Empirical Application Research Articles

Mitigating greenhouse gas emissions from the cattle sector: Land‐use regulation as an alternative to emissions pricing

AbstractReducing animal‐based food production would not only reduce agricultural greenhouse gas emissions but also free land that could sequester carbon. We examine the efficiency of a subsidy to cattle farmers for setting aside land for natural ecosystem regeneration. We develop a partial equilibrium model of the cattle sector that integrates land use, greenhouse gas emissions, and animal feeding. We compare the subsidy to alternative policies: a meat tax and a standard on animal feeding. We identify the conditions under which the subsidy is the best alternative to these other second‐best policies. The efficiency of the subsidy lies in its effects on both the extensive margin (reduced quantity of meat) and the intensive margin (production intensification, which reduces both the emission and land‐use intensities of meat). An empirical application to France, where spontaneous regeneration corresponds mostly to forest regrowth, shows that the subsidy dominates the other alternative policies considered for a wide range of parameter values but is sensitive to carbon leakage when the economy is open to trade.

Robust estimation method for panel interval-valued data model with fixed effects

Panel data model with fixed effects is widely used in economic and administrative applications. However, the presence of factors: measurement errors, data variability and outliers may potentially decrease the accuracy of the model prediction. In this paper, we use panel interval-valued data to represent measurement errors and data volatility of observations. Further, we propose a corresponding panel interval-valued data model with fixed effects, in which both the response and explanatory variables are interval-valued data. To reduce the impact of outliers on our model, we propose a robust estimation method based on the iterative weighted least squares technique. Later, Monte Carlo simulation and empirical application demonstrate that our model is a suitable tool for analyzing the behaviour of panel interval-valued data.

Breaking through the "Tower of Babel": Exploratory Research on the International Communication Practice of Artificial Intelligence Generating News

With the deepening influence of artificial intelligence on the production mode of news and the increasing application of technological innovation in cross-border communication, this study found that artificial intelligence can solve the communication dilemma of language in cross-cultural communication revealed in the myth of the Tower of Babel through language translation and empathetic communication strategies. The literature review section introduces the basic concepts of cross-cultural empathy communication and the practical application of artificial intelligence-generated news in international communication. Multiple case studies were used in the research method, based on the specific practices of major media outlets including Xinhua News Agency, People's Daily, China Central Television, Associated Press, New York Times, and CBS in the application of intelligent technology. The research results indicate that the application of intelligent technology in the news production process has significantly improved work efficiency and promoted innovation in news production models at three levels: technical logic, media logic, and capital logic. The conclusion drawn from this article is that the deep involvement of generative artificial intelligence technology in the news production process can help build a new production mechanism that balances efficiency and responsibility and promotes the comprehensive improvement of efficiency and quality in cross-cultural communication. Future research will continue to focus on the empirical application of intelligent technology in news production, further optimizing news production models, and providing new perspectives and methods for developing the media industry.

Directed likelihood statistic to test the concentration parameter in von Mises regressions

We derive explicit expressions for the directed likelihood statistic and its modified version for testing several hypotheses on the concentration parameter in the von Mises regression model. We verify that the standard normal distribution gives a poor approximation to the true distribution of the usual directed likelihood statistic to test the concentration parameter, while its modified version leads to very accurate inference even for very small samples. An empirical application is considered for illustrative purposes.

Research on the construction and application of a smart library services maturity evaluation system based on CMM

PurposeSmart libraries are the result of the application of smart technologies in the era of digital intelligence. The establishment and improvement of its service evaluation system serve as indicators for evaluating the growth of smart libraries.Design/methodology/approachThis study introduces and improves the capability maturity model (CMM), creatively constructs a service maturity model specifically designed for smart libraries and combines the Delphi method with the analytic hierarchy process (AHP) to establish a service maturity evaluation system for smart libraries while calculating indicator weights. Finally, two representative smart libraries are selected as case studies, and an empirical application is conducted using the fuzzy comprehensive evaluation method.FindingsThe empirical study shows that the developed smart libraries service maturity evaluation system holds significant theoretical and practical value in evaluating smart libraries.Originality/valueEnhances the CMM and creatively constructs a service maturity model for smart libraries. Combines the Delphi method with AHP to establish a service maturity evaluation system while calculating indicator weights. Uses a fuzzy comprehensive evaluation method to evaluate two representative smart libraries. Demonstrates that the smart library services maturity evaluation system holds significant theoretical and practical value.

