Nonparametric regression methods provide an alternative approach to parametric
estimation that requires only weak identification assumptions and thus minimizes the risk of …

Quantile regression (QR) is a principal regression method for analyzing the impact of
covariates on outcomes. The impact is described by the conditional quantile function and its …

We propose to smooth the objective function, rather than only the indicator on the check
function, in a linear quantile regression context. Not only does the resulting smoothed …

We use local polynomial fitting to estimate the nonparametric M-regression function for
strongly mixing stationary processes {(Yi, **)}. We establish a strong uniform consistency …

This paper presents estimation methods and asymptotic theory for the analysis of a
nonparametrically specified conditional quantile process. Two estimators based on local …

We consider measures of Granger causality in quantiles, which detect and quantify both
linear and nonlinear causal effects between random variables. The measures are based on …

In this paper, we present a systematic study of partial identification of two general classes of
functionals of the joint distribution of two “potential outcomes” when a bivariate sample from …

Truncated conditional expectation functions are objects of interest in a wide range of
economic applications, including income inequality measurement, financial risk …

Deep learning has enjoyed tremendous success in a variety of applications but its
application to quantile regression remains scarce. A major advantage of the deep learning …

The paper proposes a quantile-regression inference framework for first-price auctions with
symmetric risk-neutral bidders under the independent private-value paradigm. It is first …