Root-n consistent estimation of the marginal density in some time series models

TL;DR

This paper introduces a kernel-based method for estimating the marginal density in nonlinear autoregressive time series models, achieving root-n consistency even with unknown noise density, applicable to ARMA and GARCH models.

Contribution

It extends existing results by providing a root-n consistent estimator for the marginal density in nonlinear time series models with parametric conditional mean and variance.

Findings

Estimator is root-n consistent under regularity conditions

Method applies to ARMA and GARCH models

Uniform convergence on compact intervals is established

Abstract

In this paper, we consider the problem of estimating the marginal density in some nonlinear autoregressive time series models for which the conditional mean and variance have a parametric specification. Under some regularity conditions, we show that a kernel type estimate based on the residuals can be root-n consistent even if the noise density is unknown. Our results, which are shown to be valid for classical time series models such as ARMA or GARCH processes, extend substantially the existing results obtained for some homoscedatic time series models. Asymptotic expansion of our estimator is obtained by combining some martingale type arguments and a coupling method for time series which is of independent interest. We also study the uniform convergence of our estimator on compact intervals.