Perbandingan Metode Weighted Least Squares dan Huber White Mengatasi Data yang Bersifat Heteroskedastisitas
Main Article Content
Abstract
In multiple linear regression analysis using the Ordinary Least Squares (OLS) method as the parameter estimator, that the resulting regression coefficient estimthere are assumptions that must be met so ates are Best Linear Unbiased Estimator (BLUE). One of the violations if the assumption is not met is called heteroscedasticity where the residual variance is not constant, resulting in an inefficient estimate so that a way is needed to overcome heteroscedasticity. This study aims to compare two methods that can be used to overcome heteroscedasticity problems, namely Weighted Least Squares (WLS) and Huber-White. Using real estate dataset in Taiwan in 2012-2013, heteroscedasticity is detected by Glejser test. The two methods were compared in terms of estimation efficiency. It is found that the WLS method is more efficient than the HW method in overcoming heteroscedasticity in this dataset by obtaining of 0.28 and of 2.83.
Article Details
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
How to Cite
References
David. 2006. On The So-Called “Huber Sandwich Estimator” and “Robust Standard Errors”. The American Statistician, 60:4, 299-302; 2014.
Gosh, S. K. 1991. Econometric: Theory and Aplication. Prentice Hall International, Inc. London.
Greene. 2002. Econometric Analysis, Fifth Edition. United Stated of America: New York University.
Gujarati, D. N. 2004. Basic Econometric, Fourth Edition. New York: Mc Graw Hill.
Gujarati. 2009. Ekonometrika Dasa, Edisi 5. New York: Mc Graw Hill.
Long, J.S., & Ervin, L.H. (1999). Using heteroscedasticity consistent standard errors in the linear regression model. Indiana University.
Mansournia, M.A., Nazemipour, M., Naimi, A.I., Collins, G.S., & Campbell, M.J. 2021. Reflections on modern methods: demystifying robust standard errors for epidemiologists. International Journal of Epidemiology, 50(1), 346-351.
Maziyya, P. A., Sukarsa, I. K. G., & Asih, N. M. (2015). Mengatasi heteroskedastisitas pada regresi dengan menggunakan Weighted Least Square. E-Jurnal Matematika Vol. 4. No. 1: 20-25.
Mendenhall, Wackerly and Scheaffer. (2008). Mathematical Statistics with Applications, Seventh Edition. United Stated of America: Thomson Brooks/Cole.
Suyono. 2015. Analisis Regresi untuk Penelitian. Jakarta: Deepublish.
Welly, Sigit and Ramya. (2022). A Comparison of Weighted Least Square and Quantile Regression for Solving Heteroscedastisity in Simple Linear Regression. Department of Mathematics, Faculty of Mathematics and Natural Science, The University of Bengkulu.
Yitnosumarto, S. 1990. Dasar-dasar Statistika. PT. Raja Grafindo Persada: Jakarta.
Montgomery, D. C., Peck, E. A., & Vining, G. G. (2012). Introduction to Linear Regression Analysis, Fifth Edition. Hoboken, NJ: John Wiley & Sons.