Classification of Sustainable Innovations: Dimensions, Scope and Variations - Multi-Case Study in Civil Construction in The State of Paraíba

Objective: Classify sustainable innovations in the construction industry in the State of Paraíba. Theoretical Framework: Sustainable innovation, its dimensions (product, process, organizational), scope (radical/incremental, proactive/reactive) and variations (integrated, complementary, technological - corrective/preventive, pollution control, cleaner production, environmental technology and environmental - and social - endogenous/exogenous R&D). Method: Descriptive, exploratory study. Conducted in the form of a multiple case study. Data collected through semi-structured interviews, systematic observation and documents analyzed using the content analysis technique. Results and Conclusions: in the product dimension, the innovations were, for the most part, incremental and proactive in scope. And the variation fell into environmental and complementary technology. In the process dimension, they presented incremental and proactive coverage. As for the variation, they presented technological characteristics of cleaner production. In the organizational dimension, they presented radical, incremental, proactive and reactive coverage. Only in Company C did they present characteristics of complementary and endogenous social variation. It can be concluded that the companies investigated are aligned with the sustainable development paradigm, but with incremental predominance. Implications of the Research: The research reinforces the concept of sustainable innovation and its dimensions, variations and scope. Furthermore, it classifies innovations based on several previous researches, compiling them and demonstrating their applications in the practice of construction companies. Originality: The proposed classification of sustainable innovations in the way it was done in this research is considered original, which, in addition to reinforcing previous theoretical studies, makes an empirical application and demonstrates in practice how the theory of sustainable innovation can be verified.

Functional Volatility Relationship Analysis and Prediction in International Crude Oil Futures Markets

ABSTRACTTo measure intraday volatility in international crude oil futures markets, we use the functional conditional variance to measure volatility and focus on volatility relationship analysis and prediction. This paper analyzes the simultaneous and predictive volatility relationships in crude oil futures markets. For covariate markets with significantly positive predictive volatility relationships, this paper empirically extends the fGARCH‐X model so that it can introduce the volatility characteristics of covariate markets. The empirical application shows that using the fGARCH‐X model can generally improve the predictive effects of functional volatility in crude oil futures markets. The robustness results indicate that the improvement in volatility prediction is significant. This study is beneficial for the stable development of international crude oil futures markets and is valuable for investors' investment decision‐making.

Causal Machine Learning in Marketing

This study reviews three primary purposes of causal machine learning (CML) in marketing, merging impact evaluation of marketing interventions with machine learning algorithms for learning statistical patterns from data. Firstly, CML enables more credible impact evaluation by considering important control variables that simultaneously influence the intervention and business outcomes (such as sales) in a data-driven manner. Secondly, it facilitates the data-driven detection of customer segments for which a marketing intervention is particularly effective or ineffective, a process known as effect moderation or heterogeneity analysis. Thirdly, closely related to the second point, it allows for optimal customer segmentation into groups that should and should not be targeted by the intervention to maximize overall effectiveness. The discussion is grounded in recent empirical applications, all of which aim to enhance decision support in marketing by leveraging data-driven evaluation and optimization of interventions across different customer groups.

Absolute Poverty Measurement with Minimum Food Needs: A New Inverse Method for Advanced Economies

Spurred by the need for increased adequacy and international comparability, absolute poverty measures are gaining in policy relevance around the world. This paper proposes a simple and feasible method of calculating absolute poverty thresholds on the basis of households’ minimum food needs in advanced economies. It makes three important contributions. First, it demonstrates that conventional statistical methods used in the developing world deliver either unrealistically low or improbably high poverty estimates in rich countries where household spending on food is modest in relative terms. Second, it proposes a new simulation-based inverse method that focuses on the non-food Engel curve and uses predetermined minimum food bundles not as inputs but as targeted reference points in order to calculate adequate poverty thresholds. Finally, an empirical application of the new method using household budget survey data from Italy shows that resulting poverty estimates are comparable to the official figures of the Italian Statistical Office in terms of both the poverty rate and the poverty profiles. Requiring few and easily procurable resources, the proposed algorithm is well suited to produce robust, consistent and internationally comparable absolute poverty estimates in a large number of developed countries and emerging economies.

SVR-based method for fixed effects interval-valued panel models

. This article studies a class of fixed effects interval-valued panel models in which the response variables are intervals and the explanatory variables are single data. We propose an SVR-based estimation method to estimate unknown parameters and smoothing functions. The proposed method utilizes the kernel trick to map the original data (also called input data) to a high-dimensional feature space, which is more likely to achieve linearity than in the original space. Moreover, the proposed method is resistant to the influence of outliers. Monte Carlo experiments validate that the proposed models have good fitting accuracy and prediction performance, while the empirical application shows that the proposed models work efficiently on finite samples.

Functional distribution, personal income inequality, and top shares of income: do social classes still matter?

