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