Unconditional quantile treatment effects are difficult to estimate in the presence of fixed
effects. Panel data are frequently used because fixed effects or differences are necessary to …

This paper considers identification and estimation of ceteris paribus effects of continuous
regressors in nonseparable panel models with time homogeneity. The effects of interest are …

We study the conditions under which it is possible to estimate regression quantiles by
estimating conditional means. The advantage of this approach is that it allows the use of …

Stimulated by the analysis of a data set on financial portfolio returns, we propose a multi-kink
quantile regression (MKQR) model with latent homogeneous structure for panel data …

The Box–Cox quantile regression model introduced by Powell is a flexible and numerically
attractive extension of linear quantile regression techniques. Chamberlain and Buchinsky …

This paper introduces quantile regression methods for the analysis of time-seriescross-
section data. Quantile regression offers a robust, and therefore efficient alternative to least …

This paper estimates a class of models which satisfy a monotonicity condition on the
conditional quantile function of the response variable. This class includes as a special case …

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 …

We study statistical inference in quantile autoregression models when the largest
autoregressive coefficient may be unity. The limiting distribution of a quantile autoregression …

This paper develops an instrumental variables estimator for quantile regression in panel
data with fixed effects. Asymptotic properties of the instrumental variables estimator are …