  In this paper we consider a regression model that allows for time series
covariates as well as heteroscedasticity with a regression function that is
modelled nonparametrically. We assume that the regression function changes at
some unknown time $\lfloor ns_0\rfloor$, $s_0\in(0,1)$, and our aim is to
estimate the (rescaled) change point $s_0$. The considered estimator is based
on a Kolmogorov-Smirnov functional of the marked empirical process of
residuals. We show consistency of the estimator and prove a rate of convergence
of $O_P(n^{-1})$ which in this case is clearly optimal as there are only $n$
points in the sequence. Additionally we investigate the case of lagged
dependent covariates, that is, autoregression models with a change in the
nonparametric (auto-) regression function and give a consistency result. The
method of proof also allows for different kinds of functionals such that
Cram\'er-von Mises type estimators can be considered similarly. The approach
extends existing literature by allowing nonparametric models, time series data
as well as heteroscedasticity. Finite sample simulations indicate the good
performance of our estimator in regression as well as autoregression models and
a real data example shows its applicability in practise.
