 1998 to 2022 years. Employing threshold effect model, we uncover involved patterns and critical thresholds that influence the environmental outcomes of trade dynamics. Finally, this paper employs different econometric models fortifying the methodological underpinning of the study. We find that green financing and trade openness lead to a reduction in carbon emissions as they are negatively associated with emissions. Further, our study finds that green financing interacted with trade openness to reduce carbon emissions because interaction of trade openness makes stronger the relationship to reduce emissions. When these two factors interact, their combined effect is even more potent. Additionally, this study identifies a threshold effect in the role of green financing, where its inhibitory impact on carbon emissions intensifies as the level of green financing increases, lead to greater reductions in emissions. This research contributes in identifying the moderating effects of green financing and the threshold effects on carbon emissions at different levels of green financing. Thus, this article implies that increasing both green financing and trade openness, along with understanding their interactive and threshold effects, is crucial for achieving substantial carbon emissions reductions. [ABSTRACT FROM AUTHOR]

This research aims to study the relationship between energy use and economic growth between 1990 and 2021 in Colombia. Energy is a key element for economic growth and to improve people's living standards, especially in developing economies like Colombia. To estimate both the short-run and long-run relationship between economic growth and energy use and economic growth, we used data from 1990 to 2021 and we estimated an error correction model along with a regression using the FMOLS/DOLS estimators. We found that economic growth and energy use are related in the short run in Colombia, according to the Toda–Yamamoto causality test, GDP granger causes energy use and there's not enough evidence to claim that energy use and GDP are related in the long run. JEL Classification : Q43. Plain language summary: The relationship between energy use and economic growth in the short-run and long-run relationship in Colombia between 1990 and 2021 This research aims to study the relationship between energy use and economic growth between 1990 and 2021 in Colombia. Energy is a key element for economic growth and to improve people's living standards, especially in developing economies like Colombia. To estimate both the short-run and long-run relationship between economic growth and energy use and economic growth, we used data from 1990 to 2021 and we estimated two econometric models to capture both the short run and long run dynamics between energy use and economic growth. We found that economic growth and energy use are related in the short run in Colombia and the direction of the relationship goes from GDP to energy use and there's not enough evidence to claim that energy use and GDP are related in the long run in Colombia. [ABSTRACT FROM AUTHOR]

Objective: to analyze the factors that influence the academic performance of students in Colombia, considering both individual characteristics and factors related to the educational institution. Methodology: a hierarchical econometric model was used to nest the data at different levels, and the individual results of the Saber 11° tests of the Colombian Institute for the Evaluation of Education were used for both semesters of 2021. Results: indicate that the nature of the school (public or private) is a key factor in student performance, explaining almost 11% of the differences between schools, despite the fact that students attending public schools have lower socioeconomic, cultural and social conditions than those attending private schools, there is still a significant gap in academic performance that cannot be fully explained. Conclusion: private schools were found to have more variability in their effect on student performance, indicating that the unique characteristics of private schools, such as admission fees and incentives granted to teachers, may influence student academic performance more randomly. [ABSTRACT FROM AUTHOR]

This study aims to analyze the influence of ESG controversies on the value of companies. Annual data from 6,325 companies from 61 countries between 2002 and 2020 were analyzed. In the econometric model developed, the indices prepared by Refinitiv®, made available through the Eikon platform, were used as ESG controversies, in addition to the three variables as proxies of company value: Tobin’s Q, Market-to-Book, and Market Capitalization. The results indicate a negative relationship between ESG controversies, and the value of companies measured by Tobin’s Q and Market Capitalization proxies. Considering that the value of a firm represents the estimation of returns, our findings corroborate the view that controversies produce negative effects on the evaluation of future results. It contributes to the literature by demonstrating that ESG controversies affect companies based on global analysis and a deepening understanding of the impacts of corporate irregularities. [ABSTRACT FROM AUTHOR]

