A distribution free test for changes in the trend function of locally stationary processes

TL;DR

This paper introduces a distribution-free, self-normalized test for detecting significant deviations in the mean function of locally stationary processes, applicable to non-stationary time series with smoothly varying means.

Contribution

It develops a novel, asymptotically pivotal test for relevant deviations in the mean function without assuming stationarity, using a new self-normalization approach.

Findings

The test effectively detects relevant mean deviations in simulations.

The method is applicable to real-world data with non-stationary characteristics.

Simulation and data analysis demonstrate the test's practical utility.

Abstract

In the common time series model $X_{i,n} = \mu (i/n) + \varepsilon_{i,n}$ with non-stationary errors we consider the problem of detecting a significant deviation of the mean function $\mu$ from a benchmark $g (\mu )$ (such as the initial value $\mu (0)$ or the average trend $\int_{0}^{1} \mu (t) dt$). The problem is motivated by a more realistic modelling of change point analysis, where one is interested in identifying relevant deviations in a smoothly varying sequence of means $ (\mu (i/n))_{i =1,\ldots ,n }$ and cannot assume that the sequence is piecewise constant. A test for this type of hypotheses is developed using an appropriate estimator for the integrated squared deviation of the mean function and the threshold. By a new concept of self-normalization adapted to non-stationary processes an asymptotically pivotal test for the hypothesis of a relevant deviation is constructed. The results are illustrated by means of a simulation study and a data example.