Hasil untuk "Economics"

B. Nooteboom, Helge Berger, N. Noorderhaven

W. J. Baumol, W. Oates

W. Schulze, M. Lubatkin, Richard N. Dino

M. Fourcade, Étienne Ollion, Y. Algan

Luca Attolico

Macroeconomic nowcasting sits at the intersection of traditional econometrics, data-rich information systems, and AI applications in business, economics, and policy. Machine learning (ML) methods are increasingly used to nowcast quarterly GDP growth, but adoption in high-stakes settings requires that predictive accuracy be matched by interpretability and robust uncertainty quantification. This article reviews recent developments in macroeconomic nowcasting and compares econometric benchmarks with ML approaches in data-rich and shock-prone environments, emphasizing the use of nowcasts as decision inputs rather than as mere error-minimization exercises. The discussion is organized along three axes. First, we contrast penalized regressions, dimension-reduction techniques, tree ensembles, and neural networks with autoregressive models, Dynamic Factor Models, and Random Walks, emphasizing how each family handles small samples, collinearity, mixed frequencies, and regime shifts. Second, we examine explainability tools (intrinsic measures and model-agnostic XAI methods), focusing on temporal stability, sign coherence, and their ability to sustain credible economic narratives and nowcast revisions. Third, we analyze non-parametric uncertainty quantification via block bootstrapping for predictive intervals and confidence bands on feature importance under serial dependence and ragged edge. We translate these elements into a reference workflow for "decision-grade" nowcasting systems, including vintage management, time-aware validation, and automated reliability audits, and we outline a research agenda on regime-dependent model comparison, bootstrap design for latent components, and temporal stability of explanations. Explainable ML and uncertainty quantification emerge as structural components of a responsible forecasting pipeline, not optional refinements.

Mingzhi Xiao, Yuki Takayama

As high-speed rail (HSR) investment accelerates across China, the question of whether such large-scale infrastructure can promote balanced regional development or exacerbate spatial inequality has become central for policymakers and scholars. This study provides systematic micro-level evidence by analyzing a balanced panel of 353 county-level divisions, including urban districts, county-level cities, and counties, along the Shanghai-Kunming and Xuzhou-Lanzhou HSR corridors from 2008 to 2019. Using a multi-period difference-in-differences (DID) approach, supported by event study and propensity score matching, we quantify the heterogeneous impacts of HSR openings across administrative types and regions, with special attention to the presence of direct HSR station access. The results show that HSR expansion significantly increases secondary and tertiary sector output in urban districts (by 2.77 and 8.71 hundred million RMB) and in county-level cities, particularly in the eastern region. In contrast, counties without HSR stations or with weaker economic foundations experience much smaller gains. Some counties also see a notable contraction in the service sector, which is closely linked to substantial population outflows. Robustness checks confirm the causal interpretation. These findings challenge the prevailing view that HSR fosters uniform growth. Instead, the results reveal that infrastructure-led development can intensify spatial and administrative disparities at the county level. The study underscores the need for integrated and locally tailored policy interventions to ensure that HSR investments contribute to inclusive and sustainable regional development.

Zongrong Li, Haiyang Li, Yifan Yang et al.

Wildfires in urbanized regions, particularly within the wildland-urban interface, have significantly intensified in frequency and severity, driven by rapid urban expansion and climate change. This study aims to provide a comprehensive, fine-grained evaluation of the recent 2025 Los Angeles wildfire's impacts, through a multi-source, tri-environmental framework in the social, built and natural environmental dimensions. This study employed a spatiotemporal wildfire impact assessment method based on daily satellite fire detections from the Visible Infrared Imaging Radiometer Suite (VIIRS), infrastructure data from OpenStreetMap, and high-resolution dasymetric population modeling to capture the dynamic progression of wildfire events in two distinct Los Angeles County regions, Eaton and Palisades, which occurred in January 2025. The modelling result estimated that the total direct economic losses reached approximately 4.86 billion USD with the highest single-day losses recorded on January 8 in both districts. Population exposure reached a daily maximum of 4,342 residents in Eaton and 3,926 residents in Palisades. Our modelling results highlight early, severe ecological and infrastructural damage in Palisades, as well as delayed, intense social and economic disruptions in Eaton. This tri-environmental framework underscores the necessity for tailored, equitable wildfire management strategies, enabling more effective emergency responses, targeted urban planning, and community resilience enhancement. Our study contributes a highly replicable tri-environmental framework for evaluating the natural, built and social environmental costs of natural disasters, which can be applied to future risk profiling, hazard mitigation, and environmental management in the era of climate change.

