We provide novel methods for inference on quantile treatment effects in both uncon-ditional and conditional (nonparametric) settings. These methods achieve high-order accuracy by using the probability integral transform and a Dirichlet (rather than Gaus-sian) reference distribution. We propose related methods for joint inference on multiple quantiles and inference on linear combinations of quantiles, again in both unconditional and conditional settings. Optimal bandwidth and coverage probability rates are derived for all methods, and code is provided
We construct a nonparametric estimator of conditional quantiles of Y given X = x using optimal quant...
This article is concerned with nonparametric inference for quantiles from a Bayesian perspective, us...
Quantile regression (QR) is a principal regression method for analyzing the impact of covariates on ...
This dissertation concerns methods for inference on quantiles in various models. Methods that are as...
This paper makes two main contributions to inference for conditional quantiles. First, we construct ...
This paper makes two main contributions to inference for conditional quantiles. First, we construct ...
We propose a nonparametric method to construct confidence intervals for quantile marginal effects (i...
Small-sample properties of a nonparametric estimator of conditional quantiles based on optimal quant...
Using and extending fractional order statistic theory, we characterize the O(n−1) coverage probabili...
Small-sample properties of a nonparametric estimator of conditional quantiles based on optimal quant...
Charlier, Paindaveine, and Saracco (2014) recently introduced a nonparametric estimatorof conditiona...
This paper proposes a fully nonparametric procedure for testing conditional quantile independence. T...
This paper contains a complete procedure for calculating the value of a conditional quantile estimat...
This paper develops a nonparametric method to estimate a conditional quantile function for a panel d...
This paper is concerned with tests of restrictions on the sample path of condi-tional quantile proce...
We construct a nonparametric estimator of conditional quantiles of Y given X = x using optimal quant...
This article is concerned with nonparametric inference for quantiles from a Bayesian perspective, us...
Quantile regression (QR) is a principal regression method for analyzing the impact of covariates on ...
This dissertation concerns methods for inference on quantiles in various models. Methods that are as...
This paper makes two main contributions to inference for conditional quantiles. First, we construct ...
This paper makes two main contributions to inference for conditional quantiles. First, we construct ...
We propose a nonparametric method to construct confidence intervals for quantile marginal effects (i...
Small-sample properties of a nonparametric estimator of conditional quantiles based on optimal quant...
Using and extending fractional order statistic theory, we characterize the O(n−1) coverage probabili...
Small-sample properties of a nonparametric estimator of conditional quantiles based on optimal quant...
Charlier, Paindaveine, and Saracco (2014) recently introduced a nonparametric estimatorof conditiona...
This paper proposes a fully nonparametric procedure for testing conditional quantile independence. T...
This paper contains a complete procedure for calculating the value of a conditional quantile estimat...
This paper develops a nonparametric method to estimate a conditional quantile function for a panel d...
This paper is concerned with tests of restrictions on the sample path of condi-tional quantile proce...
We construct a nonparametric estimator of conditional quantiles of Y given X = x using optimal quant...
This article is concerned with nonparametric inference for quantiles from a Bayesian perspective, us...
Quantile regression (QR) is a principal regression method for analyzing the impact of covariates on ...