Robust estimation and detection of outliers in simultaneous regression model

The Two Stage Least Squares (2SLS) method is the commonly used method to estimate the parameters of the Simultaneous Equation Regression Model (SEM). This method employs the Ordinary Least Squares (OLS) method twice. Firstly, the endogenous X variable is estimated by the OLS and secondly the paramet...

Full description

Saved in:

Bibliographic Details
Main Author:	Mahdi, Orooba Mohsin
Format:	Thesis
Language:	English
Published:	2016
Subjects:	Regression analysis Mathematical statistics
Online Access:	http://psasir.upm.edu.my/id/eprint/69302/1/FS%202016%2084%20IR.pdf
Tags:	Add Tag No Tags, Be the first to tag this record!

id	my-upm-ir.69302
record_format	uketd_dc
spelling	my-upm-ir.693022020-01-30T01:55:08Z Robust estimation and detection of outliers in simultaneous regression model 2016-12 Mahdi, Orooba Mohsin The Two Stage Least Squares (2SLS) method is the commonly used method to estimate the parameters of the Simultaneous Equation Regression Model (SEM). This method employs the Ordinary Least Squares (OLS) method twice. Firstly, the endogenous X variable is estimated by the OLS and secondly the parameters of the SEM are again estimated using the OLS. It is now evident that the OLS method is easily affected by outliers. Consequently the 2SLS estimates are less efficient in the presence of outliers. Hence robust estimation methods such as the 2SMM, 2SGM6, 2SMMGM6 and 2SGM6MM are formulated to remedy this problem. These methods employ two robust methods in the first and in the second stages. The findings signify that the 2SGM6MM provides the most efficient results compared to other methods. Since the distributions of the proposed methods are intractable, robust bootstraps methods are developed to estimate the standard errors of the estimates. The findings indicate that the 2SGM6MM bootstraps standard errors of the estimates are the smallest compared to other estimates. The identification of high leverage points (HLPs) is very crucial because it is responsible for the drastic change in the parameter estimates of various regression models. Nonetheless, thus far no research has been done to detect HLPs in SEM. Hence, the Diagnostic Robust Generalized Potential (DRGP), Generalized Potential (GP) and Hat Matrix are incorporated with OLS, MM and the GM6 estimator in the development of diagnostic measures for the identification of HLPs in SEM. The results of the study show that the DRGPSEM based on the GM6 estimator is the most successful method in the detection of HLPs compared to other methods in this study. Regression analysis Mathematical statistics 2016-12 Thesis http://psasir.upm.edu.my/id/eprint/69302/ http://psasir.upm.edu.my/id/eprint/69302/1/FS%202016%2084%20IR.pdf text en public masters Universiti Putra Malaysia Regression analysis Mathematical statistics
institution	Universiti Putra Malaysia
collection	PSAS Institutional Repository
language	English
topic	Regression analysis Mathematical statistics
spellingShingle	Regression analysis Mathematical statistics Mahdi, Orooba Mohsin Robust estimation and detection of outliers in simultaneous regression model
description	The Two Stage Least Squares (2SLS) method is the commonly used method to estimate the parameters of the Simultaneous Equation Regression Model (SEM). This method employs the Ordinary Least Squares (OLS) method twice. Firstly, the endogenous X variable is estimated by the OLS and secondly the parameters of the SEM are again estimated using the OLS. It is now evident that the OLS method is easily affected by outliers. Consequently the 2SLS estimates are less efficient in the presence of outliers. Hence robust estimation methods such as the 2SMM, 2SGM6, 2SMMGM6 and 2SGM6MM are formulated to remedy this problem. These methods employ two robust methods in the first and in the second stages. The findings signify that the 2SGM6MM provides the most efficient results compared to other methods. Since the distributions of the proposed methods are intractable, robust bootstraps methods are developed to estimate the standard errors of the estimates. The findings indicate that the 2SGM6MM bootstraps standard errors of the estimates are the smallest compared to other estimates. The identification of high leverage points (HLPs) is very crucial because it is responsible for the drastic change in the parameter estimates of various regression models. Nonetheless, thus far no research has been done to detect HLPs in SEM. Hence, the Diagnostic Robust Generalized Potential (DRGP), Generalized Potential (GP) and Hat Matrix are incorporated with OLS, MM and the GM6 estimator in the development of diagnostic measures for the identification of HLPs in SEM. The results of the study show that the DRGPSEM based on the GM6 estimator is the most successful method in the detection of HLPs compared to other methods in this study.
format	Thesis
qualification_level	Master's degree
author	Mahdi, Orooba Mohsin
author_facet	Mahdi, Orooba Mohsin
author_sort	Mahdi, Orooba Mohsin
title	Robust estimation and detection of outliers in simultaneous regression model
title_short	Robust estimation and detection of outliers in simultaneous regression model
title_full	Robust estimation and detection of outliers in simultaneous regression model
title_fullStr	Robust estimation and detection of outliers in simultaneous regression model
title_full_unstemmed	Robust estimation and detection of outliers in simultaneous regression model
title_sort	robust estimation and detection of outliers in simultaneous regression model
granting_institution	Universiti Putra Malaysia
publishDate	2016
url	http://psasir.upm.edu.my/id/eprint/69302/1/FS%202016%2084%20IR.pdf
_version_	1747812681791832064

Robust estimation and detection of outliers in simultaneous regression model

Similar Items