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Small area estimation methods have become a widely used tool to provide accurate estimates for regional indicators such as poverty measures. Recent research has provided evidence that spatial modelling still can improve the precision of regional and local estimates. In this paper, we provide an intrinsic spatial autocorrelation model and prove the propriety of the posterior under a flat prior. Further, we show using the SAIPE poverty data that the gain in efficiency using a spatial model can be essentially important in the presence of a lack of strong auxiliary variables.
Model transformations are central to model-driven software development. Applications of model transformations include creating models, handling model co-evolution, model merging, and understanding model evolution. In the past, various (semi-)automatic approaches to derive model transformations from meta-models or from examples have been proposed. These approaches require time-consuming handcrafting or the recording of concrete examples, or they are unable to derive complex transformations. We propose a novel unsupervised approach, called Ockham, which is able to learn edit operations from model histories in model repositories. Ockham is based on the idea that meaningful domain-specific edit operations are the ones that compress the model differences. It employs frequent subgraph mining to discover frequent structures in model difference graphs. We evaluate our approach in two controlled experiments and one real-world case study of a large-scale industrial model-driven architecture project in the railway domain. We found that our approach is able to discover frequent edit operations that have actually been applied before. Furthermore, Ockham is able to extract edit operations that are meaningful—in the sense of explaining model differences through the edit operations they comprise—to practitioners in an industrial setting. We also discuss use cases (i.e., semantic lifting of model differences and change profiles) for the discovered edit operations in this industrial setting. We find that the edit operations discovered by Ockham can be used to better understand and simulate the evolution of models.
Evolution of stock market efficiency in Europe: Evidence from measuring periods of inefficiency
(2024)
This study introduces novel measures to quantify periods of market inefficiency, enabling precise analysis of their evolution over time and effective comparisons across markets or groups of markets. These measures are applied to an extensive dataset comprising stock indices from 25 European countries from 2007 to 2022. The empirical findings reveal a 20% increase in market inefficiency across Europe, primarily driven by heightened average inefficiencies in the stock markets of the group of developed European countries such as Germany and the Scandinavian countries.
Purpose – The COVID-19 pandemic has had a significant impact on the food tourism industry, leading to business closures and a drop in demand. In response to this challenge, virtual food tourism experiences such as VR have emerged as an alternative to traditional in-person experiences. Aim of this paper is to model consumer adoption of virtual food tourism by integrating the Diffusion of Innovation Theory and the Self-determination Theory.
Methodology/Design/Approach – The Diffusion of Innovation Theory explains the process of innovation adoption, while the Self-determination Theory focuses on consumer motivation. This article proposes that intrinsic (autonomy, relatedness, and competence) and extrinsic (relative advantage, complexity, compatibility, trialability, and observability) motivating factors influence virtual food tourism adoption.
Findings – The study suggests that extrinsic motivators can act as mediators between intrinsic motivation and adoption intention. Integrating these two theories provides a comprehensive understanding of the motivations and mechanisms driving virtual food tourism adoption. It also paves the way for the exploration of intrinsic and extrinsic motivations and specific mechanisms underlying adoption behaviours.
Originality of the research – Destinations, businesses, and policy makers can better navigate the changing landscape of food tourism and leverage the potential of virtual food tourism to create engaging, accessible, and culturally enriching experiences.
This article investigates the representation of the issue of refugees travelling to the Italian coast that was reported by two major Italian newspapers between August 8th and August 19th, 2017. Using analysis tools belonging to communication theory and cognitive sciences, i.e. the concepts of frame and attitude, this article highlights two major points: firstly, the analysis reveals how the two newspapers aimed at establishing a specific relationship with their readers on this topic in the relevant period on the basis of specific interpretative models; secondly, each of these interpretative models relies on the representation of specific emotions which play a central role in the interpretation of reality according to a characteristic facet of the definition of post-truth.
The data presented here contain information on cheating behavior from experiments and general self-reported attitudes related to honesty-related social norms and trust, together with individual-level demographic variables. Our sample included 493 university students in five countries, namely, Germany, Vietnam, Taiwan, China, and Japan. The experiment was monetarily incentivized based on the performance on a matrix task. The participants also answered a survey questionnaire. The dataset is valuable for academic researchers in sociology, psychology, and economics who are interested in honesty, norms, and cultural differences.
The purpose of this article is to evaluate optimal expected utility risk measures (OEU) in a risk-constrained portfolio optimization context where the expected portfolio return is maximized. We compare the portfolio optimization with OEU constraint to a portfolio selection model using value at risk as constraint. The former is a coherent risk measure for utility functions with constant relative risk aversion and allows individual specifications to the investor’s risk attitude and time preference. In a case study with three indices, we investigate how these theoretical differences influence the performance of the portfolio selection strategies. A copula approach with univariate ARMA-GARCH models is used in a rolling forecast to simulate monthly future returns and calculate the derived measures for the optimization. The results of this study illustrate that both optimization strategies perform considerably better than an equally weighted portfolio and a buy and hold portfolio. Moreover, our results illustrate that portfolio optimization with OEU constraint experiences individualized effects, e.g., less risk-averse investors lose more portfolio value in the financial crises but outperform their more risk-averse counterparts in bull markets.