The main determinants of COVID-19 spread in Italy are investigated, in this work, by means of a D-vine copula based quantile regression. The outcome is the COVID-19 cumulative infection rate registered on October 30th 2020, with reference to the 107 Italian provinces, and it is regressed on some covariates of interest accounting for medical, environmental and demographic factors. To deal with the issue of spatial autocorrelation, the D-vine copula based quantile regression also embeds a spatial autoregressive component that controls for the extent of spatial dependence. The use of vine copula enhances model flexibility accounting for non-linear relationships and tail dependencies. Moreover, the model selection procedure leads to parsimonious models providing a rank of covariates based on their explanatory power with respect to the outcome.

A D-vine copula-based quantile regression model with spatial dependence for COVID-19 infection rate in Italy / De Giovanni, Livia; D'Urso, Pierpaolo; Vitale, Vincenzina. - In: SPATIAL STATISTICS. - ISSN 2211-6753. - 47:March(2022), pp. 1-31. [10.1016/j.spasta.2021.100586]

A D-vine copula-based quantile regression model with spatial dependence for COVID-19 infection rate in Italy

Livia De Giovanni;Pierpaolo D'Urso;
2022

Abstract

The main determinants of COVID-19 spread in Italy are investigated, in this work, by means of a D-vine copula based quantile regression. The outcome is the COVID-19 cumulative infection rate registered on October 30th 2020, with reference to the 107 Italian provinces, and it is regressed on some covariates of interest accounting for medical, environmental and demographic factors. To deal with the issue of spatial autocorrelation, the D-vine copula based quantile regression also embeds a spatial autoregressive component that controls for the extent of spatial dependence. The use of vine copula enhances model flexibility accounting for non-linear relationships and tail dependencies. Moreover, the model selection procedure leads to parsimonious models providing a rank of covariates based on their explanatory power with respect to the outcome.
D-vine, Copula quantile regression, COVID-19 Italian data, Spatial dependence
A D-vine copula-based quantile regression model with spatial dependence for COVID-19 infection rate in Italy / De Giovanni, Livia; D'Urso, Pierpaolo; Vitale, Vincenzina. - In: SPATIAL STATISTICS. - ISSN 2211-6753. - 47:March(2022), pp. 1-31. [10.1016/j.spasta.2021.100586]
File in questo prodotto:
File Dimensione Formato  
Spatial_Statistics_2022_Dvine-rid_1-15.pdf

Solo gestori archivio

Descrizione: pp. 1-15
Tipologia: Versione dell'editore
Licenza: Tutti i diritti riservati
Dimensione 8.17 MB
Formato Adobe PDF
8.17 MB Adobe PDF   Visualizza/Apri
Spatial_Statistics_2022_Dvine-rid_16-19.pdf

Solo gestori archivio

Descrizione: pp. 16-19
Tipologia: Versione dell'editore
Licenza: Tutti i diritti riservati
Dimensione 9.96 MB
Formato Adobe PDF
9.96 MB Adobe PDF   Visualizza/Apri
Spatial_Statistics_2022_Dvine-rid_20-31.pdf

Solo gestori archivio

Descrizione: pp. 20-31
Tipologia: Versione dell'editore
Licenza: Tutti i diritti riservati
Dimensione 577.26 kB
Formato Adobe PDF
577.26 kB Adobe PDF   Visualizza/Apri
Pubblicazioni consigliate

Caricamento pubblicazioni consigliate

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11385/220199
Citazioni
  • Scopus 1
  • ???jsp.display-item.citation.isi??? 1
social impact