Empirical Application Research Articles

Estimating time-varying factors’ variance in the string-term structure model with stochastic volatility

This paper presents a novel term structure of interest rate (TSIR) model with stochastic volatility and jumps (SVJ) that combines the market framework proposed by Brace et al. (1997) with the string-shock framework of Santa-Clara and Sornette (2001). In this model, the factors’ variance is estimated through the eigendecomposition of a variance–covariance matrix obtained with a measure of market volatility derived from out-of-the-money option prices and historical correlations of interest rates traded in the futures market. The stochastic evolution of the factors’ variance is governed by the 4/2 model developed by Grasselli (2017), including jumps. A novel method is employed to estimate the parameters of the SVJ model that minimizes the distance between the sample moments and the moments of a gamma distribution. The empirical application of the model in the Brazilian derivatives market demonstrates its effectiveness in accurately capturing the volatility smile of interest rate options.

Estimating treatment effect in randomized trial after control to treatment crossover using external controls

ABSTRACT In clinical trials, it is common to design a study that permits the administration of an experimental treatment to participants in the placebo or standard of care group post primary endpoint. This is often seen in the open-label extension phase of a phase III, pivotal study of the new medicine, where the focus is on assessing long-term safety and efficacy. With the availability of external controls, proper estimation and inference of long-term treatment effect during the open-label extension phase in the absence of placebo-controlled patients are now feasible. Within the framework of causal inference, we propose several difference-in-differences (DID) type methods and a synthetic control method (SCM) for the combination of randomized controlled trials and external controls. Our realistic simulation studies demonstrate the desirable performance of the proposed estimators in a variety of practical scenarios. In particular, DID methods outperform SCM and are the recommended methods of choice. An empirical application of the methods is demonstrated through a phase III clinical trial in rare disease.

An integrated causal framework to evaluate uplift value with an example on change in public transport supply

Many empirical applications aim to isolate the impact of implementing new public transport on real estate uplift value. While their conclusions generally point to positive impact, due to reduction in transportation cost, the methodological framework to investigate uplift value has largely evolved over time. This paper reviews the different methodological challenges in measuring causal uplift value and proposes an adjusted parametric approach inspired from the Alonso-Muth-Mills model, returning a complex 2-D price premium function allowing for spatial heterogeneous patterns of the average treatment effect. The proposed framework also accounts for other methodological challenges underlined by literature such as spatial autocorrelation, selectivity and representativity issues, and possible anticipation effects. To illustrate the importance of methodological choices on estimation results, the framework is applied to the case of the implementation of a bus rapid transit (BRT) system in Québec City, a medium-size Canadian city, as a specific case study.

A holistic framework for the analysis of predictive rhetoric in digital visualizations

In digital culture, visualizations are a prevalent and ubiquitous form of communication. A veteran journalistic tool, and an increasingly popular one in digital politics, visualizations offer informative value, attract readership, and increase engagement. Visualizations’ multimodality enables them to convey rhetoric through informative, narrative and visual strategies, making them particularly well-suited for future-oriented discourse. Despite the rise of visualization-focused scholarly work over the past decade, several analytical lacunas remain, due to visualizations’ multimodal nature and their rich array of actors, contexts and usages in the digital world. Specifically, no scholarly approach examines forward-looking visualizations comprehensively, addressing the ways in which their rhetorical layers coalesce to broker knowledge in multimodal predictive discourse. To fill this gap, our paper proposes a holistic framework for their analysis, addressing knowledge-brokering functions, predictive components, and rhetorical strategies. Thus, we ask, ‘How are predictive visualizations rhetorically constructed to mediate the future?’ and answer through conceptualization complemented by qualitative analysis of predictive pandemic visualizations from journalistic and social media. We begin by creating a theoretically informed framework, based on existing perspectives from data-journalism studies, projection studies, and visualization scholarship, which we then refine through analytical workshops and empirical application. Our final analytical framework encapsulates each visualization’s rhetorical strategies, its knowledge-brokering functions, predictive structure, and their interrelations, highlighting the division of rhetorical and predictive labor across each visualization’s components. We conclude with an analytical epilogue in which we demonstrate the usefulness of this framework in holistically analyzing predictive multimodal rhetoric by revisiting the elusive concept of rhetorical complexity in predictive visualizations.

