Data Driven Robust Estimation Methods for Fixed Effects Panel Data Models

TL;DR

This paper introduces data-driven robust estimation methods for fixed effects panel data models, improving reliability and efficiency in the presence of outliers through extended M-estimation techniques.

Contribution

It develops new robust estimators with data-driven tuning, proving their consistency and asymptotic normality, and demonstrates improved performance over traditional methods in contaminated data.

Findings

Robust estimators outperform least squares in outlier scenarios.

Proposed methods maintain efficiency when data is uncontaminated.

Simulation and real data applications confirm effectiveness.

Abstract

The panel data regression models have gained increasing attention in different 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 biased and inefficient estimates of the model parameters resulting in unreliable inferences when the least squares (LS) method is applied. We propose extensions of the M-estimation approach with a data-driven selection of tuning parameters to achieve desirable level of robustness against outliers without loss of estimation efficiency. The consistency and asymptotic normality of the proposed estimators have also been proved under some mild regularity conditions. The finite sample properties of the existing and proposed robust estimators have been examined through an extensive simulation study and an application to macroeconomic data. Our findings reveal that the proposed methods often exhibits improved estimation and prediction performances in the presence of outliers and are consistent with the traditional LS method when there is no contamination.