Proceedings of the 1st International Conference on Statistics and Analytics, ICSA 2019, 2-3 August 2019, Bogor, Indonesia

Research Article

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

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  • @INPROCEEDINGS{10.4108/eai.2-8-2019.2290517,
        author={Mia  Amelia and Kusman  Sadik and Bagus  Sartono},
        title={The Study of Robust Estimators on Panel Data Regression Model for Data Contaminated with Outliers},
        proceedings={Proceedings of the 1st International Conference on Statistics and Analytics, ICSA 2019, 2-3 August 2019, Bogor, Indonesia},
        publisher={EAI},
        proceedings_a={ICSA},
        year={2020},
        month={1},
        keywords={outliers panel data regression robust estimators},
        doi={10.4108/eai.2-8-2019.2290517}
    }
    
  • Mia Amelia
    Kusman Sadik
    Bagus Sartono
    Year: 2020
    The Study of Robust Estimators on Panel Data Regression Model for Data Contaminated with Outliers
    ICSA
    EAI
    DOI: 10.4108/eai.2-8-2019.2290517
Mia Amelia1,*, Kusman Sadik1, Bagus Sartono1
  • 1: Department of Statistics, Bogor Agricultural University, Bogor, 16680, Indonesia
*Contact email: mia.amelia.0515@gmail.com

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.