Improved predictive model selection

This paper introduces a new information criterion for model selection, based on a predictive distribution which improves the estimative one. The selection statistic is defined as a first-order estimator for the expected Kullback–Leibler information between the true model and the fitted one, obtained by means of the improved predictive procedure. The criterion turns out to be a simple, non-computationally demanding, alternative to the Takeuchi information criterion. Whenever the information identity holds, the Akaike information criterion is recovered as a particular case. The results are obtained in the case of independent, but not necessarily identically distributed, observations. Some applications, related to exponential families of distributions and regression models, are presented.     

Structural Reforms and Fiscal Discipline in Europe

In this paper we provide an overview on recent contributions on the effectiveness of the institutional framework operating in Europe to fulfil the objectives declared in the Lisbon Agenda under the fiscal constraints established in the Stability and Growth Pact. After a review of the Stability and Growth Pact as well as the Lisbon Strategy and the Sapir Report, we discuss the relations between fiscal policy and structural reform. Finally, we focus on 2D points in the EU debate on institutional reforms: government short-sightedness and the coordination of national fiscal policies at the European level.      

Bayeslan Credit Ratings

In this contribution we aim at improving ordinal variable selection in the context of causal models for credit risk estimation. In this regard, we propose an approach that provides a formal inferential tool to compare the explanatory power of each covariate and, therefore, to select an effective model for classification purposes. Our proposed model is Bayesian nonparametric thus keeps the amount of model specification to a minimum. We consider the case in which information from the covariates is at the ordinal level. A noticeable instance of this regards the situation in which ordinal variables result from rankings of companies that are to be evaluated according to different macro and micro economic aspects, leading to ordinal covariates that correspond to various ratings, that entail different magnitudes of the probability of default. For each given covariate, we suggest to partition the statistical units in as many groups as the number of observed levels of the covariate. We then assume individual defaults to be homogeneous within each group and heterogeneous across groups. Our aim is to compare and, therefore select, the partition structures resulting from the consideration of different explanatory covariates. The metric we choose for variable comparison is the calculation of the posterior probability of each partition. The application of our proposal to a European credit risk database shows that it performs well, leading to a coherent and clear method for variable averaging of the estimated default probabilities.     

Assessing repeatability and reproducibility using hierarchical modeling: a case-study of distortion product otoacoustic emissions

We present how the repeatability and reproducibility of a measurement device can be estimated from a suitably defined hierarchical linear model. The methodology is illustrated using a collection of eight data sets which consist of the distortion product otoacoustic emission recordings collected from both ears of ten young Sprague-Dawley rats at different frequencies under eight different recording conditions. We formulate a model which extends the commonly used one-way random effects model (5) to account for an experimental setup that is more elaborated than the ones traditionally used in interlaboratory experiments. The fitted model is easily interpretable and furnishes as a by-product the frequencies at which the highest response level is achieved under the eight recording conditions. These values together with the repeatability and reproducibility limits of the protocols are crucial in contributing to the enhancement of the research capabilities on the possible causes of hearing impairment.     

A threshold based approach to merge data in financial risk management

According to the last proposals by the Basel Committee, banks are allowed to use statistical approaches for the computation of their capital charge covering financial risks such as credit risk, market risk and operational risk.

It is widely recognized that internal loss data alone do not suffice to provide accurate capital charge in financial risk management, especially for high-severity and low-frequency events. Financial institutions typically use external loss data to augment the available evidence and, therefore, provide more accurate risk estimates. Rigorous statistical treatments are required to make internal and external data comparable and to ensure that merging the two databases leads to unbiased estimates.

The goal of this paper is to propose a correct statistical treatment to make the external and internal data comparable and, therefore, mergeable. Such methodology augments internal losses with relevant, rather than redundant, external loss data.     

Global prior distributions for the analysis of discrete graphical models

Summary We propose a new class of prior distributions for the analysis of discrete graphical models. Such a class, obtained following a conditional approach, generalizes the hyper Dirichlet distributions of Dawid and Lauritzen (1993), since it can be extended to non decomposable graphical models. The two classes are compared in terms of model selection, with an application to a medical data-set illustrating the performance of the two resulting procedures. The proposed class turns out to select simpler, more par-simonious structures.     

Influenza-Like Illness Among University Students: Symptom Severity and Duration Due to Influenza Virus Infection Compared to Other Etiologies

Abstract

Objective: University students with influenza-like illness (ILI) were assessed to determine whether symptom severity, duration, or missed days of school or work varied according to etiology. Participants: Sixty persons presenting to a university health clinic with ILI symptoms during 3 consecutive influenza seasons completed baseline survey and viral testing; 51 (85%) completed follow-up. Methods: Influenza viral culture and polymerase chain reaction and respiratory virus immunofluorescence assay testing were performed. Information collected at baseline and follow-up included symptom occurrence, severity, duration, and numbers of days of work and school missed. Results: Influenza virus was confirmed in 63% of participants. Influenza-positive individuals were no more likely to report any symptom or miss more days of school or work. Self-reported severity and durations of symptoms were similar between groups. Conclusions: Students with influenza-associated ILI were similar to those with noninfluenza ILI with respect to severity, duration, and numbers of days of school and work missed.     

Reservation Wages: Explaining Some Puzzling Regional Patterns

We use the Italian Labour Force Survey and the European Household Panel Survey to analyse the distribution of the reservation wages reported by job‐seekers. In Italy, reservation wages appear to be higher in the South — the low‐income and high‐unemployment area of the country — than in the North and Centre. A similar, rather counterintuitive, pattern can also be found in Finland, France, and Spain. First, we show that the way in which these data are commonly collected generates double‐selection bias. Second, we show that this bias has a strong effect on the estimation of the geographical pattern of reservation wages in many countries. The size of this bias is substantial in Italy. When controlling for it, reservation wages are at least 10 per cent higher in the North and Centre than in the South.     

Can the EU funds promote regional resilience at time of Covid-19? Insights from the Great Recession

This paper makes some contributions to the policy discussion on Covid-19. We present real-time evidence on the effects of the coronavirus crisis on regional employment in Italy, by finding that the current shock is producing major adverse consequences in the South and on female occupation. We document that both the absorption and the allocation of the EU funds can sustain the resilience of regional labour markets, by reducing the employment losses at time of Covid-19. We construct regional employment adjustment scenarios based on information and estimates observed during the Great Recession. We discuss the limits of our approach and future developments.