Nonparametric estimation for dependent data with an application to panel time series

TL;DR

This paper develops nonparametric estimation methods for dependent data, especially panel time series, using 2-mixing dependence measures to handle nonlinearity and improve estimation accuracy as the panel size grows.

Contribution

It introduces a nonparametric estimation framework based on 2-mixing dependence for dependent data, including panel time series, allowing for nonlinearity and improved convergence rates.

Findings

Proposed an estimator for the panel mean function.

Derived the rate of convergence for the estimator.

Showed that increasing the number of individuals improves estimation accuracy.

Abstract

In this paper we consider nonparametric estimation for dependent data, where the observations do not necessarily come from a linear process. We study density estimation and also discuss associated problems in nonparametric regression using the 2-mixing dependence measure. We compare the results under 2-mixing with those derived under the assumption that the process is linear. In the context of panel time series where one observes data from several individuals, it is often too strong to assume the joint linearity of processes. Instead the methods developed in this paper enable us to quantify the dependence through 2-mixing which allows for nonlinearity. We propose an estimator of the panel mean function and obtain its rate of convergence. We show that under certain conditions the rate of convergence can be improved by allowing the number of individuals in the panel to increase with time.