  In different fields of applications including, but not limited to,
behavioral, environmental, medical sciences and econometrics, the use of panel
data regression models has become increasingly popular as a general framework
for making meaningful statistical inferences. However, when the ordinary least
squares (OLS) method is used to estimate the model parameters, presence of
outliers may significantly alter the adequacy of such models by producing
biased and inefficient estimates. In this work we propose a new, weighted
likelihood based robust estimation procedure for linear panel data models with
fixed and random effects. The finite sample performances of the proposed
estimators have been illustrated through an extensive simulation study as well
as with an application to blood pressure data set. Our thorough study
demonstrates that the proposed estimators show significantly better
performances over the traditional methods in the presence of outliers and
produce competitive results to the OLS based estimates when no outliers are
present in the data set.
