  The panel data regression models have gained increasing attention in
different areas of research including but not limited to econometrics,
environmental sciences, epidemiology, behavioral and social sciences. However,
the presence of outlying observations in panel data may often lead to biased
and inefficient estimates of the model parameters resulting in unreliable
inferences when the least squares (LS) method is applied. We propose extensions
of the M-estimation approach with a data-driven selection of tuning parameters
to achieve desirable level of robustness against outliers without loss of
estimation efficiency. The consistency and asymptotic normality of the proposed
estimators have also been proved under some mild regularity conditions. The
finite sample properties of the existing and proposed robust estimators have
been examined through an extensive simulation study and an application to
macroeconomic data. Our findings reveal that the proposed methods often
exhibits improved estimation and prediction performances in the presence of
outliers and are consistent with the traditional LS method when there is no
contamination.
