Dipartimento di Economia
Università degli Studi di Perugia

Relatore  Prof.ssa Veronica Vinciotti - Dipartimento di Matematica – Università di Trento

Data 23 febbraio 2024 - ore 12:00 – 13:00

Luogo Aula 101 -  Dipartimento di Economia

Abstract

Cultural values vary significantly around the world, as evidenced by numerous quantitative survey data. The changes can be seen both at the level of the marginal distributions of cultural traits for the different countries and of the dependency structure between these traits. The latter has only recently been taken into consideration and has been shown to play a key role in locating countries within a cultural spectrum. As the interest is to discover the dependencies between the cultural traits that are specific to each country as well as structural similarities between countries, we propose a computational approach for the joint inference of graphical models from the different environments. To this end, a random graph generative model is introduced. I will present a formulation of the model with a latent space, that captures relatedness across the different countries at the structural level, and a second formulation where potential drivers, such as geographical distance between countries, are directly included in the generative model. In addition, the model allows for the inclusion of external covariates at both the node and edge levels, further adapting to the richness and complexity of high dimensional data from diverse application areas.

Organizzazione Silvia Pandolfi Questo indirizzo email è protetto dagli spambots. È necessario abilitare JavaScript per vederlo.

Locandina (in formato pdf) vedi file allegato

Relatrice: prof.sa Susanna Mancinelli, ordinaria di Politica Economica all’Università degli studi di Ferrara

Luogo e data: martedì 13 febbraio alle ore 12 -  aula 23 - Dipartimento di Economia

Organizzatore dell'evento: dott. Luca Mariani Questo indirizzo email è protetto dagli spambots. È necessario abilitare JavaScript per vederlo.

Visualizza/scarica la locandina

Data e ora: 14 novembre 2023, ore 10.30

Luogo: Aula 2

Titolo: Modello di business dell’industria assicurativa e sua evoluzione

Relatore: Dr. Alberto Tosti

Titolo: Corporate technologies nell’assicurazione

Relatore: Prof. Massimo De Felice

Locandina (in formato pdf)

Relatore: Tommaso Proietti (Università di Roma “Tor Vergata”)

 Data: 29 settembre 2023 dalle 12:00 alle 13:00

Luogo: Aula 101 – Dipartimento di Economia (Via Alessandro Pascoli, 20)

Abstract

Serial dependence and predictability are two sides of the same coin. The literature has considered alternative measures of these two fundamental concepts. In this paper, we aim to distill the most predictable aspect of a univariate time series, i.e., the one for which predictability is optimized. Our target measure is the mutual information between the past and future of a random process, a broad measure of predictability that takes into account all future forecast horizons, rather than focusing on the one-step-ahead prediction error mean square error. The first most predictable aspect is defined as the measurable transformation of the series, which maximizes the mutual information between past and future. The proposed transformation arises from the linear combination of a set of basis functions localized at the quantiles of the unconditional distribution of the process. The mutual information is estimated as a function of the sample partial autocorrelations, by a semiparametric method which estimates an infinite sum by a regularized finite sum. The second most predictable aspect can also be defined, subject to suitable orthogonality restrictions. We also investigate using the most predictable aspect for testing the null of no predictability.

Organizzazione

David Aristei (Questo indirizzo email è protetto dagli spambots. È necessario abilitare JavaScript per vederlo.)

Locandina