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Research & Reviews : Journal of Statistics

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

  1. Samhita Pal

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, efficiencyCite this ArticleSamhita 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.

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