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

Y1 - 2012

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.

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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 -