Empirical Application Research Articles

A Bayesian functional approach to test models of life course epidemiology over continuous time.

Life course epidemiology examines associations between repeated measures of risk and health outcomes across different phases of life. Empirical research, however, is often based on discrete-time models that assume that sporadic measurement occasions fully capture underlying long-term continuous processes of risk. We propose (i) the functional relevant life course model (fRLM), which treats repeated, discrete measures of risk as unobserved continuous processes, and (ii) a testing procedure to assign probabilities that the data correspond to conceptual models of life course epidemiology (critical period, sensitive period and accumulation models). The performance of the fRLM is evaluated with simulations, and the approach is illustrated with empirical applications relating body mass index (BMI) to mRNA-seq signatures of chronic kidney disease, inflammation and breast cancer. Simulations reveal that fRLM identifies the correct life course model with three to five repeated assessments of risk and 400 subjects. The empirical examples reveal that chronic kidney disease reflects a critical period process and inflammation and breast cancer likely reflect sensitive period mechanisms. The proposed fRLM treats repeated measures of risk as continuous processes and, under realistic data scenarios, the method provides accurate probabilities that the data correspond to commonly studied models of life course epidemiology. fRLM is implemented with publicly-available software.

Estimating the excess of interests paid by consumers when applying an upper rate. The case of Spain

PurposeThe framework of this paper is financial mathematics and, more specifically, the control of data fraud and manipulation with their subsequent economic effects, namely, in financial markets. The purpose of this paper is to calculate the global loss or gain, which supposes, for the borrower, a change of the interest rate while the contracted loan is in force or, in another case, the loan has finished.Design/methodology/approachThe methodology used in this work has been, in the first place, a review of the existing literature on the topic of manipulability and abusiveness of the loan interest rates applied by banks; in the second place, the introduction of a mathematical-financial analysis to calculate the interests paid in excess; and, finally, the compilation of several sentences issued on the application of the so-called mortgage loan reference index (MLRI) to mortgage loans in Spain.FindingsThere are three main contributions in this paper. First, the calculation of the interests paid in excess in the amortization of mortgage loans referenced to an overvalued interest rate. Second, an empirical application shows the amount to be refunded to a Spanish consumer when amortizing his/her mortgage loan referenced to the MLRI instead of the Euro InterBank Offered Rate (EURIBOR). Third, consideration has been made to the effects and the possible solutions to the legal problems arising from this type of contract.Research limitations/implicationsThis research is a useful tool capable of implementing the financial calculation needed to find out overpaid interests in mortgage loans and to execute the sentences dealing with this topic. However, a limitation of this study is the lack of enough sentences on mortgage loans referenced to the MLRI to get some additional information about the number of borrowers affected by these legal sentences and the amount refunded by the financial institutions.Originality/valueTo the best of the authors’ knowledge, this is the first time that deviations in the payment of interests have been calculated when amortizing a mortgage.

Forecasting VaR and ES by using deep quantile regression, GANs-based scenario generation, and heterogeneous market hypothesis

Value at risk (VaR) and expected shortfall (ES) have emerged as standard measures for detecting the market risk of financial assets and play essential roles in investment decisions, external regulations, and risk capital allocation. However, existing VaR estimation approaches fail to accurately reflect downside risks, and the ES estimation technique is quite limited owing to its challenging implementation. This causes financial institutions to overestimate or underestimate investment risk and finally leads to the inefficient allocation of financial resources. The main purpose of this study is to use machine learning to improve the accuracy of VaR estimation and provide an effective tool for ES estimation. Specifically, this study proposes a VaR estimator by combining quantile regression with “Mogrifier” recurrent neural networks to capture the “long memory” and “clustering” properties of financial assets; while for estimating ES, this study directly models the quantile of assets and employs generative adversarial networks to generate future tail risk scenarios. In addition to the typical properties of financial assets, the model design is also consistent with heterogeneous market theory. An empirical application to four major global stock indices shows that our model is superior to other existing models.

