Empirical Illustration Research Articles

A Tale of Two Tails: A New Unique Information Share Measure Based on Copulas

Abstract I propose a novel measure of information share, termed tail information share (TIS), which focuses on modeling the tail dependence of price innovations using copulas. I discuss its detailed technical properties, including unique identifiability, estimation procedures, and statistical properties. The proposed TIS improves over two commonly used measures by providing meaningful economic rationale and unique identifiability. My simulation studies indicate that TIS can yield more accurate estimates of market-specific contributions to price discovery when tail dependence is present. Additionally, I demonstrate the asymptotic consistency and efficiency of TIS estimators. An empirical illustration is provided using a new dataset of high-frequency crude oil futures.

Ethics, Tradition and Temporality in Craft Work: The Case of Japanese Mingei

Based on an empirical illustration of Onta pottery and more broadly a discussion of the Japanese Mingei movement, we study the intimacy between craft work, ethics and time. We conceptualize craft work through the temporal structure of tradition, to which we find three aspects: generational rhythms of making; cycles of use and re-use amongst consumers and a commitment to historically and naturally attuned communities. We argue these temporal structures of tradition in craftwork are animated by two contrasting but co-existing ideas of the good: the moral and the ethical. By developing the work of Elizabeth Grosz, we conceptualize this distinction between moral and ethical as a temporal phenomenon, specifically in differing relations to ideas of the future. Moral aspects of craft work understand the future as a progression from past, whether in preserving practices and norms, or improving upon them in relation to ideals. Ethical aspects understand the future as inherently open to chance and divergence, valuing difference, accident and the possibilities for creativity these entail. Empirically, we show evidence of both aspects in the case of Mingei—a organized movement dedicated to preserving and promoting traditional Japanese craft work. We contribute to studies of craft work by revealing and classifying its temporal aspects. We contribute to studies in business ethics by conceptualising a generative distinction between morals and ethics configured through differing understandings of time.

A road map to capture the spatial dependence underlying regions’ economic resilience

ABSTRACT Regions are embedded in complex webs of interactions that influence, among other things, their economic resilience. However, a general lack of attention is given to the spatial dependence underlying regions’ economic resilience. This paper advocates investigating such spatial interactions and, in doing so, provides a road map to guide researchers through the specification search. The road map is theoretically underpinned by the argument that regions’ resilience is influenced by local spillovers. The several articles in the resilience literature that incorporate spatial dependence are evaluated, and their shortcomings discussed. The paper provides an empirical illustration of the road map.

Treatment effect models with strategic interaction in treatment decisions

This study considers treatment effect models in which others’ treatment decisions can affect both one’s own treatment and outcome. Focusing on the case of two-player interactions, we formulate treatment decision behavior as a complete information game with multiple equilibria. Using a latent index framework and assuming a stochastic equilibrium selection, we prove that the marginal treatment effect from one’s own treatment and that from the partner are identifiable on the conditional supports of certain threshold variables determined through the game model. Based on our constructive identification results, we propose a two-step semiparametric procedure for estimating the marginal treatment effects using series approximation. We show that the proposed estimator is uniformly consistent and asymptotically normally distributed. As an empirical illustration, we investigate the impacts of risky behaviors on adolescents’ academic performance.

Stochastic Analysis of Asset Returns Which Follows Multiplicative Effects Series

The key importance of asset values and it return rates are geared towards investment funds which grows wealth over time. This paper considered stochastic models where asset values were examined. A twelve (12) months (2022) initial closing stock price data of Oando, PLC, were used in the study. The problems were accurately solved analytical by means of Ito’s theorem and a closed form solutions were obtained which governed asset price return rates through multiplicative effects series. The empirical illustrations between Stochastic Differential Equations (SDEs) and Stochastic Delay Differential Equations (SDDEs) asset values were compared to inform Oando PLC in terms of decision making. However, the behaviour on the value of asset prices were analysed using Kolmogorov-Smirnov (KS). To this end, graphical solutions and the effects of the relevant stock variables were conferred accordingly.

Information, doubt, and democracy: how digitization spurs democratic decay

ABSTRACT The amount of information available to citizens due to global digitization and new technologies is unprecedented. However, in established democracies where access to alternative sources of information is guaranteed and educational levels are high, we witness a rise in attitudes and actions that threaten democratic foundations. Some citizens succumb to misinformation that has led them down the path of radicalization and violence; others withdraw completely from political life. How do we explain these developments? Building on democratic theory and the concepts of informational learned helplessness and selective exposure, we introduce a theoretical framework that connects digitization and digital technologies to attitudes and behaviour that threaten democracy even before democratic decay manifests. We argue that AI-assisted digital technologies expose societies to an abundance of contradictory information, limit citizens’ ability to assess their trustworthiness, and reinforce the tailored dissemination of misinformation. We present the US and Germany as empirical plausibility probes that show how these features drive some citizens towards informational agnosticism, which results in de-politicization and political apathy. Others might become misbelievers who display negative attitudes towards democracy and/or engage in anti-democratic actions. Our probe offers an initial empirical illustration that provides a basis for further systematic investigations of digital technology’s effects on democracy.