AbstractThis paper aims at providing new evidence about the link between personal and functional distribution and top-shares composition. We apply a novel class scheme based on two key features of contemporary capitalism i.e., individuals/households receiving multiple types of incomes, and the role of managers. The empirical application in Germany, Spain, and Italy over the period 2000–2017 reveals two main results. First, we observe a direct link between personal and functional distributions. A marginal increase in wages received by labourers would reduce inequality, whereas those received by capitalist households would increase it. Second, we find that a significant portion of labour income at the top of the income distribution corresponds to wages received by capitalist households. We conclude that although the linear correspondence between income source and class location is more blurred today than it was 200 years ago, a class divide is still clear and more prominent than what is often indicated in the literature.

A Canonical Representation of Block Matrices with Applications to Covariance and Correlation Matrices

Abstract We obtain a canonical representation for block matrices. The representation facilitates simple computation of the determinant, the matrix inverse, and other powers of a block matrix, as well as the matrix logarithm and the matrix exponential. These results are particularly useful for block covariance and block correlation matrices, where evaluation of the Gaussian log-likelihood and estimation are greatly simplified. We illustrate this with an empirical application using a large panel of daily asset returns. Moreover, the representation paves new ways to model and regularize large covariance/correlation matrices, test block structures in matrices, and estimate regressions with many variables.

Entry cost, fixed cost and markup: Theory and evidence from the impact of corruption on prices

The welfare costs of markups are large. We analyze the impact of firm’s entry cost, fixed cost, and variable cost on markups using a general equilibrium model. The results show that entry cost induces higher markups, whereas fixed cost reduces markups with a stronger effect in low-income countries and for high-quality goods. As an empirical application, we examine the impact of corruption on the prices of identical products across countries. We find that the prices and markups are higher in more corrupt countries, and the effect is stronger in low-income countries and for high-quality products. These results suggest that corruption causes higher entry costs and fixed costs, leading to a fewer number of firms and more misallocations among firms in equilibrium. Therefore, the model is useful for distinguishing entry and fixed costs empirically, and it highlights the importance of identifying the nature of the cost to better understand their impact on markups and welfare.

Exploring Phylogenetic Signal in Multivariate Phenotypes by Maximizing Blomberg's K.

Due to the hierarchical structure of the tree of life, closely related species often resemble each other more than distantly related species; a pattern termed phylogenetic signal. Numerous univariate statistics have been proposed as measures of phylogenetic signal for single phenotypic traits, but the study of phylogenetic signal for multivariate data, as is common in modern biology, remains challenging. Here we introduce a new method to explore phylogenetic signal in multivariate phenotypes. Our approach decomposes the data into linear combinations with maximal (or minimal) phylogenetic signal, as measured by Blomberg's K. The loading vectors of these phylogenetic components or K-components can be biologically interpreted, and scatterplots of the scores can be used as a low-dimensional ordination of the data that maximally (or minimally) preserves phylogenetic signal. We present algebraic and statistical properties, along with two new summary statistics, KA and KG, of phylogenetic signal in multivariate data. Simulation studies showed that KA and KG have higher statistical power than the previously suggested statistic Kmult, especially if phylogenetic signal is low or concentrated in a few trait dimensions. In two empirical applications to vertebrate cranial shape (crocodyliforms and papionins), we found statistically significant phylogenetic signal concentrated in a few trait dimensions. The finding that phylogenetic signal can be highly variable across the dimensions of multivariate phenotypes has important implications for current maximum likelihood approaches to phylogenetic signal in multivariate data.

On the forecasting performance of small-scale DGSE models: a Monte Carlo evaluation and an application to UK

This article investigates the forecasting performance of a new small-scale dynamic stochastic general equilibrium (DSGE) model. To this end, this article first conducts a Monte Carlo study for the evaluation of the new model against another DSGE model , a standard vector autoregression (VAR), and a Bayesian VAR, using root mean square error and directional accuracy measures. An empirical application to UK quarterly data of output gap, inflation, and interest rates over the period 1986–2019 is also carried out for point and density forecasts. The empirical findings unveil a better performance of the new model for directional accuracy than for root mean square error, while density forecasts indicate no statistical differences among the new model and the VAR models.

Selecting the number of factors in approximate factor models using group variable regularization

. We propose a novel method for the estimation of the number of factors in approximate factor models. The model is based on a penalized maximum likelihood approach incorporating an adaptive hierarchical lasso penalty function that enables setting entire columns of the factor loadings matrix to zero, which corresponds to removing uninformative factors. Additionally, the model is capable of estimating weak factors by allowing for sparsity in the non zero columns. We prove that the proposed estimator consistently estimates the true number of factors. Simulation experiments reveal superior selection accuracies for our method in finite samples over existing approaches, especially for data with cross-sectional and serial correlations. In an empirical application on a large macroeconomic dataset, we show that the mean squared forecast errors of an approximate factor model are lower if the number of included factors is selected by our method.

