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

A growing body of literature considers investor sentiment as the partial driver of change in
commodity prices. In contrast with previous studies that have almost exclusively focused on …

The likelihood cross validation (LCV) and the least square cross validation (LSCV) are two
commonly used methods of bandwidth selection in kernel density estimation. The LCV is …

Regularized quantile regression (QR) is a useful technique for analyzing heterogeneous
data under potentially heavy-tailed error contamination in high dimensions. This paper …

In this paper, we study the properties of robust nonparametric estimation using deep neural
networks for regression models with heavy tailed error distributions. We establish the non …

We develop a novel control function approach in models where the treatment variable has
bunching at one corner of its support. This situation typically arises when the treatment …

This paper considers the inference problems in nonlinear quantile regressions with both
stationary and nonstationary covariates. The nonparametric local constant quantile estimator …

The primary goal of this article is to identify the research fronts on the application of data-
driven methods in business and economics. For this purpose, the research literature of the …

Robust M-estimation uses loss functions, such as least absolute deviation (LAD), quantile
loss and Huber's loss, to construct its objective function, in order to for example eschew the …

We consider the problem of selecting the smoothing parameter by a data-driven method in
adaptive testing of a parametric model against a nonparametric alternative model …