  This paper proposes a method for estimating multiple change points in panel
data models with unobserved individual effects via ordinary least-squares
(OLS). Typically, in this setting, the OLS slope estimators are inconsistent
due to the unobserved individual effects bias. As a consequence, existing
methods remove the individual effects before change point estimation through
data transformations such as first-differencing. We prove that under reasonable
assumptions, the unobserved individual effects bias has no impact on the
consistent estimation of change points. Our simulations show that since our
method does not remove any variation in the dataset before change point
estimation, it performs better in small samples compared to first-differencing
methods. We focus on short panels because they are commonly used in practice,
and allow for the unobserved individual effects to vary over time. Our method
is illustrated via two applications: the environmental Kuznets curve and the
U.S. house price expectations after the financial crisis.
