Is MORE LESS? The role of data augmentation in testing for structural breaks

In this paper, we examine the impact of increasing the size of a data set in detecting structural breaks. Based on an empirical application, supported by theoretical justification and a simulation experiment, we find that larger sample sizes may make it more rather than less difficult to determine the existence of a structural break. •This paper addresses the issue of data augmentation in structural change testing.•Theoretical and simulation analysis shows that increasing the sample size may decrease power.•An empirical example conrms the findings.