Testing Homoskedasticity in Spatial Panel Data Models

A robust test statistic for testing homoskedasticity in spatial panel data models that have entity and time fixed effects is introduced in a quasi maximum likelihood estimation setting. A size-correction approach is introduced to ensure that the score functions have an asymptotic distribution centered around zero in the local presence of certain nuisance parameters. The outer-product-of-martingale-difference (OPMD) method is used to formulate an estimator for the asymptotic variance of the score functions. The OPMD estimator and the adjusted score functions are used to formulate a computationally simple robust test statistic. The suggested test statistic does not require knowing the presence of spatial dependence in the outcome variable and/or the disturbance terms. The asymptotic distribution of the test statistic is established under the null and local alternative hypotheses. Through Monte Carlo simulations, the finite sample size and power properties of the proposed test statistic are investigated. Finally, two empirical applications are provided to illustrate the practical use of the proposed test statistic.

Spatial Autocorrelation and Political Redistricting: A Task for the Uniform Distribution

An important quantitative geography application area derives from the periodically repeating apportionment process of redrawing legislative district boundaries to guarantee equal voter representation through identical or equivalent population counts, a situation confronting the U.S. Supreme Court with gerrymandering rulings on a decennial basis. This remains a small geographic sample size domain, with states having between one and fifty-two (i.e., California) congressional precincts in 2023. Texas constitutes an informative case study because its sample size of thirty-eight exceeds the frequently touted minimum of thirty, and its 2020 increase was two seats, more than any other state, some of which experienced decreases. Hence, although all fifty states also have undergone internal population redistribution, the Texas redrawn boundaries offer the greatest opportunity for dramatic borderline changes. This article appraises outcomes of this Texas gain within its legal constraints as they pertain to a novel uniform distribution explicitly embracing spatial autocorrelation, a fundamental georeferenced data property. Its name is sui-uniform to differentiate it from the prevalent auto- model convention; this article furnishes its first empirical application. After accounting for spatial autocorrelation, the inferential conclusion for Texas is as preferred: Superpopulation geotagged voting-age populations conform to a uniform distribution, whereas racial and ethnic subpopulations do not.

The finite mixture model for the tails of distribution: Monte Carlo experiment and empirical applications

AbstractThe finite mixture model estimates regression coefficients distinct in each of the different groups of the dataset endogenously determined by this estimator. In what follows the analysis is extended beyond the mean, estimating the model in the tails of the conditional distribution of the dependent variable within each group. While the clustering reduces the overall heterogeneity, since the model is estimated for groups of similar observations, the analysis in the tails uncovers within groups heterogeneity and/or skewness. By integrating the endogenously determined clustering with the quantile regression analysis within each group, enhances the finite mixture models and focuses on the tail behavior of the conditional distribution of the dependent variable. A Monte Carlo experiment and two empirical applications conclude the analysis. In the well‐known birthweight dataset, the finite mixture model identifies and computes the regression coefficients of different groups, each one with its own characteristics, both at the mean and in the tails. In the family expenditure data, the analysis of within and between groups heterogeneity provides interesting economic insights on price elasticities. The analysis in classes proves to be more efficient than the model estimated without clustering. By extending the finite mixture approach to the tails provides a more accurate investigation of the data, introducing a robust tool to unveil sources of within groups heterogeneity and asymmetry otherwise left undetected. It improves efficiency and explanatory power with respect to the standard OLS‐based FMM.