Leveraging Observation Timing Variability to Understand Intervention Effects in Panel Studies: An Empirical Illustration and Simulation Study

To examine developmental processes, intervention effects, or both, longitudinal studies often aim to include measurement intervals that are equally spaced for all participants. In reality, however, this goal is hardly ever met. Although different approaches have been proposed to deal with this issue, few studies have investigated the potential benefits of individual variation in time intervals. In the present paper, we examine how continuous time dynamic models can be used to study nonexperimental intervention effects in longitudinal studies where measurement intervals vary between and within participants. We empirically illustrate this method by using panel data (N = 2,877) to study the effect of the transition from primary to secondary school on students’ motivation. Results of a simulation study also show that the precision and recovery of the estimate of the effect improves with individual variation in time intervals.

Quality indices for topic model selection and evaluation: a literature review and case study

BackgroundTopic models are a class of unsupervised machine learning models, which facilitate summarization, browsing and retrieval from large unstructured document collections. This study reviews several methods for assessing the quality of unsupervised topic models estimated using non-negative matrix factorization. Techniques for topic model validation have been developed across disparate fields. We synthesize this literature, discuss the advantages and disadvantages of different techniques for topic model validation, and illustrate their usefulness for guiding model selection on a large clinical text corpus.Design, setting and dataUsing a retrospective cohort design, we curated a text corpus containing 382,666 clinical notes collected between 01/01/2017 through 12/31/2020 from primary care electronic medical records in Toronto Canada.MethodsSeveral topic model quality metrics have been proposed to assess different aspects of model fit. We explored the following metrics: reconstruction error, topic coherence, rank biased overlap, Kendall’s weighted tau, partition coefficient, partition entropy and the Xie-Beni statistic. Depending on context, cross-validation and/or bootstrap stability analysis were used to estimate these metrics on our corpus.ResultsCross-validated reconstruction error favored large topic models (K ≥ 100 topics) on our corpus. Stability analysis using topic coherence and the Xie-Beni statistic also favored large models (K = 100 topics). Rank biased overlap and Kendall’s weighted tau favored small models (K = 5 topics). Few model evaluation metrics suggested mid-sized topic models (25 ≤ K ≤ 75) as being optimal. However, human judgement suggested that mid-sized topic models produced expressive low-dimensional summarizations of the corpus.ConclusionsTopic model quality indices are transparent quantitative tools for guiding model selection and evaluation. Our empirical illustration demonstrated that different topic model quality indices favor models of different complexity; and may not select models aligning with human judgment. This suggests that different metrics capture different aspects of model goodness of fit. A combination of topic model quality indices, coupled with human validation, may be useful in appraising unsupervised topic models.

A multi-objective sustainable financial portfolio selection approach under an intuitionistic fuzzy framework

In recent decades, sustainable investing has caught on with investors, and it has now become the norm. In the age of start-ups, with scant information on the sustainability aspects of an asset, it becomes harder to pursue sustainable investing. To this end, this paper proposes a sustainable financial portfolio selection approach in an intuitionistic fuzzy framework. We present a comprehensive three-stage methodology in which the assets under consideration are ethically screened in Stage-I. Stage-II is concerned with calculating the sustainability scores, based on various social, environmental, and economic (SEE) criteria and an evaluation of the return and risk of the ethical assets. Intuitionistic fuzzy set theory is used to gauge the linguistic assessment of the assets on several SEE criteria from multiple decision-makers. A novel intuitionistic fuzzy multi-criteria group decision-making technique is applied to calculate the sustainability score of each asset. Finally, in Stage-III, an intuitionistic fuzzy multi-objective financial portfolio selection model is developed with maximization of the satisfaction degrees of the sustainability score, return, and risk of the portfolio, subject to several constraints. The ϵ-constraint method is used to solve this model, which yields various efficient, sustainable financial portfolios. Subsequently, investors can choose the portfolio best suited to their preferences from this pool of efficient, sustainable financial portfolios. A detailed empirical illustration and a comparison with existing works are given to substantiate and validate the proposed approach.

