Abstract

In-work poverty rate in EU countries over the period 2013–2024.
Authors: Matilde Bini and Lucio Masserini
Keywords: labor force, macroeconomy, panel data, poverty, statistical models
Abstract
This study analyses the in-work at-risk-of-poverty rate across EU countries over the period 2013–2024, using macro panel data from the Eurostat database. This rate measures the percentage of employed individuals aged 18–64 who, despite having a job, live in households with an equivalized disposable income below the poverty threshold.
This analysis allows for a comparative cross-country perspective. To address cross-sectional dependence and the presence of unobserved common factors, we propose the use of the Common Correlated Effects pooled (CCE pooled) estimator, proposed by Pesaran (2006). Such unobserved factors may induce correlation across residuals, rendering conventional panel estimators inconsistent. By accounting for these issues, the CCE pooled approach provides robust estimates of the determinants of in-work poverty, while controlling for heterogeneity across countries and over time.
The results contribute to a better understanding of the dynamics of in-work poverty in EU, highlighting structural differences among countries and offering insights relevant for labor market and social policy design.
Comparing Control and Treatment Groups Using Regression and Mixture Models
Author: Daniel Jeske
Keywords: clinical trials, mixture model, regression
Abstract
In the context of comparing a treatment group to a two-component discrete control group, a mixture model for the observations from the treatment group allows for being able to make inference about the existence of responders and non-responders in the treatment group. A generalized treatment effect for the model is represented by the probability a treated patient is a responder and the magnitude of a shift in the control group distribution that models the responses from patients that respond to the treatment. Pseudolikelihood (PSL) and method of moment (MOM) estimators for the generalized treatment effect are derived that account for the inclusion of covariates. Confidence intervals based on the asymptotic properties of the MOM estimator are also developed. Except when the overall treatment effect is small, simulation results demonstrate that the PSL estimator is preferred over the MOM estimator and that the confidence intervals have satisfactorily close to nominal coverage probabilities. Extension to an infinite mixture model is also discussed.
Dynamic Modelling of Irregular Block Count Time Series
Auhtors: Rosanna Verde (presenter), Antonio Balzanella, Raffaele Mattera, Nalini Ravishanker
Abstract: We propose a block time series framework for count data observed over disjoint time windows. We represent irregular time series as a sequence of observed blocks, each with its own local temporal dynamics. At the same time, the blocks are not assumed to be independent. Instead, we introduce a structured dependence across blocks to investigate if nearby blocks are more correlated than those observed with large gaps. This yields a flexible compromise between complete independence of the blocks and the too restrictive global stationarity assumptions. The proposed framework is especially appealing for irregular count time series in which long periods without observations arise from external constraints rather than from the underlying data-generating mechanism. We propose an application to astronomical X-ray binary (XRB) time series.