Advances in Decision Sciences
Volume 2012 (2012), Article ID 728980, 25 pages
Research Article

Solving the Omitted Variables Problem of Regression Analysis Using the Relative Vertical Position of Observations

1Hull College of Business Administration, Augusta State University, 2500 Walton Way, Augusta, GA 30904, USA
2Faculty of Economics, Seikei University, 3-3-1 Kichijoji-kitamachi, Musashino-shi, Tokyo 180-8633, Japan

Received 9 April 2012; Accepted 11 October 2012

Academic Editor: David Bulger

Copyright © 2012 Jonathan E. Leightner and Tomoo Inoue. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


The omitted variables problem is one of regression analysis’ most serious problems. The standard approach to the omitted variables problem is to find instruments, or proxies, for the omitted variables, but this approach makes strong assumptions that are rarely met in practice. This paper introduces best projection reiterative truncated projected least squares (BP-RTPLS), the third generation of a technique that solves the omitted variables problem without using proxies or instruments. This paper presents a theoretical argument that BP-RTPLS produces unbiased reduced form estimates when there are omitted variables. This paper also provides simulation evidence that shows OLS produces between 250% and 2450% more errors than BP-RTPLS when there are omitted variables and when measurement and round-off error is 1 percent or less. In an example, the government spending multiplier, , is estimated using annual data for the USA between 1929 and 2010.