Comparison between Generalized Least Square Method and Quantile Regression in Dealing with Heteroscedastic Data

Authors

  • Samhita Pal

DOI:

https://doi.org/10.37591/rrjost.v7i3.1325

Keywords:

Heteroscedasticity, Generalized least square, Quantile regression, efficiency

Abstract

Heteroscedastic data arise from a population whose sub-populations have different variabilities from one another. In presence of heteroscedasticity, the OLS estimators no longer remain the BLUEs, giving rise to wider acceptance regions and confidence intervals for the model parameters. The generalized least square technique, specifically weighted least square method is the conventional way of eliminating heteroscedasticity. However, quantile regression can also be used in dealing with this kind of data. This paper aims at comparing the efficiencies of these two methods.

 

Keywords: Heteroscedasticity, generalized least square, quantile regression, efficiency

Cite this Article

Samhita Pal. Comparison between Generalized Least Square Method and Quantile Regression in Dealing with Heteroscedastic Data. Research & Reviews: Journal of Statistics. 2018; 7(3): 12–16p.

References

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Published

2018-10-31

Issue

Vol. 7 No. 3 (2018)

Section

Review Article

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