Focus Group Discussions on Food Waste: An Empirical Application Providing Insights into Rural and Urban Households in Greece

This paper demonstrates the utility of the focus group discussions (FGDs) methodology in the scientific exploration of food waste. The main objective is to show how FGDs can be designed and implemented by collecting data on household food waste (HFW). The paper provides an empirical application of FGDs to members of urban and rural households in Greece through 10 steps. It is qualitative research that was implemented as a supplement in the framework of a large-scale study on HFW, providing an in-depth interpretation of the statistical results that were arrived at. The research shows that FGDs are an effective data collection methodology that reveals insights into HFW through interactions and complex behaviors. Further, the methodology used gives the opportunity to bring information to the fore. The role of women in relation to food-related responsibilities in the Greek household and the impact of rural experiences on HFW composition constitute two topics under exploration. A detailed understanding of HFW examined through the FGDs methodology enriches the global bibliography, mainly for the case of Greece. In addition, useful information is provided to local and governmental bodies, enabling them to collaborate with academics and experts in food waste management. There is a willingness among household members to raise their awareness of HFW reduction and prevention.

A Ridge-Regularized Jackknifed Anderson-Rubin Test

We consider hypothesis testing in instrumental variable regression models with few included exogenous covariates but many instruments—possibly more than the number of observations. We show that a ridge-regularized version of the jackknifed Anderson and Rubin (henceforth AR) test controls asymptotic size in the presence of heteroscedasticity, and when the instruments may be arbitrarily weak. Asymptotic size control is established under weaker assumptions than those imposed for recently proposed jackknifed AR tests in the literature. Furthermore, ridge-regularization extends the scope of jackknifed AR tests to situations in which there are more instruments than observations. Monte Carlo simulations indicate that our method has favorable finite-sample size and power properties compared to recently proposed alternative approaches in the literature. An empirical application on the elasticity of substitution between immigrants and natives in the United States illustrates the usefulness of the proposed method for practitioners.

Buying Future Endorsements from Prospective Influencers on User-Generated Content Platforms

Excessive monetary compensation and existing contractual agreements of influencers limit the ability of many firms to engage in effective influencer seeding. The authors suggest a forward-looking approach of targeting prospective influencers—while they are still largely unknown (e.g., a few months after their platform registration)—and signing them to endorse the firm in the future (e.g., more than a year later). This approach has the potential to significantly reduce costs. However, as only rarely do newly registered users ultimately become influencers (and as signals are weak), the authors propose a novel framework to cope with this rare-event problem. For empirical demonstration and application, the authors conduct data-based simulations using a data set from a worldwide leading audio platform. Every wave of newly registered users is associated with a profit potential stemming from future endorsements by prospective influencers. With knowledge about the order of magnitude of the return on successful influencer spend, managers applying the framework can extract around 20% of this profit potential (if the return is around three times the spend).

Quasi Maximum Likelihood Estimation of Vector Multiplicative Error Model using the ECCC-GARCH Representation

Abstract We introduce an ECCC-GARCH representation for the vector Multiplicative Error Model (vMEM) that enables maximum likelihood estimation using the multivariate normal distribution. We show via Monte Carlo simulations that the QML estimator possesses desirable small sample properties (towards unbiasedness and efficiency). In the empirical application, we firstly use a two-dimensional vMEM for the squared return and realized volatility, which nests the High-frEquency-bAsed VolatilitY (HEAVY) and Realized GARCH model. We show that the Realized GARCH model is a more appropriate specification for the dynamics of the return-volatility relationship. The second empirical application is a four-dimensional vMEM for volatility spillover effects in the four European stock markets. The results confirm interdependence across European markets and the relative strength of volatility spillovers increases in the post-2010 turmoil periods.