Pablo Hidalgo, Julio E. Sandubete, Agustín García-García

This study investigates the contribution of Intrinsic Mode Functions (IMFs) derived from economic time series to the predictive performance of neural network models, specifically Multilayer Perceptrons (MLP) and Long Short-Term Memory (LSTM) networks. To enhance interpretability, DeepSHAP is applied, which estimates the marginal contribution of each IMF while keeping the rest of the series intact. Results show that the last IMFs, representing long-term trends, are generally the most influential according to DeepSHAP, whereas high-frequency IMFs contribute less and may even introduce noise, as evidenced by improved metrics upon their removal. Differences between MLP and LSTM highlight the effect of model architecture on feature relevance distribution, with LSTM allocating importance more evenly across IMFs.

Auyona Ray

This project began by constructing an index of economic insecurity using multiple socioeconomic indicators. Although poverty alone predicted SNAP participation more accurately than the composite index, its explanatory power was weaker than anticipated, echoing past findings that enrollment cannot be explained by income alone. This led to a shift in focus: identifying ZIP codes with high poverty but unexpectedly low SNAP participation, areas defined here as having a SNAP Gap, where ZIPs fall in the top 30 percent of family poverty and the bottom 10 percent of SNAP enrollment. Using nationally available ZIP level data from 2014 to 2023, I trained logistic classification models on four interpretable structural indicators: lack of vehicle, lack of internet access, lack of computer access, and percentage of adults with only a high school diploma. The most effective model relies on just two predictors, vehicle access and education, and outperforms tree based classifiers in both precision and calibration. Results show that economic insecurity is consistently concentrated in rural ZIP codes, with transportation access emerging as the most stable barrier to program take up. This study provides a nationwide diagnostic framework that can inform the development of scalable screening tools for targeting outreach and improving benefit access in underserved communities.

Adam Smerigan, Rui Shi

Transitioning to green energy technologies requires more sustainable and secure rare earth elements (REE) production. The current production of rare earth oxides (REOs) is completed by an energy and chemically intensive process from the mining of REE ores. Investigations into a more sustainable supply of REEs from secondary sources, such as toxic phosphogypsum (PG) waste, is vital to securing the REE supply chain. However, conventional solvent extraction to recover dilute REEs from PG waste is inefficient and has high environmental impact. In this work, we propose a treatment train for the recovery of REEs from PG which includes a bio-inspired adsorptive separation to generate a stream of pure REEs, and we assess its financial viability and environmental impacts under uncertainties through a "probabilistic sustainability" framework integrating life cycle assessment (LCA) and techno-economic analysis (TEA). Results show that in 87% of baseline scenario simulations, the internal rate of return (IRR) exceeded 15%, indicating that this system has the potential to be profitable. However, environmental impacts of the system are mixed. Specifically, the proposed system outperforms conventional systems in ecosystem quality and resource depletion, but has higher human health impacts. Scenario analysis shows that the system is profitable at capacities larger than 100,000 kg*hr-1*PG for PG with REE content above 0.5 wt%. The most dilute PG sources (0.02-0.1 wt% REE) are inaccessible using the current process scheme (limited by the cost of acid and subsequent neutralization) requiring further examination of new process schemes and improvements in technological performance. Overall, this study evaluates the sustainability of a first-of-its-kind REE recovery process from PG and uses these results to provide clear direction for advancing sustainable REE recovery from secondary sources.

