The Applications of Robust Estimation in Fixed Effect Panel Data Model

High leverage points (HLPs) are known to have significant effects on parameter estimation of linear fixed effect regression. Their presence causes panel data to become heavily contaminated which in turn leads to biasness and wrong analysis. Thus, robust regression estimators are introduced to provide resistant estimates towards HLPs. In this study, two Robust Within Group (RW) estimators are applied to a few economics and finance real world data. The study is aimed to estimate the usefulness and efficiency of robust methods in contaminated panel data. Results show the advantage of using robust estimation to reduce the influence of HLPs on panel data over the Ordinary Least Square (OLS). Keywords--panel data; fixed effect; regression; GM-estimator; MM-estimator, robust.