Complexity theory for complexity reduction? Revisiting the ontological and epistemological basis of complexity science with Critical Realism

AbstractComplexity theory (CT) identifies our social system as a contingent and emergent product of non‐linear interactions between existing patterns and events. However, CT scholars carrying out various empirical applications have often adopted constructivist positions that disallow the separate existence of social systems and agency, thereby preventing effective analysis of their interactions. Instead, with the help of Critical Realism (CR), we offer a realist complexity approach that sees complexity in terms of the distinction between the domains of the Real, the Actual, and the Empirical, when existing studies of CT still work with a flat ontology that collapses the three domains into one (the Empirical domain). Our non‐conflationary CR‐CT approach thus argues that a satisfactory explanation of social complexity cannot be at the level of agential experience (the Empirical domain) or at the level of human and systematic events (the Actual domain) but needs to identify causal mechanisms (in the Real domain) of such events. It then combines this depth ontology (that distinguishes the three reality domains) with epistemological relativism (that underscores the contingent character of knowledge claims) to argue that though our knowledge and complexity reduction techniques are socially constructed, it hardly follows that the ontological dimension of reality (spreading across the three domains) is always affected by our complexity reduction efforts at the epistemological dimension in the Empirical domain.

Postmodern organizational theory: empirical substantiation and practical application

The sports industry today is not only an important component of the socio7cultural life of the population, but also an open platform for the development of entrepreneurial projects in sports. Entrepreneurial activity in the sports industry includes the development of new ideas, the implementation of business plans and the search for investors to introduce new directions. Currently, the requirements of the markets for goods and services to enter them are quite high, there is high competition for consumer attention and a fairly short time for the launch of new products and services. Therefore, when implementing a new project in a shortened timeframe, entrepreneurs, including in the sports industry, face a difficult task: to take into account the existing critical factors when developing a new direction and reduce the risks during the implementation of the project. In this regard, managerial decision–making skills and the ability to calculate their consequences come to the fore.

Hypothesis-driven mediation analysis for compositional data: an application to gut microbiome

Sequencing read-count data often exhibit sparsity (zero-count inflation) and overdispersion. As most sequencing techniques provide an arbitrary total count, taxon-specific counts should be treated under the compositional data-analytic framework. There is increasing interest in the role of gut microbiome composition in mediating the effects of exposures on health. Previous compositional mediation approaches have focused on identifying mediating taxa among a number of candidates. We here consider compositional causal mediation when a priori knowledge is available about the hierarchy for a restricted number of taxa, building on a single hypothesis structured as contrasts between appropriate sub-compositions. Based on the assumed causal graph and the theory of multiple contemporaneous mediators, we define non-parametric estimands for overall and coordinate-wise mediation effects and show how they are estimated based on parametric linear models. The mediators have straightforward and coherent interpretations, related to causal questions about interrelationships between the sub-compositions. We perform a simulation study focusing on the impact of sparsity on estimation. While unbiased, the estimators' precision depends on sparsity and the relative magnitudes of exposure-to-mediator and mediator-to-outcome effects in a complex manner. In the empirical application we find an inverse association of fibre intake on insulin level, mainly attributable to the direct effects.

A new fractional integration approach based on neural network nonlinearity with an application to testing unemployment hysteresis

This paper proposes a nonlinear fractional unit root approach which is known as the autoregressive neural network–fractional integration (ARNN–FI) test. This new fractional integration test is based on a new multilayer perceptron of a neural network process, proposed in Yaya et al. (Oxf Bull Econ Stat 83(4):960–981, 2021). The asymptotic theory and the properties of the proposed test are given. By setting up a Monte Carlo simulation experiment, the simulation results reveal that as the number of observations increases, size and power distortions would disappear in the test. The empirical application based on this new test reveals that the unemployment rates of three European countries are neither stationary nor mean-reverting in line with the hysteresis hypothesis.

Dyadic Treatment Effects: Theory and Empirical Applications

Dyadic Treatment Effects: Theory and Empirical Applications

Geometric Understanding of Discriminability and Transferability for Visual Domain Adaptation.

