Handling outliers in panel data models: a robust approach

Abstract

Real-world data often violate the conditions assumed by classical estimation methods. One reason for this failure may be the presence of observations with a low probability of belonging to the same distribution as the majority of the data, known as outliers. Outliers can appear in different forms, such as casewise and cellwise outliers. The results of classical estimation methods, particularly those based on least squares, can be seriously affected by the presence of any type of outlier. Panel data modeling is applied in various fields, including economics, finance, marketing, biology, environmental studies, healthcare, and more. The estimation of these models is typically performed using classical methods. In this paper, we consider the random effects panel data model and propose a robust method to estimate the parameters of this model. To evaluate the performance of the proposed robust estimation method compared to the classical estimation method, we conducted a Monte Carlo simulation study. Additionally, we illustrate the proposed methodology by applying it to estimate a model based on a real panel data set.