Robust Density Power Divergence Estimates for Panel Data Models Arxiv:2108.02408V1 [Stat.ME] 5 Aug 2021
Robust Density Power Divergence Estimates for Panel Data Models Abhijit Mandal Department of Mathematical Sciences, Univeristy of Texas at El Paso, El Paso, U.S.A. Beste Hamiye Beyaztas Department of Statistics, Istanbul Medeniyet University, Istanbul, Turkey Soutir Bandyopadhyay Department of Applied Mathematics, Statistics Colorado School of Mines, Denver, U.S.A. August 6, 2021 Abstract The panel data regression models have become one of the most widely applied statistical ap- proaches in different fields of research, including social, behavioral, environmental sciences, and econometrics. However, traditional least-squares-based techniques frequently used for panel data models are vulnerable to the adverse effects of the data contamination or outlying observations that may result in biased and inefficient estimates and misleading statistical inference. In this study, we propose a minimum density power divergence estimation procedure for panel data regression models with random effects to achieve robustness against outliers. The robustness, as well as the asymp- totic properties of the proposed estimator, are rigorously established. The finite-sample properties of the proposed method are investigated through an extensive simulation study and an application to climate data in Oman. Our results demonstrate that the proposed estimator exhibits improved performance over some traditional and robust methods in the presence of data contamination. Keywords: Minimum density power divergence; Panel data; Random effects; Robust estimation. 1 Introduction arXiv:2108.02408v1 [stat.ME] 5 Aug 2021 The advancements in applied and methodological researches on panel data have been growing remarkably since the seminal paper of Balestra and Nerlove(1966). Panel data, sometimes referred to as longitudinal data, is multi-dimensional data consisting of observations collected over a period of time on the same set of cross-sectional units.
[Show full text]