Identification of suitable explanatory variable in goldfeld-quandt test and robust inference under heteroscedasticity and high leverage points

Violation of the assumption of homogeneity of variance of the errors in the linear regression model, causes heteroscedasticity. In the presence of heteroscedastic errors, the ordinary least squares (OLS) estimates are unbiased and consistent, but their covariance matrix estimator is biased and no...

Full description

Saved in:

Bibliographic Details
Main Author:	Muhammadu, Adamu Adamu
Format:	Thesis
Language:	English
Published:	2016
Subjects:	Heteroscedasticity Robust statistics
Online Access:	http://psasir.upm.edu.my/id/eprint/69761/1/IPM%202016%204%20-%20IR.pdf
Tags:	Add Tag No Tags, Be the first to tag this record!

id	my-upm-ir.69761
record_format	uketd_dc
spelling	my-upm-ir.697612019-10-29T06:41:47Z Identification of suitable explanatory variable in goldfeld-quandt test and robust inference under heteroscedasticity and high leverage points 2016-05 Muhammadu, Adamu Adamu Violation of the assumption of homogeneity of variance of the errors in the linear regression model, causes heteroscedasticity. In the presence of heteroscedastic errors, the ordinary least squares (OLS) estimates are unbiased and consistent, but their covariance matrix estimator is biased and not consistent. As a consequence, this problem negatively affects inference made with biased standard errors. As such, before making any inferences from the OLS, it is very important to check whether or not heteroscedasticity is present. It is now evident that Goldfeld-Quandt (GQ) test is a very powerful test of heteroscedasticity among its competitors. The GQ test requires ordering observations of one explanatory variable in increasing order such that arrangement of observations from the other explanatory variables and the dependent variable in the model follows. When the model involves more than one explanatory variables, identifying suitable variable to be used in the ordering becomes problem when there is no prior knowledge of which variable causes the heteroscedasticity problem. This study has developed an algorithm of identifying this variable prior to conducting the Goldfeld-Quandt test in multiple linear regression model. To overcome the heteroscedasticity problem, many adjustment methods have been proposed in the literature to correct the biased covariance matrix estimator . These heteroscedasticity correcting estimators are known as heteroscedasticity-consistent covariance matrix estimators (HCCME) which include among others HC0, HC1, HC2, HC3, HC4, HC4m and HC5. However, HC4 and HC5 were designed to take into account, the combined problems of heteroscedasticity and high leverage points in a data. For the same purpose, Furno (1996) used a weighted least squares approach and Lima et al. (2009) extended the idea to HC4 and HC5. This study has modified the weighted HCCME used by Furno and Lima et al. to come out with two new weighted HCCME that perform well in quasi t inference, in the presence of heteroscedasticity and high leverage points in small to moderate sample size. Heteroscedasticity Robust statistics 2016-05 Thesis http://psasir.upm.edu.my/id/eprint/69761/ http://psasir.upm.edu.my/id/eprint/69761/1/IPM%202016%204%20-%20IR.pdf text en public masters Universiti Putra Malaysia Heteroscedasticity Robust statistics
institution	Universiti Putra Malaysia
collection	PSAS Institutional Repository
language	English
topic	Heteroscedasticity Robust statistics
spellingShingle	Heteroscedasticity Robust statistics Muhammadu, Adamu Adamu Identification of suitable explanatory variable in goldfeld-quandt test and robust inference under heteroscedasticity and high leverage points
description	Violation of the assumption of homogeneity of variance of the errors in the linear regression model, causes heteroscedasticity. In the presence of heteroscedastic errors, the ordinary least squares (OLS) estimates are unbiased and consistent, but their covariance matrix estimator is biased and not consistent. As a consequence, this problem negatively affects inference made with biased standard errors. As such, before making any inferences from the OLS, it is very important to check whether or not heteroscedasticity is present. It is now evident that Goldfeld-Quandt (GQ) test is a very powerful test of heteroscedasticity among its competitors. The GQ test requires ordering observations of one explanatory variable in increasing order such that arrangement of observations from the other explanatory variables and the dependent variable in the model follows. When the model involves more than one explanatory variables, identifying suitable variable to be used in the ordering becomes problem when there is no prior knowledge of which variable causes the heteroscedasticity problem. This study has developed an algorithm of identifying this variable prior to conducting the Goldfeld-Quandt test in multiple linear regression model. To overcome the heteroscedasticity problem, many adjustment methods have been proposed in the literature to correct the biased covariance matrix estimator . These heteroscedasticity correcting estimators are known as heteroscedasticity-consistent covariance matrix estimators (HCCME) which include among others HC0, HC1, HC2, HC3, HC4, HC4m and HC5. However, HC4 and HC5 were designed to take into account, the combined problems of heteroscedasticity and high leverage points in a data. For the same purpose, Furno (1996) used a weighted least squares approach and Lima et al. (2009) extended the idea to HC4 and HC5. This study has modified the weighted HCCME used by Furno and Lima et al. to come out with two new weighted HCCME that perform well in quasi t inference, in the presence of heteroscedasticity and high leverage points in small to moderate sample size.
format	Thesis
qualification_level	Master's degree
author	Muhammadu, Adamu Adamu
author_facet	Muhammadu, Adamu Adamu
author_sort	Muhammadu, Adamu Adamu
title	Identification of suitable explanatory variable in goldfeld-quandt test and robust inference under heteroscedasticity and high leverage points
title_short	Identification of suitable explanatory variable in goldfeld-quandt test and robust inference under heteroscedasticity and high leverage points
title_full	Identification of suitable explanatory variable in goldfeld-quandt test and robust inference under heteroscedasticity and high leverage points
title_fullStr	Identification of suitable explanatory variable in goldfeld-quandt test and robust inference under heteroscedasticity and high leverage points
title_full_unstemmed	Identification of suitable explanatory variable in goldfeld-quandt test and robust inference under heteroscedasticity and high leverage points
title_sort	identification of suitable explanatory variable in goldfeld-quandt test and robust inference under heteroscedasticity and high leverage points
granting_institution	Universiti Putra Malaysia
publishDate	2016
url	http://psasir.upm.edu.my/id/eprint/69761/1/IPM%202016%204%20-%20IR.pdf
_version_	1747812724340948992

Identification of suitable explanatory variable in goldfeld-quandt test and robust inference under heteroscedasticity and high leverage points

Similar Items