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This chapter reviews graphical modeling techniques for estimating large covariance matrices and their inverse. The chapter provides a selective survey of different models and estimators proposed by the graphical modeling literature and offers some practical examples where these methods could be applied in the area of health economics.

The latest financial crisis has stressed the need of understanding the world financial system as a network of interconnected institutions, where financial linkages play a fundamental role in the spread of systemic risks. In this paper we propose to enrich the topological perspective of network models with a more structured statistical framework, that of Bayesian Gaussian graphical models. From a statistical viewpoint, we propose a new class of hierarchical Bayesian graphical models that can split correlations between institutions into country specific and idiosyncratic ones, in a way that parallels the decomposition of returns in the well-known Capital Asset Pricing Model. From a financial economics viewpoint, we suggest a way to model systemic risk that can explicitly take into account frictions between different financial markets, particularly suited to study the ongoing banking union process in Europe. From a computational viewpoint, we develop a novel Markov chain Monte Carlo algorithm based on Bayes factor thresholding.

This study investigates organizational climate under the thriving at work perspective using a network approach. The authors demonstrate how organizational climate functions as a complex system and what relationships between variables from different dimensions are the most important to characterize the construct.

By surveying 119,266 workers from 284 companies based in Brazil, the authors estimated a Gaussian graphical model with LASSO regularization for the complete dataset and for two subsets of cases randomly drawn from the whole dataset. The walktrap algorithm was applied for community detection, and a strong model for measurement invariance was fit to test whether the organizational climate is perceived similarly across groups.

Results show that the networks estimated for both groups are quite consistent, with similar number of communities and items detected. The same pattern was found for the expected influence of each item. Measurement invariance was confirmed, showing that organizational climate is perceived similarly in both groups. The most important community detected and whose items have higher levels of centrality was organizational commitment, followed by a community centered around macro-organizational aspects covering cultural integrity, organizational agility and responsible leadership.

Studies in the field have attested to the possibility of investigating the phenomenon from four (Campbell et al., 1970) to over 80 dimensions (Koys and DeCottis, 1991). As a result, since several dimensions have been produced to investigate organizational climate, there is no consensus on the quality and number of dimensions that should be considered to measure such a vast and multifaceted construct. Built on thriving at work perspective, eight dimensions were devised to cover a wide range of characteristics that distinguish organizational climate, including those related to Industry 4.0 (Coetzee, 2019). However, one may argue that a few dimensions, namely social responsibility, diversity and inclusion, or even more items describing work-life balance could expand the depth and breadth of the instrument and potentially trigger new associations that might eventually impose a new logic to the comprehension of climate as a system. Future studies combining the dimensions investigated in this study with other dimensions are therefore highly recommended for an even more comprehensive investigation.

The results of this investigation show how to apply psychological networks to gain insights into different variables and dimensions of organizational climate. These findings can be used for the development of organizational policies focused on the most relevant aspects of organizational climate. This information would allow organizations to go beyond simply describing the individual frequencies for each item and could even be used to create a weighted scoring model that could prioritize variables with higher levels of centrality.

To the authors’ knowledge, this is the first study that investigates organizational climate using psychological networks; it provides a better understanding of the relationships established between items from different dimensions as opposed to the common cause framework whose focus is on the investigation of dimensions separately.

Our theoretical understanding of subjective well-being in the workplace is incomplete without a dynamic understanding of antecedents and outcomes of subjective well-being. While between-person differences provide useful information about employee outcomes, these differences do not provide information about the relationships between subjective well-being and employee outcomes that evolve over time and across situations. In this paper, we discuss specific statistical methods within the nomothetic and idiographic perspectives that can support dynamic research on subjective well-being in the workplace and outline unanswered contemporary questions regarding structure, processes, and dynamics of subjective well-being that may be addressed with these methods reviewed; some of which were proposed in early research but progressed slowly due to a lack of adequate methods. This discussion highlights how idiographic methods from outside organizational psychology can be applied to the study of worker subjective well-being to strengthen this dynamic approach in a way that addresses limitations associated with reliance on between-person models.

This paper aims to examine potential diversification benefits between Eurozone (i.e. EURO STOXX 50) and key Asia markets: HSI (Hong Kong), KOSPI (South Korea), NIKKEI 225 (Japan) and TSEC (Taiwan). The sample covers the period from 04-01-2008 to 19-10-2023 in daily frequency.

The empirical investigation is based on the wavelet coherence analysis, which is a localized correlation coefficient in the time and frequency domain.

The results provide evidence that long-term diversification benefits exist between EURO STOXX and NIKKEI, EURO STOXX and KOSPI (after 2015) and there are signs for the pair and EURO STOXX-TSEC (after 2014). During the short term, there are signs of diversification benefits during the sample period. However, during the medium term, the diversification benefits seem to diminish.

These results have crucial implications for investors regarding the benefits of international portfolio diversification.

Various method have been used by organisations in the construction industry to improve quality, employing mainly two major techniques: management techniques such as quality control, quality assurance, total quality management; and statistical techniques such as cost of quality, customer satisfaction and the six sigma principle. The purpose of this paper is to show that it is possible to employ the six sigma principle in the field of construction management provided that sufficient information on a particular population is obtained.

Statistical properties of the hyperbolic distribution are given and quality factors such as population in range, number of defects, yield percentage and defects per million opportunities are estimated. Graphical illustrations of the hyperbolic and Gaussian distributions are also given. From that, detailed comparisons of these two distributions are numerically obtained. The impacts of these quality factors are briefly discussed to give a rough guidance to organisations in the construction industry on how to lower cost and to improve project quality by prevention. A case study on a construction project is given in which it is shown that the hyperbolic distribution is better suited to the cost data than the Gaussian distribution. Cost and quality data of all projects in the company are collected over a period of eight years. Each project may consist of a number of phases, typically spanning about three months. Each phase can be considered as a member of the project population. Quality factors of this population are estimated using the six sigma principle.

The paper finds that by using a suitable distribution, it is possible to improve quality factors such as population in range, yield percentage and number of defects per million opportunities.

This paper is of value in assessing the suitability of the hyperbolic and Gaussian distributions in modelling the population and showing that hyperbolic distribution can be more effectively used to model the cost data than the Gaussian distribution.