Real-time temperature control in rubber extrusion lines: a neural network approach

In rubber extrusion, precise temperature control is critical due to the process’s sensitivity to fluctuating parameters like compound behavior and batch-specific material variations. Rapid adjustments to temperature deviations are essential to ensure stable throughput and extrudate surface integrity. Based on our previous research, which initiated the development of a feedforward neural network (FNN) without real-world empirical application, we now present a real-time control system using artificial neural networks (ANNs) for dynamic temperature regulation. The underlying FNN was trained on a dataset comprising different ethylene propylene diene monomer (EPDM) rubber compounds, totaling 14,923 measurement points for each temperature value. After training, the FNN achieves remarkable precision, evidenced by a mean absolute percentage error (MAPE) of 0.68% and a mean squared error (MSE) of 0.63°C2 in predicting temperature variations. Its integration into the control system enables real-time responsiveness, allowing for adjustments to temperature deviations within an average timeframe of 68 ms. A key advantage over proportional-integral-derivative (PID) controllers is the ability to continuously learn and adjust to complex, non-linear, and batch-specific process dynamics. This adaptability results in enhanced process stability, as evidenced by inline manufacturing validation. Our paper presents the first ANN-based rubber extrusion control, demonstrating how machine learning techniques can be effectively leveraged for real-time, adaptive temperature control. Beyond rubber extrusion, this strategy has potential applications in various polymer processing and other industries requiring precise temperature control. Future trends may involve the integration of online learning techniques and the expansion of interconnected manufacturing processes.

Leveraging Item Parameter Drift to Assess Transfer Effects in Vocabulary Learning

ABSTRACT Longitudinal models typically emphasize between-person predictors of change but ignore how growth varies within persons because each person contributes only one data point at each time. In contrast, modeling growth with multi-item assessments allows evaluation of how relative item performance may shift over time. While traditionally viewed as a nuisance under the label of “item parameter drift” (IPD), IPD may be of substantive interest if it reflects how learning manifests on different items or subscales at different rates. In this study, we apply the Explanatory Item Response Model to assess IPD in a causal inference context. Simulation results show that when IPD is not accounted for, both parameter estimates and standard errors can be affected. We illustrate with an empirical application to the persistence of transfer effects from a content literacy intervention , revealing how researchers can leverage IPD to achieve a more fine-grained understanding of how vocabulary learning develops over time.

AI-Driven UX/UI Design: Empirical Research and Applications in FinTech

This study explores the transformative impact of AI-driven UX/UI design in the FinTech sector, examining current practices, user preferences, and emerging trends. Through a mixed-methods approach, including surveys, interviews, and case studies, the research reveals significant adoption of AI technologies in FinTech UX/UI design, with 78% of surveyed companies implementing such solutions. Personalization emerges as a dominant trend, with 76% of FinTech apps utilizing AI for tailored user interfaces. The study demonstrates a strong correlation between AI-enhanced features and improved user engagement, with apps incorporating advanced AI features showing a 41% increase in daily active users. Ethical considerations, including data privacy and algorithmic bias, are addressed as critical challenges in AI implementation. The research contributes a conceptual framework for AI-driven UX/UI design in FinTech, synthesizing findings from diverse data sources. Future trends, including emotional AI and augmented reality integration, are explored. The study concludes that while AI-driven UX/UI design offers significant potential for enhancing user experiences in FinTech, balancing innovation with ethical considerations is crucial for responsible implementation and user trust.

Financial fragility, regime change, and monetary policy in an open economy – A model and empirical application to emerging market countries

July 20, 2023 (Revised April 15, 2024) The experiences of the 1997–98 Asian Financial Crisis and the 2008–09 Global Financial Crisis encouraged economists to develop new open-economy dynamic finance-macro models. Such models allow for nonlinearities -- to study the effects of contractionary currency devaluation -- in contrast to models of expansionary currency devaluation. The generic dynamic model of the finance-macro link in this paper includes the dynamics of the inflation rates, output gap, and financial variables (credit flows, risk premia, and exchange rates). The theoretical model with nonlinearities shows that there can be resilient or non-resilient outcomes of the dynamics. The level of resilience is however dependent on some empirical vulnerabilities. Relevant empirics show the risk premium is related not only to domestic real macro and financial variables, but also to the exchange rate and the foreign currency reserves. Econometrically, this paper explores the regime-dependent interaction of the output gap and financial variables in a two-regime non-linear Logistic Vector Smooth Transition Auto-Regressive (LVSTAR) model with a logistic-type transition function. This empirical study reveals that the financial variables of emerging economies show regime-dependent responses to external shocks. Therefore, a positive shock to the risk premia in a negative output gap regime entails an exchange rate depreciation, and accelerating contractions. Overall, however, a diversity of EM economies are examined in terms of their response to external shocks and their resilience levels. This research also has implications for international portfolio holdings.