Alternative Confirmatory Factor Analytic Models for Examining Preservice Teachers' Non-Cognitive Skills

This study provides a comparative analysis of the inferences that can be reached about preservice teachers' emotional awareness and personality traits when several alternative factor analytic models are tested simultaneously. An empirical illustration is provided using two datasets collected for a research study aiming to profile the social-emotional behavior repertoire of preservice teachers. Four alternative factor analytic models were considered for both datasets: the unidimensional model, the correlated model, the higher- (or second-) order model, and the bifactor model. Results indicate that the higher-order factor model for emotional awareness (n = 670) and the bifactor model for personality traits (n = 670) were the preferred models that better represent the underlying factor structures. Multidimensionality and practical decisions concerning score reporting are discussed.

Socorro: Persistent bricoleurs at the urban margins

This article examines the ways in which the urban poor in Argentina help one another in the arduous task of making ends meet when neither the formal labor market nor state welfare policies are able to secure their subsistence. Drawing on long-term ethnographic fieldwork, the article makes one substantive, one analytic, and one theoretical claim. Substantively, the article argues by way of empirical illustration that the urban poor are hardworking bricoleurs. Analytically, the article demonstrates the advantages of studying poor people’s strategies in a simultaneously historic and ethnographic fashion through joint collaborative fieldwork. Theoretically, the article pushes toward replacing the notions of ‘strategy of survival or subsistence’ with the more encompassing notion of ‘strategy of persistence’.

Short T dynamic panel data models with individual, time and interactive effects

SummaryThis paper proposes a transformed quasi‐maximum likelihood (TQML) estimator for short dynamic fixed effects panel data models allowing for interactive effects through a multifactor error structure. The proposed estimator is robust to the heterogeneity of the initial values and common unobserved effects, while at the same time allowing for standard fixed and time effects. It is applicable to both stationary and unit root cases. The order condition for identification of the number of interactive effects is established, and conditions are derived under which the parameters are locally identified. It is shown that global identification in the presence of the lagged dependent variable cannot be guaranteed. The TQML estimator is proven to be consistent and asymptotically normally distributed. A sequential multiple testing likelihood ratio procedure is also proposed for estimation of the number of factors which is shown to be consistent. Finite sample results obtained from Monte Carlo simulations show that the proposed procedure for determining the number of factors performs very well, and the TQML estimator has small bias and root mean square error (RMSE) and correct empirical size in most settings. The practical use of the TQML approach is demonstrated by means of two empirical illustrations from the literature on cross county crime rates and cross country growth regressions.

Group Solidarity Based on Halal Network Labels: Moral Embeddedness of Agents of PT. HNI HPAI from an Economic Sociology Perspective

The number of consumers of halal products in the world has continued to increase since 2015 until now. For Muslim consumers, halal products are related to their moral and social values. Halal products meet the dietary standards of a devout Muslim. This research is intended to explore and present empirical evidence about group solidarity from the theory of moral embeddedness. It is important to provide empirical illustrations, especially those related to the halal product business network. At the same time, PT can also use empirical evidence. HNI HPAI in developing its business network. This study uses Beckert's perspective to analyze the foreign labor market. This research analyzes the formation of network label-based group solidarity in the moral-based halal product market. Researchers use digital research, namely SNA (Social Network Analysis) and TNA (Textual Network Analysis), as new methodologies. SNA to analyze the actor relation between HNI and Graph Density. TNA to analyze the narrative words of success stories of HNI agents in online media. The findings explain that morals, beliefs, and norms affect group solidarity based on the halal product business network label.

Estimating a Continuous Treatment Model with Spillovers: A Control Function Approach

We study a continuous treatment effect model in the presence of treatment spillovers through social networks. We assume that one’s outcome is affected not only by his/her own treatment but also by a (weighted) average of his/her neighbors’ treatments, both of which are treated as endogenous variables. Using a control function approach with appropriate instrumental variables, we show that the conditional mean potential outcome can be nonparametrically identified. We also consider a more empirically tractable semiparametric model and develop a three-step estimation procedure for this model. As an empirical illustration, we investigate the causal effect of the regional unemployment rate on the crime rate.

Bootstrap Inference for Panel Data Quantile Regression

This article develops bootstrap methods for practical statistical inference in panel data quantile regression models with fixed effects. We consider random-weighted bootstrap resampling and formally establish its validity for asymptotic inference. The bootstrap algorithm is simple to implement in practice by using a weighted quantile regression estimation for fixed effects panel data. We provide results under conditions that allow for temporal dependence of observations within individuals, thus, encompassing a large class of possible empirical applications. Monte Carlo simulations provide numerical evidence the proposed bootstrap methods have correct finite sample properties. Finally, we provide an empirical illustration using the environmental Kuznets curve.

