Abstract
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, ∂ GDP / ∂ G, is estimated using annual data for the USA between 1929 and 2010.
| Original language | English (US) |
|---|---|
| Article number | 728980 |
| Journal | Advances in Decision Sciences |
| Volume | 2012 |
| DOIs | |
| State | Published - Dec 1 2012 |
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ASJC Scopus subject areas
- Decision Sciences(all)
- Statistics and Probability
- Computational Mathematics
- Applied Mathematics
Cite this
Solving the omitted variables problem of regression analysis using the relative vertical position of observations. / Leightner, Jonathan E.; Inoue, Tomoo.
In: Advances in Decision Sciences, Vol. 2012, 728980, 01.12.2012.Research output: Contribution to journal › Article
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TY - JOUR
T1 - Solving the omitted variables problem of regression analysis using the relative vertical position of observations
AU - Leightner, Jonathan E.
AU - Inoue, Tomoo
PY - 2012/12/1
Y1 - 2012/12/1
N2 - 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, ∂ GDP / ∂ G, is estimated using annual data for the USA between 1929 and 2010.
AB - 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, ∂ GDP / ∂ G, is estimated using annual data for the USA between 1929 and 2010.
UR - http://www.scopus.com/inward/record.url?scp=84872816748&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84872816748&partnerID=8YFLogxK
U2 - 10.1155/2012/728980
DO - 10.1155/2012/728980
M3 - Article
AN - SCOPUS:84872816748
VL - 2012
JO - Advances in Decision Sciences
JF - Advances in Decision Sciences
SN - 2090-3359
M1 - 728980
ER -