We propose novel quantile regression methods when the response is discrete and the data come from a longitudinal design. The approach is based on conditional mid-quantiles, which have good theoretical properties even in the presence of ties. Optimization of a ridge-type penalized objective function accommodates for the data dependence. We investigate the performance and pertinence of our methods in a simulation study and an original application to macroprudential policies use in more than one hundred countries over a period of seventeen years.

Russo, A., Farcomeni, A., Geraci, M. (2024). Mid-quantile regression for discrete panel data. JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION, 94, 2754-2771 [10.1080/00949655.2024.2352527].

Mid-quantile regression for discrete panel data

Alfonso Russo;Alessio Farcomeni;
2024-01-01

Abstract

We propose novel quantile regression methods when the response is discrete and the data come from a longitudinal design. The approach is based on conditional mid-quantiles, which have good theoretical properties even in the presence of ties. Optimization of a ridge-type penalized objective function accommodates for the data dependence. We investigate the performance and pertinence of our methods in a simulation study and an original application to macroprudential policies use in more than one hundred countries over a period of seventeen years.
2024
Pubblicato
Rilevanza internazionale
Articolo
Esperti anonimi
Settore SECS-S/01
English
Cluster design
fixed effects
mid-quantile regression
panel data
random effects
Russo, A., Farcomeni, A., Geraci, M. (2024). Mid-quantile regression for discrete panel data. JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION, 94, 2754-2771 [10.1080/00949655.2024.2352527].
Russo, A; Farcomeni, A; Geraci, M
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2108/365166
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