To overcome the restriction of identical distribution assumption, invariant representation learning for unsupervised domain adaptation (UDA) has made significant advances in computer vision and pattern recognition communities. In UDA scenario, the training and test data belong to different domains while the task model is learned to be invariant. Recently, empirical connections between transferability and discriminability have received increasing attention, which is the key to understand the invariant representations. However, theoretical study of these abilities and in-depth analysis of the learned feature structures are unexplored yet. In this work, we systematically analyze the essentials of transferability and discriminability from the geometric perspective. Our theoretical results provide insights into understanding the co-regularization relation and prove the possibility of learning these abilities. From methodology aspect, the abilities are formulated as geometric properties between domain/cluster subspaces (i.e., orthogonality and equivalence) and characterized as the relation between the norms/ranks of multiple matrices. Two optimization-friendly learning principles are derived, which also ensure some intuitive explanations. Moreover, a feasible range for the co-regularization parameters is deduced to balance the learning of geometric structures. Based on the theoretical results, a geometry-oriented model is proposed for enhancing the transferability and discriminability via nuclear norm optimization. Extensive experiment results validate the effectiveness of the proposed model in empirical applications, and verify that the geometric abilities can be sufficiently learned in the derived feasible range.

A public value-based model to understand patients' adoption of eHealth: Theoretical underpinnings and empirical application.

The main objective of this study is to develop an eHealth adoption model based on patients' perceptions of public value dimensions and empirically apply the model to understand the adoption of a governmental health app by Jordanian patients. The study attempts to contribute to overcoming the narrow focus of contemporary theories such as UTAUT and, ultimately, to designing more effective implementation strategies in order to address the current delays in global eHealth adoption. We conducted a quantitative survey of 430 Jordanian patients, utilizing structural equation modeling (SEM) to process the empirical data. Specifically, we applied an SEM two-step approach that involved (a) evaluating the model-data fit through a review of the measurement model(s) and common method bias; and (b) analyzing the structural model. Our findings confirm that the proposed patients' value scale is valid and reliable. Its five dimensions (hedonistic motivation, utilitarian motivation, social value, ethical public value, and public trust value) significantly correlate with patients' public value, and the latter directly affects their use of eHealth apps, with habits mediating this relationship. Among the dimensions, hedonistic motivations tend to be prioritized over utilitarian ones. Ethical and trust values also play an essential role, particularly in how health technology handles patients' data and upholds their dignity and self-esteem. This study highlights the holistic nature of eHealth adoption by patients and the crucial role of public values in their use behavior. It provides useful insights for policymakers and developers.

Variable Importance Measures for Multivariate Random Forests

Multivariate random forests (or MVRFs) are an extension of tree-based ensembles to examine multivariate responses. MVRF can be particularly helpful where some of the responses exhibit sparse (e.g., zero-inflated) distributions, making borrowing strength from correlated features attractive. Tree-based algorithms select features using variable importance measures (VIMs) that score each covariate based on the strength of dependence of the model on that variable. In this paper, we develop and propose new VIMs for MVRFs. Specifically, we focus on the variable’s ability to achieve split improvement, i.e., the difference in the responses between the left and right nodes obtained after splitting the parent node, for a multivariate response. Our proposed VIMs are an improvement over the default naïve VIM in existing software and allow us to investigate the strength of dependence both globally and on a per-response basis. Our simulation studies show that our proposed VIM recovers the true predictors better than naïve measures. We demonstrate usage of the VIMs for variable selection in two empirical applications; the first is on Amazon Marketplace data to predict Buy Box prices of multiple brands in a category, and the second is on ecology data to predict co-occurrence of multiple, rare bird species. A feature of both data sets is that some outcomes are sparse — exhibiting a substantial proportion of zeros or fixed values. In both cases, the proposed VIMs when used for variable screening give superior predictive accuracy over naïve measures.

Core Inflation Rate for China and the ASEAN-10 Countries: Smoothed Signal for Score-Driven Local Level Plus Scale Models

Abstract We use a score-driven minimum mean-squared error (MSE) signal extraction method and perform inflation smoothing for China and the ASEAN-10 countries. Our focus on China and ASEAN-10 countries is motivated by the significant historical variation in inflation rates, e.g. during the 1997 Asian Financial Crisis, the 2007–2008 Financial Crisis, the COVID-19 Pandemic, and the Russian Invasion of Ukraine. Some advantages of the score-driven signal extraction method are that it uses dynamic mean and volatility filters, it considers stationary or non-stationary mean dynamics, it is computationally fast, it is robust to extreme observations, it uses information-theoretically optimal updating mechanisms for both mean and volatility, it uses closed-form formulas for smoothed signals, and parameters are estimated by using the maximum likelihood (ML) method for which the asymptotic properties of estimates are known. In the empirical application, we present the political and economic conditions for each country and analyze the evolution and determinants of the core inflation rate.

