We show that the quantile regression estimator is consistent and asymptotically normal when the error terms are correlated within clusters but independent across clusters. A consistent estimator of the covariance matrix of the asymptotic distribution is provided and we propose a specification test capable of detecting the presence of intra-cluster correlation. A small simulation study illustrates the finite sample performance of the test and of the covariance matrix estimator
This paper aims to propose an innovative approach to identify a typology in a quantile regression mo...
Traditional frequentist quantile regression makes few assumptions on the form of the error distribut...
Traditional frequentist quantile regression makes few assumptions on the form of the error distribut...
AbstractWe study the properties of the quantile regression estimator when data are sampled from inde...
This study develops cluster robust inference methods for panel quantile regression (QR) models with ...
<p>In this article I develop a wild bootstrap procedure for cluster-robust inference in linear quant...
In this paper I develop a wild bootstrap procedure for cluster-robust inference in linear quantile r...
In this paper I develop a wild bootstrap procedure for cluster-robust inference in linear quantile r...
In this paper I develop a wild bootstrap procedure for cluster-robust inference in linear quantile r...
As an alternative to the mean regression model, the quantile regression model has been studied exten...
A new cluster analysis method, K-quantiles clustering, is introduced. K-quantiles clustering can be ...
A new cluster analysis method, K-quantiles clustering, is introduced. K-quantiles clustering can be ...
In this paper we aim at nding similarities among the coefficients from a multivariate regression. Us...
The purpose of this article is to evaluate and compare several three-level cluster randomized design...
This paper studies panel quantile regression models with individual fixed effects. We formally estab...
This paper aims to propose an innovative approach to identify a typology in a quantile regression mo...
Traditional frequentist quantile regression makes few assumptions on the form of the error distribut...
Traditional frequentist quantile regression makes few assumptions on the form of the error distribut...
AbstractWe study the properties of the quantile regression estimator when data are sampled from inde...
This study develops cluster robust inference methods for panel quantile regression (QR) models with ...
<p>In this article I develop a wild bootstrap procedure for cluster-robust inference in linear quant...
In this paper I develop a wild bootstrap procedure for cluster-robust inference in linear quantile r...
In this paper I develop a wild bootstrap procedure for cluster-robust inference in linear quantile r...
In this paper I develop a wild bootstrap procedure for cluster-robust inference in linear quantile r...
As an alternative to the mean regression model, the quantile regression model has been studied exten...
A new cluster analysis method, K-quantiles clustering, is introduced. K-quantiles clustering can be ...
A new cluster analysis method, K-quantiles clustering, is introduced. K-quantiles clustering can be ...
In this paper we aim at nding similarities among the coefficients from a multivariate regression. Us...
The purpose of this article is to evaluate and compare several three-level cluster randomized design...
This paper studies panel quantile regression models with individual fixed effects. We formally estab...
This paper aims to propose an innovative approach to identify a typology in a quantile regression mo...
Traditional frequentist quantile regression makes few assumptions on the form of the error distribut...
Traditional frequentist quantile regression makes few assumptions on the form of the error distribut...