Title: On the unbiased asymptotic normality of quantile regression with fixed effect
Authors: Antonio Galvao - University of Arizona (United States) [presenting]
Abstract: Nonlinear panel data models with fixed individual effects provide an important set of tools for describing microeconometric data. In a large class of such models (including probit, proportional hazard and quantile regression to name just a few) it is impossible to difference out the individual effects, and inference is usually justified in a `large $n$ large $T$' asymptotic framework. However, there is a considerable gap in the type of assumptions that are currently imposed in models with smooth score functions (such as probit, and proportional hazard) and quantile regression. We show that this gap can be bridged and establish asymptotic unbiased normality for fixed effects quantile regression panels under conditions on $n$ and $T$ that are very close to what is typically assumed in standard nonlinear panels. The results considerably improve upon existing theory and show that quantile regression is applicable to the same type of panel data (in terms of $n,T$) as other commonly used nonlinear panel data models. Thorough numerical experiments confirm our theoretical findings.