Uncertainty spill-overs: When policy and financial realms overlap

No matter its source, financial- or policy-related, uncertainty can feed onto itself, leading to identification challenges in empirical applications. We add to the stock of analytical methods able to disentangle among various types of uncertainty shocks by proposing an identification approach based on a combination of zero and absolute magnitude restrictions implemented in a multi-country setting. We find evidence of sizable spill-overs within the Euro Area (EA) arising from country-specific uncertainty shocks, with financial realm being a more important source than the policy realm. By leveraging on the flexibility of our approach, we show how important is a correct identification of each uncertainty shock type. A comparison between the on-impact responses of sovereign yields for EA members and non-members suggests that the common currency area likely features institutional arrangements and mechanisms that can initially blur the thin line separating uncertainty shock types. Because we find ECB reactions were not likely to be conditioned by the type of the uncertainty shock hitting a particular country, but by its potential to spill over abroad, we believe ECB has filled the leadership vacuum by doing “whatever it takes” to prevent fragmentation of the EA.

A consumer demands-resources model of engagement: Theoretical and managerial contributions from a cross-validated predictive ability test procedure

Despite the widespread economic and social interest in consumer engagement (CE) in do-it-yourself (DIY) or “making” activities, the construct remains underexplored in the literature. Integrating the theoretical frameworks of CE, the service-dominant logic, and conservation of resources theory, this study uses a mixed-methods approach to identify the resources that best predict CE in making activities. We conduct an exploratory qualitative study through 26 semi-structured interviews with consumers and professionals to refine our hypotheses. We then use a quantitative approach (N = 210) based on the latest advances in partial least squares structural equation modeling (PLS-SEM) to determine predictive validity. It thus offers an empirical application of the latest guidelines in this field. In particular, the study applies the new cross-validated predictive ability test (CVPAT) to test and compare several models. Combined with an importance-performance map analysis (IPMA), the CVPAT approach helps identify operational levers for decision-makers in the maker ecosystem.

Option Pricing Using a Skew Random Walk Binary Tree

We develop a binary tree pricing model with underlying asset price dynamics following Itô–McKean skew Brownian motion. Our work was motivated by the Corns–Satchell, continuous-time, option pricing model. However, the Corns–Satchell market model is incomplete, while our discrete-time market model is defined in the natural world, extended to the risk-neutral world under the no-arbitrage condition where derivatives are priced under uniquely determined risk-neutral probabilities, and is complete. The skewness introduced in the natural world is preserved in the risk-neutral world. Furthermore, we show that the model preserves skewness under the continuous-time limit. We provide empirical applications of our model to the valuation of European put and call options on exchange-traded funds tracking the S&P Global 1200 index.

Unsupervised tail modeling via noisy cross‐entropy minimization

AbstractEstimation of dynamic mixture distributions is a difficult task, because the density contains an intractable normalizing constant. To overcome this difficulty, we develop an approach that maximizes, by means of the cross‐entropy method, a Monte Carlo approximation of the log‐likelihood function. The proposed noisy cross‐entropy approach is unsupervised, since it does not require the specification of a threshold between the distributions. Moreover, it bypasses the evaluation of the normalizing constant, combining good statistical properties with a modest computational burden. Both simulation‐based evidence and empirical applications suggest that noisy cross‐entropy estimation is comparable or preferable to existing methods in terms of statistical efficiency, but is less demanding from the computational point of view.

Competitive parity in chain management – factors that influence the long-term compliance with product and quality criteria along the chain

Abstract Focal firms in the agri-food business must take strategic measures to ensure certain product and quality characteristics along the value chain. This is necessary in order to be able to keep up with competitors in the long term. Research shows that competitive parity, in addition to competitive advantage, should be considered as a strategic dimension. In the production of food, often several actors are involved and the focal firm (that is primarily responsible for the product) must organize the business relationships along the chain in such a way that the desired outcome is achieved in the short and long term. The achievement and safeguarding of competitive parity have been little researched so far. Up to now, there has only been a systematic literature review on this topic. Therefore, this aim of this research is to examine which factors influence the construct of competitive parity. Based on the findings of the literature review and further literature on the management of value chains, a theoretical framework is developed. A first empirical application of the model makes it possible to derive recommendations for action for focal firms in the agri-food industry. Therefore, we conduct a quantitative survey in the wine industry. PLS-SEM is used to analyze the model, using SmartPLS software. This research is the first to empirically investigate the strategic relevance of competitive parity in the agri-food business. The results show that chain management needs to pay attention to competitive parity besides the competitive advantage. Whereas the measures of cooperation have a stronger effect on achieving a competitive advantage, the measures of coordination have a stronger effect on achieving competitive parity. The constructs of power and trust — in contrast to the existing basis of resources and capabilities in the company — seem to have a significant influence on cooperation and coordination.