Sarun Kamolthip

This paper examines the effects of daily temperature fluctuations on subnational economic growth in Thailand. Using annual gross provincial product (GPP) per capita data from 1982 to 2022 and high-resolution reanalysis weather data, I estimate fixed-effects panel regressions that isolate plausibly exogenous within-province year-to-year variation in temperature. The results indicate a statistically significant inverted-U relationship between temperature and annual growth in GPP per capita, with adverse effects concentrated in the agricultural sector. Industrial and service outputs appear insensitive to short-term weather variation. Distributed lag models suggest that temperature shocks have persistent effects on growth trajectories, particularly in lower-income provinces with higher average temperatures. I combine these estimates with climate projections under RCP4.5 and RCP8.5 emission scenarios to evaluate province-level economic impacts through 2090. Without adjustments for biases in climate projections or lagged temperature effects, climate change is projected to reduce per capita output for 63-86% of Thai population, with median GDP per capita impacts ranging from -4% to +56% for RCP4.5 and from -52% to -15% for RCP8.5. When correcting for projected warming biases - but omitting lagged dynamics - median losses increase to 57-63% (RCP4.5) and 80-86% (RCP8.5). Accounting for delayed temperature effects further raises the upper-bound estimates to near-total loss. These results highlight the importance of accounting for model uncertainty and temperature dynamics in subnational climate impact assessments. All projections should be interpreted with appropriate caution.

Bahram Adrangi, Saman Hatamerad, Madhuparna Kolay et al.

The objective of this study is to analyze the response of firm value, represented by the Tobin's Q (Q) for a group of twelve U.S. durable goods producers to uncertainties in the US Economy. The results, based on an estimated panel quantile regressions (PQR) and panel vector autoregressive MIDAS model (PVM), show that Q for these firms reacts negatively to the positive shocks to the current ratio, and debt-to-asset ratio and positively to operating income after depreciation and the quick ratio in most quantiles. The Q of the firms under study reacts negatively to the economic policy uncertainty, risk of recession, and inflationary expectation, but positively to consumer confidence in most quantiles of its distribution. Finally, Granger causality tests confirm that the uncertainty indicators considered in the study are significant predictors of changes in the value of these companies as reflected by Q.

Ana-Maria Coatu, Felix-Angel Popescu, Laurențiu Petrila

This study explores how socio-economic factors affect the effectiveness of public accountability frameworks in EU member states, with Romania as a case study. Using data from the World Bank, Eurobarometer, and cross-country comparisons, it identifies five key determinants: income inequality, education, healthcare access, political participation, and economic stability. Grounded in institutional theory, the research shows that inclusive institutions and lower disparities lead to stronger accountability, while weaker frameworks often reinforce inequality and corruption. For Romania, the study recommends boosting transparency, enforcing anti-corruption measures, improving rural-urban equity, and enhancing civic education to strengthen the link between citizens and institutions.

Gholamreza Soleimani Amiri, Neda Pourgholamreza

Objective: “The purpose of this study is to investigate the effect of auditor industry specialization on audit fees and audit report lag. In addition, this study examines the effect of audit report lag on the relationship between auditor industry specialization and audit fees”. Method: “In this research, the data of 132 companies admitted to the Tehran Stock Exchange during the period from 2014 to 2023 were used. Also, in this research, Standard Audit Fee Model and multivariate linear regression with fixed effects has been used”.Results: “The results showed that the auditor industry specialization does not affect the audit fee. However, the auditor industry specialization has a significant effect on the audit fees by mediating the audit report lag. Also, the results have shown a significant negative effect of the auditor's specialization in the industry on the audit report lag”.Conclusions: “In general, this research shows that companies that contract with audit firms with specialization in the industry pay less due to the expertise of the audit firm and the timeliness and brevity of their audit reports”.

Eveline Sarintohe, William J. Burk, Jacqueline M. Vink et al.

