Concomitant-Variable Latent-Class Beta Inflated Models to Assess Students’ Performance: An Italian Case Study

Students’ performance is a crucial aspect for university programs effectiveness and organization. In this paper, we introduce and analyze a performance index for the first-year students of a private Italian university, namely the Libera Università Maria Ss. Assunta. We use administrative data on 532 undergraduate students enrolled in any of the eight available bachelor degrees in 2015. Our aim is to improve the general understanding of performance linking it with personal student’s characteristics and with degree-specific aspects. A beta inflated latent class approach is employed to identify clusters of performance establishing a link with all available explanatory variables. The empirical analysis unveils that a good and balanced degree organization may improve students’ performance. The student’s ability plays a crucial role in discriminating between good and bad performances, and also strongly depends on individual-specific characteristics, such as the final mark obtained at high school.

     

On Baseline Conditions for Zero-Inflated Longitudinal Count Data

We describe a mixed-effect hurdle model for zero-inflated longitudinal count data, where a baseline variable is included in the model specification. Association between the count data process and the endogenous baseline variable is modeled through a latent structure, assumed to be dependent across equations. We show how model parameters can be estimated in a finite mixture context, allowing for overdispersion, multivariate association and endogeneity of the baseline variable. The model behavior is investigated through a large-scale simulation experiment. An empirical example on health care utilization data is provided.     

Revisions in official data and forecasting

This paper deals with the topic of revisions in macroeconomic Italian data with the aim of investigating whether consecutive vintages published by the National Statistical Institute contain useful information for economic analysis and forecasting. The rationality of the revisions process is tested considering the complete history of data and an application to show the usefulness of revisions for improving the precision of forecasts is proposed. The results on Italian GDP show that embedding the revision process in a dynamic factor model helps to reduce the forecast error in the short term.