Overall Model Fit Does Not Imply Linearity in Longitudinal Structural Equation Models: Examining Linear Change Over Time Using Latent Variable Modeling

This article is concerned with the assumption of linear temporal development that is often advanced in structural equation modeling-based longitudinal research. The linearity hypothesis is implemented in particular in the popular intercept-and-slope model as well as in more general models containing it as a component, such as longitudinal structural models with covariates, or models for the study of predictors and correlates of change. In empirical research applications, currently behavioral and social scientists typically evaluate only overall goodness of fit for a considered model. However, this omnibus fit assessment may miss violations of the underlying linearity assumption. To respond to this limitation, the present article discusses a testing procedure for examining the hypothesis of linear growth or decline separately from the widely used overall fit evaluation process. The method is readily utilized with popular latent variable modeling software and is illustrated using a numerical example.

Slow Work: The Mainstream Concept

The global acceleration of the pace of life has led to an increase in working hours, time pressure, and intensification of work tasks in organisations, with consequences for the physical and psychological health of workers. This acceleration and its consequences make it especially relevant to consider the principles of the slow movement and how they can be applied to the work context, focusing on the importance of slowing down the current pace of work and its implications for the sustainability of people and organisations. The key purpose of this study is to define the concept of slow work and understand its relationship with individual and organisational factors in order to extract the structuring dimensions, enabling its empirical study and practical application. Using grounded theory methodology, we conducted 12 semi-structured interviews with leaders of organisations from different sectors. Data analysis was performed using the MAXQDA programme. It was concluded that slow work is a way of working that respects the balance between individual rhythms and the objectives of the organisation, in favour of the sustainability of both parties, and that advocates qualitative goals, thinking time, individual recovery, purpose, and the humanisation of work. The main contribution is the conceptualisation of a construct that may be used in future studies, as well as in the development of organisational policies promoting the slow work culture.

Healthy Lifespan Statistics Derived From Cross-Sectional Prevalence Data Using the Sullivan Method are Informative Summary Measures of Population Health

Health expectancy (HE), commonly derived from cross-sectional prevalence data using the Sullivan method, serves as the most frequently used summary measure of population health. Like lifespan distribution statistics, which are often discussed alongside life expectancy (LE) in demographic studies, analogous statistics on healthy lifespans can provide valuable information on population health. We examine whether healthy lifespan distribution statistics beyond HE can be estimated based on cross-sectional prevalence data and the life table, the data inputs of the Sullivan method. To do so, we treat the Sullivan method as an extension of the stationary population model to health and distinguish between health conditions with and without recovery from the state of decreased health. Our empirical demonstration is based on the prevalence of chronic diseases in selected European countries in 2017 from the Survey of Health, Ageing and Retirement in Europe (SHARE), as well as on life tables from EUROSTAT. We find that the Sullivan method, when considered as an extension of the stationary population model to health, allows for the estimation of a healthy survival distribution and its statistics, beyond HE, for health characteristics with no recovery from the state of decreased health. We show that for such health conditions, the method requires that the number of persons in full health in a stationary population does not increase with age. We argue that for such health dimensions, HE conditional on being in good health at the life table radix age is of relevance for health policy interventions. In our empirical application, we show that the conditional and unconditional measures of HE can give substantially different pictures of population health. Furthermore, we show that across European countries, in contrast to the negative relationship between LE and lifespan inequality, higher HE is associated with greater inequality in healthy years lived when conditional on being healthy at age 50. Overall, the Sullivan method, when considered as an extension of the stationary population model, proves to be a valuable tool for deriving summary statistics of population health beyond HE, which are highly relevant to public policy. * This article belongs to a special issue on “Levels and Trends of Health Expectancy: Understanding its Measurement and Estimation Sensitivity”.

