Endogeneity bias and two-stage least squares: a simulation study

Author

Xujun Wang

Abstract

"In disciplines such as economics, social science, and epidemiology, estimating regression models using ordinary least squares can lead to biased estimates due to one or more of the independent variables being correlated with the error term. When such correlation is not ruled out, the covariate under suspicion is called endogeneous, and the ordinary least squares estimate of the covariate effect is typically biased and yields inconsistent estimates of the causal parameter...This thesis is a simulation study of the bias due to endogeneity, and how much of this bias is removed is determined by resorting to the 2SLS technique. The simulation study was carried out to investigate the bias under several different model assumptions, and the results are compared"--Abstract, page iii.

Advisor(s)

Samaranayake, V. A.

Committee Member(s)

Le, Vy Khoi
Bryant, Richard

Department(s)

Mathematics and Statistics

Degree Name

M.S. in Mathematics and Statistics

Publisher

University of Missouri--Rolla

Publication Date

Spring 2006

Pagination

viii, 55 pages

Rights

© 2006 Xujun Wang, All rights reserved.

Document Type

Thesis - Citation

File Type

text

Language

English

Subject Headings

Equations, Simultaneous -- Numerical solutions
Instrumental variables (Statistics)
Least squares

Thesis Number

T 8955

Print OCLC #

82371529

Link to Catalog Record

Full-text not available: Request this publication directly from Missouri S&T Library or contact your local library.

Recommended Citation

Wang, Xujun, "Endogeneity bias and two-stage least squares: a simulation study" (2006). Masters Theses. 3859.
https://scholarsmine.mst.edu/masters_theses/3859