# Multiscale inference and long-run variance estimation in nonparametric regression with time series errors

## TL;DR

This paper introduces multiscale testing methods for qualitative features of nonparametric regression functions with time series errors, along with a new estimator for long-run variance in AR(p) processes, supported by theory, simulations, and climate data analysis.

## Contribution

It presents novel multiscale tests for shape properties of regression functions with time series errors and a new difference-based estimator for long-run variance in AR(p) models.

## Key findings

- Asymptotic validity of the proposed tests and estimator.
- Simulation results demonstrate good finite-sample performance.
- Application to climate data reveals meaningful trend features.

## Abstract

In this paper, we develop new multiscale methods to test qualitative hypotheses about the regression function m in a nonparametric regression model with fixed design points and time series errors. In time series applications, m represents a nonparametric time trend. Practitioners are often interested in whether the trend m has certain shape properties. For example, they would like to know whether m is constant or whether it is increasing/decreasing in certain time regions. Our multiscale methods allow to test for such shape properties of the trend m. In order to perform the methods, we require an estimator of the long-run variance of the error process. We propose a new difference-based estimator of the long-run error variance for the case that the error terms form an AR(p) process. In the technical part of the paper, we derive asymptotic theory for the proposed multiscale test and the estimator of the long-run error variance. The theory is complemented by a simulation study and an empirical application to climate data.