  We consider the problem of estimating the common time of a change in the mean
parameters of panel data when dependence is allowed between the panels in the
form of a common factor. A CUSUM type estimator is proposed, and we establish
first and second order asymptotics that can be used to derive consistent
confidence intervals for the time of change. Our results improve upon existing
theory in two primary directions. Firstly, the conditions we impose on the
model errors only pertain to the order of their long run moments, and hence our
results hold for nearly all stationary time series models of interest,
including nonlinear time series like the ARCH and GARCH processes. Secondly, we
study how the asymptotic distribution and norming sequences of the estimator
depend on the magnitude of the changes in each panel and the common factor
loadings. The performance of our results in finite samples is demonstrated with
a Monte Carlo simulation study, and we consider applications to two real data
sets: the exchange rates of 23 currencies with respect to the US dollar, and
the GDP per capita in 113 countries.
