The Study of Robust Estimators on Panel Data Regression Model for Data Contaminated with Outliers

Abstract

Outliers can cause biased parameter estimators and deviate from the actual values. This research studies robust estimators on panel data regression model. The robust estimators used are least trimmed squares (LTS) and within-group generalized M (WGM). This research aims to study robust estimator method in estimating panel data regression parameter on simulation data with various kinds of outliers and outlier proportions. This research utilizes primary data taken from the results of simulation data designed based on fixed effects of the panel data regression. The variety of overall simulation data in this study contains 16 types of contamination. The result shows that the within estimation method is not robust against outliers. Based on the absolute relative bias and RMSE, the WGM method produces a small variety of estimators and high accuracy of estimators for various types of outliers and levels of outlier contaminations.