Setting closer targets based on non-dominated convex combinations of Pareto-efficient units: A bi-level linear programming approach in Data Envelopment Analysis

Data Envelopment Analysis (DEA) very often sets unrealistic targets, which require from the decision-making units (DMUs) a huge amount effort, perhaps non-assumable, for their achievement. For the identification of best practices in the benchmarking, this paper proposes considering as peers not only DEA efficient DMUs but also those that are Pareto efficient, and allowing for reference sets spanning convex combinations that are not dominated by observed DMUs. It is therefore an approach that somehow relaxes the convexity in DEA, and sets targets representing best practices in the sense that they define a course of action leading to results that are not worse than those of the real plans. A bi-level linear programming (BLP) DEA model is developed which finds the closest targets from a convex combination of the DMUs in a reference set satisfying such non-dominance requirement. This approach has proven to be successful in setting targets that require an effort significantly smaller than that needed to achieve the closest targets on the strong efficient frontier of both the DEA and the free disposal hull (FDH) technologies. In the empirical illustration, we have observed reductions in the order of 40 percentage points on average in the total effort required for their achievement, thus setting more realistically implementable directions for improving performance towards best practices than those provided by conventional DEA and FDH.

Instrumental variable quantile regression for clustered data

The purpose is to enable inference in case of quantile regression with endogenous covariates and clustered data. It is proven that the instrumental variable quantile regression estimator is consistent where there is correlation of errors within clusters, and an asymptotic distribution for the estimator, which may be used for inference for a given quantile τ, is derived. As regards inference based on the entire instrumental variable quantile regression process, it is proven that cluster-based resampling of a statistic of a certain class offers a computationally tractable approach for implementing asymptotic tests. The theoretical results concerning the asymptotic properties of the instrumental variable quantile regression estimator for clustered data are supported by simulation analysis. An empirical illustration shows the use of the proposed technique in order to estimate the earning equations of US men and women where female labor supply is endogenous and subject to the shock of World War II.

Semi-nonparametric estimation of random coefficients logit model for aggregate demand

In this paper, we propose a two-step semi-nonparametric estimator for the widely used random coefficients logit demand model. The approach applies to the same setup as Berry et al. (1995, BLP)-type of models with many products, but has the advantage of not requiring computing demand inversion. In particular, the first step of our approach estimates the fixed coefficients via a computationally very easy linear sieve generalized method of moments (GMM). The second step uncovers the distribution of the random coefficient via a sieve minimum distance or GMM procedure. We show identification and derive the asymptotic properties of the estimator in a large market environment. Monte Carlo simulations and empirical illustrations support the theoretical results and demonstrate the usefulness of our estimator in practice.

A supply risk perspective integrated sustainable supplier selection model in the intuitionistic fuzzy environment.

With the recent focus on supply risk management in sustainable supply chains, it is more important than ever to evaluate and select the right sustainable suppliers from a supply risk perspective. However, few existing studies consider supply risks from the perspective of all three triple-bottom-line dimensions at the same time. To bridge this research gap, this research constructs a supply risk perspective integrated sustainable supplier selection model in the intuitionistic fuzzy environment. First of all, the weights of decision-makers in the decision-making group are obtained by intuitionistic fuzzy set. Secondly, after obtaining the aggregated intuitionistic fuzzy decision matrix considering the weight of decision-makers, the fuzzy entropy weight method is used to calculate criteria weight, objectively. Then, an improved failure mode and effects analysis is used to undertake risk assessments and to identify high-risk suppliers. Last but not least, the extended alternative queuing method is adopted to rank the eligible sustainable suppliers in the intuitionistic fuzzy environment. The proposed model not only reduces the uncertainty of decision-making in sustainable supplier selection, but also enables focal companies to reduce supply risk in their sustainable supplier selection practices and prevent the failure modes that relate to supply risk. The practicality and effectiveness of the proposed model are verified through an empirical illustration in a leading electrical appliance manufacturer in China.

Tie-Break Bootstrap for Nonparametric Rank Statistics

In this article, we propose a new bootstrap procedure for the empirical copula process. The procedure involves taking pseudo samples of normalized ranks in the same fashion as the classical bootstrap and applying small perturbations to break ties in the normalized ranks. Our procedure is a simple modification of the usual bootstrap based on sampling with replacement, yet it provides noticeable improvement in the finite sample performance. We also discuss how to incorporate our procedure into the time series framework. Since nonparametric rank statistics can be treated as functionals of the empirical copula, our proposal is useful in approximating the distribution of rank statistics in general. As an empirical illustration, we apply our bootstrap procedure to test the null hypotheses of positive quadrant dependence, tail monotonicity, and stochastic monotonicity, using U.S. Census data on spousal incomes in the past 15 years.