Solving the omitted variables problem of regression analysis using the relative vertical position of observations
type: Item
instance of: scholarly article
title: Solving the omitted variables problem of regression analysis using the relative vertical position of observations
zbMATH Open document ID: 1257.62079
DOI: 10.1155/2012/728980
author: Jonathan E. Leightner
author: Tomoo Inoue
published in: Advances in Decision Sciences
publication date: 2013-01-10T00:00:00Z
review text: Summary: 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 the 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 that 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, \(\partial\)GDP/\(\partial\)G, is estimated using annual data for the USA between 1929 and 2010.
Mathematics Subject Classification ID: 62J05
Mathematics Subject Classification ID: 62J99
Mathematics Subject Classification ID: 65C60
zbMATH DE Number: 6123903
Wikidata QID: Q58697843
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2012/728980
OpenAlex ID: W1991536631
cites work: Tackling the omitted variables problem without the strong assumptions of proxies
cites work: Q3617836
cites work: Capturing climate's effect on pollution abatement with an improved solution to the omitted variables problem
cites work: Investigating omitted variable bias in regression parameter estimation: a genetic algorithm approach
cites work: Correcting for omitted-variable and measurement-error bias in autoregressive model estimation with panel data
description: scientific article; zbMATH DE number 6123903