The Applications of Robust Estimation in Fixed Effect Panel Data Model

DOI

10.2991/agc-18.2019.54How to use a DOI?

Keywords

panel data, fixed effect, regression, GM-estimator, MM-estimator, robust

Abstract

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)

Copyright

© 2018, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Nor Mazlina Abu Bakar
AU  - Habshah Midi
PY  - 2019/01
DA  - 2019/01
TI  - The Applications of Robust Estimation in Fixed Effect Panel Data Model
BT  - Proceedings of the 1st Aceh Global Conference (AGC 2018)
PB  - Atlantis Press
SP  - 341
EP  - 346
SN  - 2352-5398
UR  - https://doi.org/10.2991/agc-18.2019.54
DO  - 10.2991/agc-18.2019.54
ID  - AbuBakar2019/01
ER  -