Barley is one of Ethiopia's most important cereal crops, ranking fifth in total cereal production, after maize, wheat, teff, and sorghum. Based on its intended use, it is divided into two types: food barley and malt barley. This study investigated the factors that affect farmers' decisions to adopt malt barley technology. The research was conducted in eight major malt barley‐growing districts in the central highlands of Ethiopia. Data were collected from both primary and secondary sources. A structured questionnaire was used to obtain quantitative data from 400 sample farmers. Key informant interviews and focus group discussions were conducted to triangulate and substantiate the quantitative data. Secondary data were also used to supplement the primary data. The data were analyzed using descriptive statistics and econometric models. A logistic regression model was employed to analyze quantitative data. The findings revealed that educational level of the household head, family size of the household, access to input, experience, and access to demanded variety all have a positive and significant impact on malt barley technology adoption. However, the age of the household head, income from off‐farm activities, and distance to the market have a negative and significant impact on farmers' decisions to use malt barley technology. Up to 2021, about 30 malt barley varieties were released or registered by the Ministry of Agriculture for production nationwide, while only six to seven varieties were adopted by the sampled farmer households. As a result, we concluded that strong government support and clear policy direction are required to encourage farmers and other stakeholders to invest more to enhance adoption of improved varieties across malt barley growing areas. [ABSTRACT FROM AUTHOR]

Copyright of Revista de Ciencias Sociales (13159518) is the property of Revista de Ciencias Sociales de la Universidad del Zulia Venezuela 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.)

To support the 2021 Manufacturing & Service Operations Management (M&SOM) Data-Driven Research Challenge, RiRiShun Logistics (a Haier group subsidiary focusing on logistics service for home appliances) provides MSOM members with logistics operational-level data for data-driven research. This paper provides a detailed description of the data associated with over 14 million orders from 149 clients (the consigners) associated with 4.2 million end consumers (the recipients and end users of the appliances) in China, involving 18,000 stock keeping units operated at 103 warehouses. Researchers are welcomed to develop econometric models, data-driven optimization techniques, analytical models, and algorithm designs by using this data set to address questions suggested by company managers. History: This paper has been accepted as part of the 2021 M&SOM Data-Driven Research Challenge. Funding: This work was supported by the National Natural Science Foundation of China [Grants 72091215/72091210, 71871207, 71991464/71991460, and 71921001]. Supplemental Material: Data and the online appendix are is available at https://doi.org/10.1287/msom.2021.0994. [ABSTRACT FROM AUTHOR]

This study challenges the prevailing belief in the necessity of complex models for accurate forecasting by demonstrating the effectiveness of parsimonious econometric models, namely ARCH(1) and GARCH(1,1), over deep learning robust approaches, such as LSTM and 1D-CNN neural networks, in modeling historical volatility within pre-emerging stock markets, specifically the Moroccan and Bahraini stock markets. The findings suggest reevaluating the balance between model complexity and predictive accuracy. Future research directions include investigating the potential existence of threshold effects in market capitalization for optimal model performance. This research contributes to a deeper understanding of volatility dynamics and enhances forecasting models' effectiveness in diverse market conditions. [ABSTRACT FROM AUTHOR]

This research aims to analyse the financial performance of companies using one of the most used profitability indicators, the return on investment (ROI), which measures the company's performance in terms of the profit generated over time. To this end, several different methods are used to calculate the ROI indicator, considering the different calculation methods used by different authors over the years. The use of different ROI calculation formulas has been identified in the literature, leading to different conclusions. Based on a sample of 2805 Portuguese companies, it examines how the different indicators react to the different variables analysed, using nine different econometric models. Through this study, it is possible to verify that the different variables that depend on the return on investment have different results, namely that the variables "age" and "size" have a negative effect on the return on investment. On the other hand, "financial leverage" and "ROA" have a positive impact on the contribution to the return on investment. We also found that the different variables behave similarly for virtually all types of ROI calculation, although not completely harmonious, especially in terms of impact. The results are empirically vital, as they alert researchers and companies to the need for standardised formulas for calculating variables such as ROI so that results are not distorted. Using one to the detriment of the other impacts the results obtained and the analyses to be carried out. How empirical research will continue to use the ROI metric will always depend on its users' discretion and free will. [ABSTRACT FROM AUTHOR]

Copyright of Mexican Journal of Economics & Finance / Revista Mexicana de Economia y Finanzas is the property of Instituto Mexicano de Ejecutivos de Finanzas 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.)