A new heteroskedasticity-consistent covariance matrix estimator and inference under heteroskedasticity

Li, Shunyong; Zhang, Nahui; Zhang, Xiaoqin; Wang, Guannan

doi:10.1080/00949655.2016.1198906

Cited by 4 publications

(3 citation statements)

References 18 publications

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“…• We shall consider alternative covariance matrix estimators that are consistent under both homoskedasticity and heteroskedasticity, such as HC5 (Cribari-Neto, Souza, and Vasconcellos 2007) and HC5m (Li et al 2016).…”

Section: Discussionmentioning

confidence: 99%

Testing inference in heteroskedastic linear regressions: a comparison of two alternative approaches

Pereira

2019

Journal of Statistical Computation and Simulation

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I would like to thank my family, especially my parents, Inelda and Francinaldo, my grandparents, Valtomiro and Socorro, my brothers, Ikaro and Ryan, my nephews, Isis and Pietro, my godson, Vicente, my uncles, Neto and Rubhia, my cousins, Victor, Vanessa, Sofia and Vinícius and all my friends for their affection, love, encouragement, concern and support in difficult times.I also express gratitude to my fiancée, Jhonnata, who help me when I was still getting used to living in Recife. Thank you for your love, affection, patience and dedication.I thank my adviser, Francisco Cribari Neto, for his dedication and patience during the preparation of this dissertation and also for sharing his knowledge with me. I thank him for his wise words, for trusting my ability to overcome difficulties and for his substantial contribution to task of writing this manuscript in English.I also thank the dissertation committee members for reviewing my work and for their suggestions and constructive criticism.A big thank you to Valéria Bittencourt, the Graduate Program in Statistics chief secretary, for her competent work, affection and dedication.Finally, I would like to the financial support I received from CAPES during the past two years.A educação tem raízes amargas, mas os seus frutos são doces.

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Section: Discussionmentioning

confidence: 99%

Testing inference in heteroskedastic linear regressions: a comparison of two alternative approaches

Pereira

2019

Journal of Statistical Computation and Simulation

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“…The first HC-estimator was HC0 proposed by White (1980), called a Sandwich estimator. Next were: HC1 by Hinkley (1977), HC2 by MacKinnon and White (1985), HC3 by Efron (1982), HC4 by Cribari-Neto (2004), HC5 by Cribari-Neto, Souza and Vasconcellos (2007), HC4m by Cribari-Neto and da Silva (2011) and there are still being created new ones, like the newest HC5m by Li et al (2017), each improving previous ones. Formulas of all eight are given below.…”

Section: Heteroskedasticity-consistent Covariance Matrix Estimatorsmentioning

confidence: 99%

“…The most recently presented HC5m-estimator by Li (et al, 2017), combines strengths of HC4m for low degree of leverages and HC5 for high degree of leverages. Simulations performed by Li (et al, 2017) showed that HC5m-based tests are reliable at points both with low or with high leverages and they have the smallest size distortions among tests based on all of HC3, HC4, HC4m and HC5.…”

Section: Heteroskedasticity-consistent Covariance Matrix Estimatorsmentioning

confidence: 99%

Dealing With Heteroskedasticity Within the Modeling of the Quality of Life of Older People

Jabłońska

2018

Statistics in Transition New Series

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Using the estimation method of ordinary least squares leads to unreliable results in the case of heteroskedastic linear regression model. Other estimation methods are described, including weighted least squares, division of the sample and heteroskedasticity-consistent covariance matrix estimators, all of which can give estimators with better properties than ordinary least squares. The methods are presented giving the example of modelling quality of life of older people, based on a data set from the first wave of the COURAGE-Poland study. The comparison of estimators and their practical application may teach how to choose methodologically the most appropriate estimation tool after detection of heteroscedasticity.

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An effective approach towards efficient estimation of general linear model in case of heteroscedastic errors

Bhatti

Khan

Irfan

et al. 2020

Communications in Statistics - Simulation and Computation

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A new heteroskedasticity-consistent covariance matrix estimator and inference under heteroskedasticity

Cited by 4 publications

References 18 publications

Testing inference in heteroskedastic linear regressions: a comparison of two alternative approaches

Testing inference in heteroskedastic linear regressions: a comparison of two alternative approaches

Dealing With Heteroskedasticity Within the Modeling of the Quality of Life of Older People

An effective approach towards efficient estimation of general linear model in case of heteroscedastic errors

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