Introduction: Little is known about how the COVID-19 situation affected weight development among Indonesian adolescents. This longitudinal study examined whether, and for whom, the COVID-19 situation affected weight outcomes over time among adolescents from private schools and higher socio-economic positions in Indonesia, where being overweight is a rather prevalent characteristic. This study specifically examined whether appetitive traits (i.e., emotional overeating, food responsiveness) as well as baseline zBMI, sex, and urban area could explain changes in zBMI. Methods: At baseline, 411 adolescents from 5 private schools in Indonesia (53.3% males, Mage = 12.02 years, SD = 0.45) filled out questionnaires on appetitive traits and background characteristics. In addition, their height and weight were measured. Of these, 336 adolescents (81.8%) also participated at follow-up. At follow-up, height and weight were measured or reported. We used linear regression to analyze the association between predictors and interactions with zBMI. Results: The results showed a significant decrease in zBMI over time, with a lower average zBMI during COVID-19 compared to before COVID-19. Female adolescents and adolescents with higher baseline zBMI values particularly tended to show this zBMI decreasing pattern. We did not find statistically significant main effects of baseline emotional overeating, food responsiveness, and urban area or any interactions. Conclusions: Indonesian adolescents appeared to decrease in terms of zBMI during COVID-19, particularly females and adolescents with higher pre-COVID-19 zBMI. Our findings suggest that (culturally-specific) contextual changes (i.e., less exposure to the Indonesian food environment at schools and more exposure to the home environment) might have a beneficial impact in terms of preventing overweight among Indonesian adolescents, particularly among those being more vulnerable (i.e., having higher baseline zBMI).

Alberto Pliego Marugán, Fausto Pedro García Márquez, Jesús María Pinar Pérez

Currently, wind energy is one of the most important sources of renewable energy. Offshore locations for wind turbines are increasingly exploited because of their numerous advantages. However, offshore wind farms require high investment in maintenance service. Due to its complexity and special requirements, maintenance service is usually outsourced by wind farm owners. In this paper, we propose a novel approach to determine, quantify, and reduce the possible conflicts of interest between owners and maintenance suppliers. We created a complete techno-economic model to address this problem from an impartial point of view. An iterative process was developed to obtain statistical results that can help stakeholders negotiate the terms of the contract, in which the availability of the wind farm is the reference parameter by which to determine penalisations and incentives. Moreover, a multi-objective programming problem was addressed that maximises the profits of both parties without losing the alignment of their interests. The main scientific contribution of this paper is the maintenance analysis of offshore wind farms from two perspectives: that of the owner and the maintenance supplier. This analysis evaluates the conflicts of interest of both parties. In addition, we demonstrate that proper adjustment of some parameters, such as penalisation, incentives, and resources, and adequate control of availability can help reduce this conflict of interests.

Andreas Petrou-Zeniou, Azeem M. Shaikh

This paper considers the problem of inference after ranking. In our setting, we are interested in any population whose rank according to some random quantity, such as an estimated treatment effect, a measure of value-added, or benefit (net of cost), falls in a pre-specified range of values. As such, this framework generalizes the inference on winners setting previously considered in Andrews et al. (2023), in which a winner is understood to be the single population whose rank according to some random quantity is highest. We show that this richer setting accommodates a broad variety of empirically-relevant applications. We develop a two-step method for inference, which we compare to existing methods or their natural generalizations to this setting. We first show the finite-sample validity of this method in a normal location model and then develop asymptotic counterparts to these results by proving uniform validity over a large class of distributions satisfying a weak uniform integrability condition. Importantly, our results permit degeneracy in the covariance matrix of the limiting distribution, which arises naturally in many applications. In an application to the literature on economic mobility, we find that it is difficult to distinguish between high and low-mobility census tracts when correcting for selection. Finally, we demonstrate the practical relevance of our theoretical results through an extensive set of simulations.

Walter Yodah , Chrisphine Ouma , Graca Machel et al.

This study tries to determine the impact of crude oil price fluctuations on the Consumer Price Index, CPI in Kenya, using the monthly data from 2012M1-2023M3. The Augmented Dickey-Fuller unit root test used in the analysis to test for stationarity for the series indicated that CPI and significant types of Crude oil (Kerosene, Diesel, and Super petrol) were non-stationary. The empirical analysis is carried out using cointegration by applying Johansen’s multivariate approach, which reveals that not more than one cointegrating relationship holds between oil prices and CPI in Kenya. The analysis using VECM further shows that the series deviation from equilibrium to disequilibrium cannot be corrected back to equilibrium in the long run. The impulse response functions reveal that oil price shocks positively affect CPI in Kenya. As expected, the forecasted values continue to exhibit an increasing trend in the central oil type prices and CPI values in Kenya. The Jacque-Bera test for normality, ARCH-LM test for Homoscedasticity, and Portmanteau test for serial correlation were used to test for the adequacy of the Model.

