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The panel data regression models have gained increasing attention indifferent 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 biasedand inefficient estimates of the model parameters resulting in unreliableinferences when the least squares (LS) method is applied. We propose extensionsof the M-estimation approach with a data-driven selection of tuning parametersto achieve desirable level of robustness against outliers without loss ofestimation efficiency. The consistency and asymptotic normality of the proposedestimators have also been proved under some mild regularity conditions. Thefinite sample properties of the existing and proposed robust estimators havebeen examined through an extensive simulation study and an application tomacroeconomic data. Our findings reveal that the proposed methods oftenexhibits improved estimation and prediction performances in the presence ofoutliers and are consistent with the traditional LS method when there is nocontamination.

Data Driven Robust Estimation Methods for Fixed Effects Panel Data Models