How to establish input and output targets for airlines with heterogeneous production technologies: A nash bargaining DEA approach within the meta-frontier framework

In order to take into consideration the presence of technological heterogeneity in the aviation industry, numerous studies over the past ten years have embraced the data envelopment analysis (DEA)-based meta-frontier framework. Since group targets and meta targets are calculated independently to evaluate the technology gap in airlines, there is a possibility of obtaining unrealistic targets. Estimating the projected inputs and outputs of the evaluated units separately at both the group and meta-frontier does not automatically ensure superiority on the meta-frontier compared to the group frontier. Our proposed combined Nash bargaining model that integrates group technology and metatechnology presents a potential answer to the current unreasonable targets issue and gives more reliable outcomes. The results of the empirical application on a dataset of Alliance and Non-alliance airlines show that the proposed approach generates targets for inputs and outputs which are realistic and optimal, as well as a valid and reasonable technology gap. The finding thus validates the utilisation of our suggested model and it can be used by airline managers to improve the airline operational process.

Weak Identification in Low-Dimensional Factor Models with One or Two Factors

Abstract This paper describes how to reparameterize low-dimensional factor models with one or two factors to fit weak identification theory developed for generalized method of moments models. Some identification-robust tests, here called “plug-in” tests, require a reparameterization to distinguish weakly identified parameters from strongly identified parameters. The reparameterizations in this paper make plug-in tests available for subvector hypotheses in low-dimensional factor models with one or two factors. Simulations show that the plug-in tests are less conservative than identification-robust tests that use the original parameterization. An empirical application to a factor model of parental investments in children is included.

Analyzing health outcomes measured as bounded counts

This paper assesses analytical strategies that respect the bounded-count nature of health outcomes encountered often in empirical applications. Absent in the literature is a comprehensive discussion and critique of strategies for analyzing and understanding such data. The paper's goal is to provide an in-depth consideration of prominent issues arising in and strategies for undertaking such analyses, emphasizing the merits and limitations of various analytical tools empirical researchers may contemplate. Three main topics are covered. First, bounded-count health outcomes' measurement properties are reviewed and their implications assessed. Second, issues arising when bounded-count outcomes are the objects of concern in evaluations are described. Third, the (conditional) probability and moment structures of bounded-count outcomes are derived and corresponding specification and estimation strategies presented with particular attention to partial effects. Many questions may be asked of such data in health research and a researcher's choice of analytical method is often consequential.

Evaluating Density Forecasts Using Weighted Multivariate Scores in a Risk Management Context

AbstractScoring rules are commonly applied to assess the accuracy of density forecasts in both univariate and multivariate settings. In a financial risk management context, we are mostly interested in a particular region of the density: the (left) tail of a portfolio’s return distribution. The dependence structure between returns on different assets (associated with a given portfolio) is usually time-varying and asymmetric. In this paper, we conduct a simulation study to compare the discrimination ability between the well-established scores and their threshold-weighted versions with selected regions. This facilitates a comprehensive comparison of the performance of scoring rules in different settings. Our empirical applications also confirm the importance of weighted-threshold scores for accurate estimates of Value-at-risk and related measures of downside risk.

Modeling Spatial Regimes With Smooth Transitions

In this paper, we introduce a new way of modeling spatial regimes using smooth transitions. We propose an autoregressive spatial lag model where a logistic function captures structural variation in the spatial lag parameter. In the regime-switching spatial lag model with smooth transitions, the effect of the spatial neighbors depends on the transition variable that governs the regime switch. An LM test for detecting nonlinearity is derived, and a simulation study, where the properties of the test are investigated, is conducted. The test shows good power in relatively small samples with moderate deviation from linearity. An empirical application is included, where data on median house prices from the Boston area is used to explore the spatial dependence between census tracts. A smooth shift between regimes governed by the variable property tax rate is found. The smooth spatial lag model performs slightly better than the regular spatial lag model in terms of model fit and capturing spatial dependence.