Amalia Rani Setyawati, Gemala Anjani, Endang Mahati

Background: Metabolic syndrome is a significant risk factor for both type 2 diabetes mellitus and cardiovascular disease, with a high prevalence in Asia Pacific, particularly in Indonesia. To reduce its prevalence, several studies have recommended the use of tropical nuts, which can be developed as functional foods and complementary treatment. In this context, the bioactivities of tropical nuts can largely be attributed to their rich content of monounsaturated fatty acids, polyunsaturated fatty acids, fiber, minerals, vitamins, phytosterols, and polyphenols. Objectives: This literature review aims to evaluate the potential benefits and mechanism of action of tropical nuts against metabolic syndrome. Methods: The study design was a literature review of several articles from 3 online databases, including PubMed, Google Scholar, and ScienceDirect. Discussions: The results showed that tropical nuts (peanut, sacha inchi, cashew, tropical almond, and Brazil nut) had several biologically active components, such as arginine, fiber, fatty acid, mineral, vitamin, phenolic compounds, resveratrol, and phytosterol. The test samples were reported to have the ability to modulate Nrf2, SOD, MDA, GSH, GPx, and CAT due to their antioxidant activity. In inflammation, tropical nuts had a significant effect on NF-κB, NLRP3, TNF-ɑ, IL-8, IL-1ꞵ, IL-6, and IL-10. The results also showed their ability to enhance lipid synthesis, nitric oxide production, advanced glycation end-product, prostaglandin, SIRT3, homocysteine, protein kinase C, adhesion molecules, platelet aggregation, GLP-1, PYY, AGRP, PPARɑ/ꞵ/δ, GLUT4, and insulin receptor. Conclusions: Tropical nuts had beneficial effects on metabolic syndrome due to their bioactivities, including antioxidants, anti-inflammatory, anti-obesity, antidiabetic, antihypertensive, anti-dyslipidemia, and cardioprotective.

Mahbub Ul Islam Khan, Md. Ilius Hasan Pathan, Mohammad Mominur Rahman et al.

Electric vehicles (EVs) are commonly recognized as environmentally friendly modes of transportation. They function by converting electrical energy into mechanical energy using different types of motors, which aligns with the sustainable principles embraced by smart cities. The motors of EVs store and consume electrical power from renewable energy (RE) sources through interfacing connections using power electronics technology to provide mechanical power through rotation. The reliable operation of an EV mainly relies on the condition of interfacing connections in the EV, particularly the connection between the 3-<inline-formula> <tex-math notation="LaTeX">$\phi $ </tex-math></inline-formula> inverter output and the brushless DC (BLDC) motor. In this paper, machine learning (ML) tools are deployed for detecting and classifying the faults in the connecting lines from 3-<inline-formula> <tex-math notation="LaTeX">$\phi $ </tex-math></inline-formula> inverter output to the BLDC motor during operational mode in the EV platform, considering double-line and three-phase faults. Several machine learning-based fault identification and classification tools, namely the Decision Tree, Logistic Regression, Stochastic Gradient Descent, AdaBoost, XGBoost, K-Nearest Neighbour, and Voting Classifier, were tuned for identifying and categorizing faults to ensure robustness and reliability. The ML classifications were developed based on the datasets of healthy and faulty conditions considering the combination of six critical parameters that have significance in reliable EV operation, namely the current supplied to the BLDC motor from the inverter, the modulated DC voltage, output speed, and measured speed, as well as the output of the Hall-effect sensor. In addition, the superiority of the proposed fault detection and classification approaches using ML tools was assessed by comparing the detection and classification efficiency through some statistical performance parameter comparisons among the classifiers.