TY  - JOUR
T1  - Robust Estimation for Fixed and Random Effects Panel Data Models with
Different Centering Methods
AU - Midi, Habshah AU - Muhammad, Sani 
JO  - Journal of Engineering and Applied Sciences
VL  - 13
IS  - 17
SP  - 7156
EP  - 7161
PY  - 2018
DA  - 2001/08/19
SN  - 1816-949x
DO  - jeasci.2018.7156.7161
UR  - https://makhillpublications.co/view-article.php?doi=jeasci.2018.7156.7161
KW  - Centering method
KW  -fixed and random effect model
KW  -outlier
KW  -ordinary least square
KW  -weighted least square
KW  -panel data
AB  - In the presence of outlying observations in panel data set, the traditional ordinary least square estimator can be strongly biased, lead to erroneous estimation and misleading inferential statement. However, Weighted Least Squares (WLS) are usually used to remedy the effect of outliers. Visek used Least Weighted Squares (LWS) based on mean-centering technique for data transformation. The mean-centering was found to be very sensitive to outliers. Furthermore, robust method for data transformation is needed in order to down weight the effect of outliers. We employed a new method of transformation based on MM-estimate of location termed MM-Centering method. A simulation study was used to evaluate the performance the proposed method. The Weighted Least Square based on the proposed MM-centering Method (WLSMM) was found to be the best method for both the high leverage points and vertical outliers.
ER  -