A maximum likelihood bunching estimator of the elasticity of taxable income

SummaryThis paper develops a maximum likelihood (ML) bunching estimator of the elasticity of taxable income (ETI). Our structural approach provides a natural framework to simultaneously account for unobserved preference heterogeneity and optimization errors and for measuring their relative importance. We characterize the conditions under which the parameters of the model are identified and show that the ML estimator performs well in terms of bias and precision. The paper also contains an empirical application using Swedish data, showing that both the ETI and the standard deviation of the optimization friction are precisely estimated, albeit relatively small.

Model-Assisted Inference for Covariate-Specific Treatment Effects with High-dimensional Data

Covariate-specific treatment effects (CSTEs) represent heterogeneous treatment effects across subpopulations defined by certain selected covariates. In this article, we consider marginal structural models where CSTEs are linearly represented using a set of basis functions of the selected covariates. We develop a new approach in high-dimensional settings to obtain not only doubly robust point estimators of CSTEs, but also model-assisted confidence intervals, which are valid when a propensity score model is correctly specified but an outcome regression model may be misspecified. With a linear outcome model and subpopulations defined by discrete covariates, both point estimators and confidence intervals are doubly robust for CSTEs. In contrast, confidence intervals from existing high-dimensional methods are valid only when both the propensity score and outcome models are correctly specified. We establish asymptotic properties of the proposed point estimators and the associated confidence intervals. We present simulation studies and empirical applications which demonstrate the advantages of the proposed method compared with competing ones.

Kibria–Lukman Hybrid Estimator for Handling Multicollinearity in Poisson Regression Model: Method and Application

The Poisson regression model (PRM) is a widely used statistical technique for analyzing count data. However, when explanatory variables in the model are correlated, the estimation of regression coefficients using the maximum likelihood estimator (MLE) can be compromised by multicollinearity. This phenomenon leads to inaccurate parameter estimates, inflated variance, and increased mean squared error (MSE). To address multicollinearity in PRM, we propose a novel Kibria–Lukman hybrid estimator. We evaluate the performance of our estimator through extensive Monte Carlo simulations, assessing its accuracy using mean absolute percentage errors (MAPE) and MSE. Furthermore, we provide empirical applications to illustrate the practical relevance of our proposed method. Our simulation results and empirical applications demonstrate the superiority of the proposed estimator.

Optimisation Analysis of Enterprise Environmental Cost Accounting Based on Support Vector Machine Model

Abstract Environmental cost accounting, as a developing field, has been implemented in enterprises for only a brief duration, revealing several areas necessitating enhancements. This paper presents an environmental cost accounting method based on Support Vector Machines (SVM) to address the challenges posed by large and complex data sets in enterprise ecological cost accounting. The technique employs the Radial Basis Function (RBF) kernel to optimize the SVM model, derives the linear regression equation for the Least Squares SVM (LS-SVM) model, and preprocesses enterprise environmental cost data. It integrates Material Flow Cost Accounting (MFCA) to extract essential environmental cost-related data for enterprises. In the empirical application within a tested enterprise, the total cost attributed to resource loss amounted to 1,423,002.55 yuan, representing 4.89% of total expenses, with material costs accounting for the highest share at 86.35%. The analysis suggests that enterprises should prioritize monitoring and managing material costs to minimize resource wastage. Regarding the accounting for external environmental damage, sulfur dioxide and fluoride emissions from material quantity center 1 were identified as the predominant pollutants, exceeding 90% of emissions. This highlights the need for targeted energy-saving and emission-reduction measures for these pollutants to mitigate their environmental impact.