Modelling small and medium enterprise loan defaults as rare events: the generalized extreme value regression model

A pivotal characteristic of credit defaults that is ignored by most credit scoring models is the rarity of the event. The most widely used model to estimate the probability of default is the logistic regression model. Since the dependent variable represents a rare event, the logistic regression model shows relevant drawbacks, for example, underestimation of the default probability, which could be very risky for banks. In order to overcome these drawbacks, we propose the generalized extreme value regression model. In particular, in a generalized linear model (GLM) with the binary-dependent variable we suggest the quantile function of the GEV distribution as link function, so our attention is focused on the tail of the response curve for values close to one. The estimation procedure used is the maximum-likelihood method. This model accommodates skewness and it presents a generalisation of GLMs with complementary log–log link function. We analyse its performance by simulation studies. Finally, we apply the proposed model to empirical data on Italian small and medium enterprises.     

Avian biotic homogenization driven by airport-affected environments

Urban Ecosystems - Building and operating airports are human activities associated with adverse changes in the natural environment, resulting in threats to the biodiversity in airport surroundings....     

Developing social work education in Lithuania

From 1991 through 1998, during the turmoil of independence and transition to a democracy and free-market economy, the authors established the first social work education programme in Lithuania and assisted Lithuanian professionals to assume responsibility for the programme. This article supplies an initial general and conceptual narrative and analyses some of the factors that influenced the development of the programme and a practice model to address conditions in Lithuania.     

Increased risk of emergency department presentations for bronchiolitis in infants exposed to air pollution

Air pollution has been linked to an increased risk of several respiratory diseases in children, especially respiratory tract infections. The present study aims to evaluate the association between pediatric emergency department (PED) presentations for bronchiolitis and air pollution. PED presentations due to bronchiolitis in children aged less than 1 year were retrospectively collected from 2007 to 2018 in Padova, Italy, together with daily environmental data. A conditional logistic regression based on a time-stratified case-crossover design was performed to evaluate the association between PED presentations and exposure to NO₂, PM2.5, and PM10. Models were adjusted for temperature, relative humidity, atmospheric pressure, and public holidays. Delayed effects in time were evaluated using distributed lag non-linear models. Odds ratio for lagged exposure from 0 to 14 days were obtained. Overall, 2251 children presented to the PED for bronchiolitis. Infants’ exposure to higher concentrations of PM10 and PM2.5 in the 5 days before the presentation to the PED increased the risk of accessing the PED by more than 10%, whereas high concentrations of NO₂ between 2 and 12 days before the PED presentation were associated with an increased risk of up to 30%. The association between pollutants and infants who required hospitalization was even greater. A cumulative effect of NO₂ among the 2 weeks preceding the presentation was also observed. In summary, PM and NO₂ concentrations are associated with PED presentations and hospitalizations for bronchiolitis. Exposure of infants to air pollution could damage the respiratory tract mucosa, facilitating viral infections and exacerbating symptoms.     

Comparing relative effects of education and economic resources on infant mortality in developing countries

Research on the social determinants of health has often considered education and economic resources as separate indicators of socioeconomic status. From a policy perspective, however, it is important to understand the relative strength of the effect of these social factors on health outcomes, particularly in developing countries. It is also important to examine not only the impact of education and economic resources of individuals, but also whether community and country levels of these factors affect health outcomes. This analysis uses multilevel regression models to assess the relative effects of education and economic resources on infant mortality at the family, community, and country level using data from demographic and Health Surveys in 43 low-and lower-middle-income countries. We find strong effects for both per capita gross national income and completed secondary education at the country level, but a greater impact of education within families and communities.     

Equivalence scales,the cost of children and household consumption patterns in Italy

This article deals with the estimation of parametric equivalence scales for Italian households with different demographic characteristics: composition, location and number of employed members in the household. Using a sample of 43,701 observations on monthly current expenditures from 1997 to 2004 we estimate a demand system for ten goods and we tackle the problem of corner solutions for some goods adopting the Two Step estimator proposed by Shonkweiler and Yen (Am J Agric Econ 81:972–982, 1999). The consumption behavior of households is also analyzed calculating compensated, uncompensated and expenditure elasticites for each commodity. By considering households that differ in composition (number of children), geographic location (four-different macro-areas of Italy), and number of employed adults, we allow for a range of useful comparisons.     

A factor mixture model for analyzing heterogeneity and cognitive structure of dementia

The Health and Retirement Study (HRS) is funded by the National Institute on Aging of US with the aim of investigating the health, social and economic implications of the aging of the American population. The participants of the study receive a thorough in-home clinical and neuropsychological assessment leading to a diagnosis of normal, cognitive impairment but not demented, or dementia. Due to the heterogeneity of the participants into three classes, we analyze some overall cognitive functioning responses through a factor mixture analysis model. The model extends recent proposals developed for binary and continuous data to general mixed data and to the situation of observed heterogeneity, typical of the HRS study.     

Bayesian analysis of skew-normal independent linear mixed models with heterogeneity in the random-effects population

We present a new class of models to fit longitudinal data, obtained with a suitable modification of the classical linear mixed-effects model. For each sample unit, the joint distribution of the random effect and the random error is a finite mixture of scale mixtures of multivariate skew-normal distributions. This extension allows us to model the data in a more flexible way, taking into account skewness, multimodality and discrepant observations at the same time. The scale mixtures of skew-normal form an attractive class of asymmetric heavy-tailed distributions that includes the skew-normal, skew-Student-t, skew-slash and the skew-contaminated normal distributions as special cases, being a flexible alternative to the use of the corresponding symmetric distributions in this type of models. A simple efficient MCMC Gibbs-type algorithm for posterior Bayesian inference is employed. In order to illustrate the usefulness of the proposed methodology, two artificial and two real data sets are analyzed.