This paper is published in Volume-4, Issue-4, 2018
Area
Econometrics
Author
B. Niranjana Rao
Co-authors
Dr. R. V. S. S. Nagabhushana Rao, Dr. O. Hari Babu
Org/Univ
North East Frontier Technical University, Aalo, Arunachal Pradesh, India
Pub. Date
02 July, 2018
Paper ID
V4I4-1153
Publisher
Keywords
Linear Regression Model, Seemingly Unrelated Regression Equation Model, Ordinary Least Squares, Generalized Least Squares, Feasible Generalized Least Squares, Seemingly Unrelated Unrestricted Residual, Seemingly Unrelated Restricted Residual.

Citationsacebook

IEEE
B. Niranjana Rao, Dr. R. V. S. S. Nagabhushana Rao, Dr. O. Hari Babu. Introduction to sets of linear regression models – A brief review, International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.

APA
B. Niranjana Rao, Dr. R. V. S. S. Nagabhushana Rao, Dr. O. Hari Babu (2018). Introduction to sets of linear regression models – A brief review. International Journal of Advance Research, Ideas and Innovations in Technology, 4(4) www.IJARIIT.com.

MLA
B. Niranjana Rao, Dr. R. V. S. S. Nagabhushana Rao, Dr. O. Hari Babu. "Introduction to sets of linear regression models – A brief review." International Journal of Advance Research, Ideas and Innovations in Technology 4.4 (2018). www.IJARIIT.com.

Abstract

Seemingly Unrelated Regression Equations (SURE) model is the generalization of the linear regression model. Zellner (1962) proposed the SURE model and its various associated estimators, test statistics and generalizations have generated a substantial body of literature on sets of linear regression models. Here we specify the SURE models with the assumptions and also explains different estimation methods such as Ordinary Least Squares(OLS), Generalized Least Squares (GLS), Zellner’s Feasible Generalized Least Squares (FGLS), Seemingly Unrelated Unrestricted Residual (SUUR) and Seemingly Unrelated Restricted Residual (SURR) have been explained with their properties.
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