Credit contagion in a network of firms with spatial interaction

In this contribution we carried out a wide simulation analysis in order to study the contagion mechanism induced in a portfolio of bank loans by the presence of business relationships among the positions. To this aim we jointly apply a structural model based on a factor approach extended in order to include the presence of microeconomic relationships that takes into account the counterparty risk, and a network model to describe the business connections among interdependent firms. The network of firms is generated resorting to an entropy spatial interaction model.credit risk, bank loan portfolios, contagion models, entropy spatial models

Resilience–Recovery Factors in Post-traumatic Stress Disorder Among Female and Male Vietnam Veterans: Hardiness, Postwar Social Support, and Additional Stressful Life Events

Structural equation modeling procedures were used to examine relationships among several war zone stressor dimensions, resilience-recovery factors, and post-traumatic stress disorder symptoms in a national sample of 1,632 Vietnam veterans (26% women and 74% men). A 9-factor measurement model was specified on a mixed-gender subsample of the data and then replicated on separate subsamples of female and male veterans. For both genders, the structural models supported strong mediation effects for the intrapersonal resource characteristic of hardiness, postwar structural and functional social support, and additional negative life events in the postwar period. Support for moderator effects or buffering in terms of interactions between war zone stressor level and resiliencerecovery factors was minimal

Structured variable selection and estimation

In linear regression problems with related predictors, it is desirable to do variable selection and estimation by maintaining the hierarchical or structural relationships among predictors. In this paper we propose non-negative garrote methods that can naturally incorporate such relationships defined through effect heredity principles or marginality principles. We show that the methods are very easy to compute and enjoy nice theoretical properties. We also show that the methods can be easily extended to deal with more general regression problems such as generalized linear models. Simulations and real examples are used to illustrate the merits of the proposed methods.Comment: Published in at http://dx.doi.org/10.1214/09-AOAS254 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Prokaryotic phylogenies inferred from protein structural domains

This is the publisher's version, also available electronically from http://genome.cshlp.org/content/15/3/393.The determination of the phylogenetic relationships among microorganisms has long relied primarily on gene sequence information. Given that prokaryotic organisms often lack morphological characteristics amenable to phylogenetic analysis, prokaryotic phylogenies, in particular, are often based on sequence data. In this work, we explore a new source of phylogenetic information, the distribution of protein structural domains within fully sequenced prokaryotic genomes. The evolution of the structural domains we use has been studied extensively, allowing us to base our phylogenetic methods on testable theoretical models of structural evolution. We find that the methods that produce reasonable phylogenetic relationships are indeed the methods that are most consistent with theoretical evolutionary models. This work represents, to our knowledge, the first such theoretically motivated phylogeny, as well as the first application of structural information to phylogeny on this scale. Our results have strong implications for the phylogenetic relationships among prokaryotic organisms and for the understanding of protein evolution as a whole

"She is Like a Sister to Me." Gender-Affirming Services and Relationships are Key to the Implementation of HIV Care Engagement Interventions with Transgender Women of Color.

We present findings from qualitative interviews (N = 67) with 36 staff and 31 participants of nine distinct individual and/or group level interventions to engage transgender women of color (TWOC) in HIV care in the U.S. We examine the commonalities amongst the intervention services (addressing unmet basic needs, facilitating engagement in HIV care, health system navigation, improving health literacy, emotional support), and the relationships formed during implementation of the interventions (between interventionists and participants, among participants in intervention groups, between participants and peers in the community). Interventionists, often TWOC themselves, who provided these services developed caring relationships, promoted personal empowerment, and became role models for participants and the community. Intervention groups engaged participants to reinforce the importance of health and HIV care and provided mutual support. Gender affirming services and caring relationships may be two key characteristics of interventions that address individual and structural-level barriers to engage TWOC in HIV care

The estimation of Human Capital in structural models with flexible specification

The present paper focuses on statistical models for estimating Human Capital (HC) at disaggregated level (worker, household, graduates). The more recent literature on HC as a latent variable states that HC can be reasonably considered a broader multi-dimensional non-observable construct, depending on several and interrelate causes, and indirectly measured by many observed indicators. In this perspective, latent variable models have been assuming a prominent role in the social science literature for the study of the interrelationships among phenomena. However, traditional estimation methods are prone to different limitations, as stringent distributional assumptions, improper solutions, and factor score indeterminacy for Covariance Structure Analysis and the lack of a global optimization procedure for the Partial Least Squares approach. To avoid these limitations, new approaches to structural equation modelling, based on Component Analysis, which estimates latent variables as exact linear combinations of observed variables minimizing a single criterion, were proposed in literature. However, these methods are limited to model particular types of relationship among sets of variables. In this paper, we propose a class of models in such a way that it enables to specify and fit a variety of relationships among latent variables and endogenous indicators. Specifically, we extend this new class of models to allow for covariate effects on the endogenous indicators. Finally, an application aimed to measure, in a realistic structural model, the causal impact of formal Human capital (HC), accumulated during Higher education, on the initial earnings for University of Milan (Italy) graduates is illustrated.

Carbon Abatement Leaders and Laggards Non Parametric Analyses of Policy Oriented Kuznets Curves

We study the eventual structural differences of climate change leading ‘actors’ such as Northern EU countries, and ‘lagging actors’ - southern EU countries and the ‘Umbrella group’ - with regard to long run (1960-2001) carbon-income relationships. Parametric and semi parametric panel models show that the groups of countries that were in the Kyoto arena less in favour of stringent climate policy, have yet to experience a turning point, though they at least show relative delinking in their monotonic carbon-income relationship. Northern EU instead robustly shows bell shapes across models, which seem to depend on time related (policy) events. Time related effects are more relevant than income effects in explaining the occurrence of robust Kuznets curves. The reaction of northern EU to exogenous policy events such as the 1992 climate change convention that gave earth to the Kyoto era, and even the second oil shock that preceded it in the 80’s are among the causes of the observed structural differences.Carbon Kuznets Curves, Kyoto, Long Run Dynamics, Policy Events, Heterogeneous Panels, Cross-Section Correlation, Semi Parametric Models, Common Time Trends

A Model of Responses to Race-Based and Gender-Based Stereotype Threat in Computer Science

The perception of stereotype threat among computer science students was examined at two universities. A model of stereotype threat was developed and tested among students enrolled in three undergraduate computer science courses at two universities. The goal of this model was to provide an understanding of the underlying mechanisms through which stereotype threat works. The study tested relationships among the following variables: race-based stereotype threat, gender-based stereotype threat, goal orientation, CS self-efficacy, active coping, behavioral disengagement, effort, and performance. Structural equation modeling was used to test the measurement model and a series of nested structural models. Findings supported the proposed model of stereotype threat and most of the hypothesized relationships. Future directions and contributions of this research are discussed