  Panel data of our interest consist of a moderate or relatively large number
of panels, while the panels contain a small number of observations. This paper
establishes testing procedures to detect a possible common change in means of
the panels. To this end, we consider a ratio type test statistic and derive its
asymptotic distribution under the no change null hypothesis. Moreover, we prove
the consistency of the test under the alternative. The main advantage of such
an approach is that the variance of the observations neither has to be known
nor estimated. On the other hand, the correlation structure is required to be
calculated. To overcome this issue, a bootstrap technique is proposed in the
way of a completely data driven approach without any tuning parameters. The
validity of the bootstrap algorithm is shown. As a by-product of the developed
tests, we introduce a common break point estimate and prove its consistency.
The results are illustrated through a simulation study. An application of the
procedure to actuarial data is presented.
